ALBERT

Description: A smart grid (SG) is an advanced power grid system deployed in a cloud center and smart meters (at the consumer end) that provides higher reliability, better data protection, improved power efficiency, automatic monitoring, and effective management of power consumption. However, an SG also poses certain challenges that need to be addressed. For example, data provided by a smart meter are time-sensitive and cannot handle high latency in an SG. Moreover, a smart meter depends on memory, energy, and other factors. Besides, the security between a cloud center and a smart meter is a critical issue that needs to be resolved. Edge computing, an extension of cloud computing deployed in an edge network between a cloud center and the end devices, is an efficient solution to the aforementioned issues. Therefore, in this study, we propose a secure mutual authentication protocol based on edge computing for use in an SG.

Description: To improve the transmission efficiency and facilitate the realization of the scheme, an adaptive modulation (AM) scheme based on the steady-state mean square error (SMSE) of blind equalization is proposed. In this scheme, the blind equalization is adopted and no training sequence is required. The adaptive modulation is implemented based on the SMSE of blind equalization. The channel state information doesn’t need to be assumed to know. To better realize the adjustment of modulation mode, the polynomial fitting is used to revise the estimated SNR based on the SMSE. In addition, we also adopted the adjustable tap-length blind equalization detector to obtain the SMSE, which can adaptively adjust the tap-length according to the specific underwater channel profile, and thus achieve better SMSE performance. Simulation results validate the feasibility of the proposed approaches. Simulation results also show the advantages of the proposed scheme against existing counterparts.

Description: We consider the design of distributed detection algorithms for single-hop, single-channel wireless sensor networks in which sensor nodes send their local decisions to a fusion center (FC) by using a random access protocol. There is also limited time to collect local decisions before a final decision must be made. We thus propose and analyze a modified random access protocol in which the FC combines slotted ALOHA with a population-splitting algorithm called population-splitting-based random access (PSRA) and collision-aware distributed detection according to an estimate-then-fuse approach. Under the PSRA, only sensor nodes whose observations fall in a particular range of reliability will send their decisions in a specific frame by using slotted ALOHA. At the end of the collection time, the FC applies the collision-aware distributed detection to make a final decision. Here, the FC will first observe the state of each time slot—idle, successful, collision—in each frame, use this information to estimate the number of sensor nodes participating in each frame, and, then, compute a final decision using a population-based fusion rule. An approximation of the optimal transmission probability of the slotted ALOHA is determined to minimize the probability of error. Numerical results show that, unlike slotted-ALOHA-based data networks, the transmission probability maximizing the number of successful time slots does not optimize the performance of the proposed distributed detection. Instead, the proposed distributed detection performs best with a transmission probability that induces many collisions.

Description: Multimedia content streaming from Internet-based sources emerges as one of the most demanded services by wireless users. In order to alleviate excessive traffic due to multimedia content transmission, many architectures (e.g., small cells, femtocells, etc.) have been proposed to offload such traffic to the nearest (or strongest) access point also called “helper”. However, the deployment of more helpers is not necessarily beneficial due to their potential of increasing interference. In this work, we evaluate a wireless system which can serve both cacheable and non-cacheable traffic. More specifically, we consider a general system in which a wireless user with limited cache storage requests cacheable content from a data center that can be directly accessed through a base station. The user can be assisted by a pair of wireless helpers that exchange non-cacheable content as well. Files not available from the helpers are transmitted by the base station. We analyze the system throughput and the delay experienced by the cached user and show how these performance metrics are affected by the packet arrival rate at the source helper, the availability of caching helpers, the caches’ parameters, and the user’s request rate by means of numerical results.

Description: As a multi hop self-organizing network, wireless sensor network has the ability to cooperatively sense, collect and process the information of the sensed objects. The applications of WCN in 5G-based Internet of Vehicles (5G-IoV), using information fusion and intelligent information processing technologies, can obtain more reliable and accurate detection parameters, which has been widely concerned. However, the massive connectivity and information exchange in 5G-IoV pose great challenges to the bandwidth efficiency. In order to overcome these issues in 5G-IoV networks, a performance enhanced scheme based on non-orthogonal multiple access (NOMA) is proposed. In the proposed scheme, different vehicle locations are respectively discussed, i.e., whether in the overlap region of cluster head vehicles (CHVs). In particular, different to conventional works, each receiving node only decodes the desired signal to avoid performance loss provided from the poor channel quality limitation. On the other hand, all CHVs decode-and-forward new superposition coded signals with new power allocation factors, while that the maximum ratio combining is utilized at receivers to further improve the ergodic sum-rate (SR) and probability of conflict. The closed-form expressions of ergodic SR for our proposed scheme are analyzed under the independent Rayleigh fading channels. Numerical results corroborating our theoretical analysis show that the superposition coded signal transmission scheme applied to the proposed NOMA-IoV improves the ergodic SR performance significantly compared with the existing works, especially for the high signal-to-noise region.

Description: In order to guarantee a robust transmission of JPWL (JPEG Wireless: Joint Photographic Experts Group Wireless) images through time and frequency selective wireless channels, an efficient adaptive communication strategy is proposed. It is based on an optimization of a closed-loop adaptive multiple-input multiple-output, orthogonal frequency division multiplexing (MIMO-OFDM) scheme associated with a shaping BICM (bit-interleaved coded modulation) technique composed of a duo binary turbo code (DBTC), high-order modulations such as 64–256 QAM (Quadrature Amplitude Modulation) and a shaping code. According to the CSI (channel state information) knowledge at the transmitter side, an algorithm based on unequal error protection (UEP) and unequal power allocation (UPA) is used to select the transmitter key parameters (source/channel encoder rate, modulation order, power, number of quality layers and number of iterations of the Turbo decoder) to achieve the target Quality of Service (QoS). The proposed DBTC-shaping BICM scheme reaches a shaping gain of 1.2 dB for a 256 QAM modulation over a SISO Gaussian channel, whereas only 0.7 dB of shaping gain can be achieved in a scheme that uses the LDPC shaping BICM scheme for the same modulation order. Based on a DBTC shaping BICM scheme and an adaptive algorithm, the proposed MIMO-OFDM strategy achieves better performance compared to a strategy using an iterative process between an RS (Reed-Solomon) and arithmetic decoders. As a result, and on the one hand, a gain of 5.38 dB can be achieved in terms of PSNR (peak signal-to-noise ratio). On the other hand, a gain of 78% in terms of power consumption is obtained for the same QoS level. Moreover, the adaptive number of iterations in the proposed strategy can minimize the computational complexity of the turbo decoding compared to a scheme using four iterations whatever the channel conditions.

Description: In order to improve the connection rate and transmission efficiency of field network for power distribution grid, a dual-mode heterogeneous field network with high-speed power line broadband carrier and micro-power radio frequency wireless communication capabilities was designed. First, the topological structure of the field network, the networking process of the central node and the free nodes and the dynamic maintenance mechanism of the network were discussed in detail. Secondly, the routing measurement mechanism for creating a hybrid routing table and the improved layer limit shortest path routing algorithm were presented. On this basis, each node in the network could choose the optimal communication media at any given moment to create communication links with the adaptive data transfer speed according to the real-time hybrid routing table. Finally, the dual-mode heterogeneous field network was applied to the electricity consumption information collection system and tested in the laboratory and jobsite. The test results show that the dual-mode field network was more effective than the single-mode field network in shortening the reading meter time and increasing networking success rate.

Description: With the advent of the Internet of things era, power equipment is gradually connected to the network, and its intelligent fault detection function provides greater help for the power industry. The purpose of this study is to design the power equipment fault information acquisition system of the Internet of things. This research is based on the equipment fault information collection system of the Internet of things and mainly studies the fault information collection method based on the Internet of things technology. Equipment fault data are generally time series data. In the analysis of equipment failure, the data before and after fault and before and after fault are analyzed. The abnormal state of equipment is associated with the data before and after the fault. Therefore, by analyzing the characteristics of the fault data and the equipment before and after the fault, a bidirectional recurrent neural network model based on LSTM is constructed. The method designed in this paper can not only improve the efficiency and speed of collection, but also can compare and collect fault information. The overall operation state of the power unit is improved accurately. The research results show that the company's low-voltage user acquisition success rate has reached more than 99%. With the increase of time, the fault information collection efficiency can approach 99%. It shows that the function of this research system is better, the economic loss of the company is reduced, and the management is optimized.

Description: This paper proposes a low complexity channel estimation algorithm for unmanned aerial vehicle three dimension multi-user multiple-input-multiple-output (3D MIMO) systems with the uniform planar array (UPA) at base station using paired spatial signatures. With the aid of antenna array theory and array signal processing, 3D channel is firstly modeled based on the angles between the direction of arrival along x- and y-axis of the UPA. And 3D MIMO channels can be projected onto the x- and y-directions, respectively. Then, channel estimation for multi-user uplinks using small amount of training resources is divided into two phases. At the first uplink preamble phase, each user is assigned the orthogonal pilot, and the paired spatial signatures and optimal rotation angle of each user through the same pilot sequence are obtained. We also propose a user grouping strategy based on three-dimension angle-division multiple access (3D-ADMA) to ensure that the user's spatial signatures do not overlap. At the second phase during several coherence times, the same pilot sequence within a group and orthogonal pilot sequences between groups are assigned, then, the channel state information of the user's x- and y-directions are recovered by the paired space signatures and optimal rotation angle of each user obtained in the preamble phase, respectively. And dynamically updating the user's paired spatial signatures and optimal rotation angle utilizes the obtained channel parameter of x- and y-directions. Finally, the channel parameter of the x- and y-directions are reconstructed by the updated user's space signatures and the optimal rotation angle, and the 3D MIMO channel estimation is obtained through the Kronecker product. Compared with the conventional channel estimation method of the 3D MIMO system under UPA using a low-rank model, the proposed methods reduce the computational complexity without degrading the estimated performance to a large extent. Furthermore, it is carried out with limited training resources, and the pilot resource overhead of the system is greatly reduced by the 3D-ADMA packet and the two-stage pilot allocation. Simulation results verified that the proposed algorithm is effective and feasible.

Description: Internet of things (IoT) is a concept that is currently gaining a lot of popularity as a result of its potential to be incorporated into many heterogeneous systems. Because of its diversity, integrating IoT is conceivable in almost all fields, including the healthcare sector. For instance, a promising technology in the healthcare sector known as wireless body area network (WBAN) could be integrated with the IoT to enhance its productivity. However, in order to guarantee the optimization of the operation of the healthcare applications facilitated by the WBAN-enabled IoT technology, there must be enough support from all the different protocol stack layers so as to satisfy the critical quality-of-service (QoS) requirements of the WBAN systems. Consequently, the medium access control (MAC) protocol has recently been gaining lots of attention in the area of WBANs due to its ability to manage and coordinate when a shared communication channel can be accessed. For the purpose of achieving efficient MAC protocols for WBAN-enabled IoT technology, this paper investigates some key MAC protocols that could be exploited in WBANs based on their characteristics, service specifications, technical issues such as energy wastage issues, and possible technical solutions were provided to enhance energy efficiency, channel utilization, data transmission rate, and dealy rate. Also, these MAC protocols were grouped and compared based on short- and long-range communication standards. Following this, future directions and open research issues are pointed out.

Description: In this paper, we investigate the secrecy performance of short-packet transmissions in ultra-reliable and low-latency communications (URLLC). We consider the scenario where a multi-antenna source communicates with a single-antenna legitimate receiver requiring ultra-high reliability and low latency, in the presence of a single-antenna eavesdropper. In order to safeguard URLLC, the source transmits the artificial noise (AN) signal together with the confidential signal to confuse the eavesdropper. We adopt a lower bound on the maximal secrecy rate as the secrecy performance metric for short-packet transmissions in URLLC, which takes the target decoding error probabilities at the legitimate receiver and the eavesdropper into account. Using this metric, we first derive a compact expression of the generalized secrecy outage probability (SOP). Then, we formally prove that the generalized SOP is a convex function with respect to the power allocation factor between the confidential signal and the AN signal. We further determine the optimal power allocation factor that minimizes the generalized SOP. The results presented in this work can be useful for designing new secure transmission schemes for URLLC.

Description: Cognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.

Description: The Hemispherical Resonator Gyroscope (HRG) has many advantages such as high precision, high reliability and long life-time, it is widely used in the space-launcher and the satellites. The HRG has been mechanized to operate in the distinct operating modes, the Force to Rebalanced (FTR) mode and the Whole Angle (WA) mode. In the paper, different from the traditional control algorithm is based on average methods which usually used for the WA mode, a new electrostatic control algorithm is presented, which based on the vector composition and decomposition method to control the equivalent drive force in order to track the phase of the standing wave. The mathematics model and the control algorithm are presented in the paper, and the hardware experimental circuit system is implemented, the HRG has a range of more than 300°/s with the linearity of 40 ppm. Meanwhile, the energy transition efficiency increases by an order of magnitude.

Description: This paper proposes a matrix operation method for modeling the three-phase transformer by phase-coordinates. Based on decoupling theory, the 12 × 12 dimension primitive admittance matrix is obtained at first employing the coupling configuration of the windings. Under the condition of asymmetric magnetic circuits, according to the boundary conditions for transformer connections, the transformers in different connections enable to be modeling by the matrix operation method from the primitive admittance matrix. Another purpose of this paper is to explain the differences of the phase-coordinates and the positive sequence parameters in the impedances of the transformers. The numerical testing results in IEEE-4 system show that the proposed method is valid and efficient.

Description: Precipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmosphere after condensation, which can be divided into stratiform cloud and convective cloud. Different precipitation clouds often accompany different precipitation processes. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. However, all of them have a common shortcoming that the radial velocity data detected by Doppler Weather Radar has not been applied to the identification of precipitation clouds because it is insensitive to the convective movement in the vertical direction. This paper proposes a new method for precipitation clouds identification based on deep learning algorithm, which is according the distribution morphology of multiple radar data. It mainly includes three parts, which are Constant Altitude Plan Position Indicator data (CAPPI) interpolation for radar reflectivity, Radial projection of the ground horizontal wind field by using radial velocity data, and the precipitation clouds identification based on Faster-RCNN. The testing result shows that the method proposed in this paper performs better than the traditional methods in terms of precision. Moreover, this method boasts great advantages in running time and adaptive ability.

Description: With the development of the wireless network, increasing mobile applications are emerging and receiving great popularity. These applications cover a wide area, such as traffic monitoring, smart homes, real-time vision processing, objective tracking, and so on, and typically require computation-intensive resources to achieve a high quality of experience. Although the performance of mobile devices (MDs) has been continuously enhanced, running all the applications on a single MD still causes high energy consumption and latency. Fortunately, mobile edge computing (MEC) allows MDs to offload their computation-intensive tasks to proximal eNodeBs (eNBs) to augment computational capabilities. However, the current task offloading schemes mainly concentrate on average-based performance metrics, failing to meet the deadline constraint of the tasks. Based on the deep reinforcement learning (DRL) approach, this paper proposes an Energy-aware Task Offloading with Deadline constraint (DRL-E2D) algorithm for a multi-eNB MEC environment, which is to maximize the reward under the deadline constraint of the tasks. In terms of the actor-critic framework, we integrate the action representation into DRL-E2D to handle the large discrete action space problem, i.e., using the low-complexity k-nearest neighbor as an approximate approach to extract optimal discrete actions from the continuous action space. The extensive experimental results show that DRL-E2D achieves better performance than the comparison algorithms on all parameter settings, indicating that DRL-E2D is robust to the state changes in the MEC environment.

Description: As one of the basic supporting technologies of 5G system, wireless sensor networks technology is facing a new challenge to improve its transmission energy efficiency. This paper considers combining simultaneous wireless information and power transfer (SWIPT) technique and routing technique, and applying them to multi-hop clustered wireless sensor networks (MCWSN), where each node can decode information and harvest energy from a received radio-frequency signal. And the relay nodes in MCWSN can utilize the harvest energy to forward data to their next hop nodes according to the routing scheme. First, we formulate an energy-efficient routing problem of MCWSN with SWIPT. Then, a heuristic energy efficient cooperative SWIPT routing algorithm (EECSR) is presented to find a transmission path with the maximum energy efficiency. Specifically, in EECSR, the resource allocation problem in each hop of the path is transformed to some equivalent convex optimization problems, which are resolved via dual decomposition. Moreover, a distributed routing protocol based on EECSR is proposed. As far as we know, this is the first solution that considers energy efficiency optimization based on routing and SWIPT in MCWSN. Simulation results show that our EECSR algorithm has high energy efficiency and good robustness. And our distributed routing protocol has better real-time performance than traditional protocols.

Description: In time division duplexing based mobile networks, under certain atmospheric ducting conditions, the uplink reception may be interfered by the downlink transmissions of remote base-stations (gNBs) located hundreds of kilometers away. This paper addresses such remote interference problem in a 5G new radio (NR) macro deployment context. Specifically, two potential reference signal (RS) designs for remote interference management (RIM) are described. The first signal structure, denoted as the one OFDM symbol (1OS) based RIM-RS, is building on the channel state information reference signals of 5G NR. The second candidate is referred to as the two OFDM symbol based RIM-RS design, which builds on the design principles of LTE RIM-RS. The achievable detection performance is evaluated by introducing enhanced receiver algorithms together with three feasible propagation delay based gNB grouping and corresponding RIM-RS transmissions schemes. The performance results in terms of the receiver processing gain highlight that the improved detection algorithm assures sufficient performance to detect the remote interference for both RIM-RSs with all evaluated frequency domain comb-like patterns. The benefit of grouping corresponding RIM-RS transmissions from gNBs located on the same area is greater when using same frequency domain resources per transmitted sequence in practical interference scenarios. Furthermore, applying a common base sequence for all gNBs within a group allows to identify the group based on detected sequence and enables adaptive RIM mitigation schemes. On the other hand, it is shown that the 1OS RIM-RS provides smaller overhead and can be frequency multiplexed with the physical downlink shared channel, which opens up the possibility of using gNB group wise 1OS RIM-RS also for UE interference measurements.

Description: Ad hoc network is a special network with centerless and dynamic topology. Due to the free mobility of the nodes, routing security has been a bottleneck problem that plagues its development. Therefore, a multi-path QoS (quality of service) routing security algorithm based on blockchain by improving the traditional AODV (ad hoc on-demand distance vector) protocol (AODV-MQS) is proposed. Firstly, a chain of nodes is established in the network and the states of all nodes by making the intermediate nodes on the chain are saved. Secondly, the smart contract in the blockchain is set to filter out the nodes that meet the QoS constraints. Finally, two largest unrelated communication paths are found in the blockchain network through smart contract, one of which is the main path and the other is the standby path. Simulation experiments show that the performance of the proposed algorithm is better than other algorithms, especially in an unsafe environment.

Description: In the era of the 5G network, network traffic grows rapidly. Edge computing will face the challenges of high bandwidth, low latency, high reliability and other requirements of 5G network services. Due to the limited resources of node communication, computing and storage, under the sudden, intensive and high-traffic task request, edge computing will suffer from network jitter, excessive delay, access congestion, service failure and low distribution efficiency. In order to ensure network service quality and improve service efficiency, it is necessary to construct an effective collaborative service mechanism, motivate nodes to participate in cooperation, integrate network service resources and self-adapt to manage collaborative services. Therefore, establishing an effective node and service evaluation system to identify reliable resources and nodes is an effective way to improve the overall availability and reliable services of edge computing network. In this paper, we summarize and analyze the key technologies of the current collaborative service organization incentive and trust mechanism. This paper presents an attack-resistant node and service evaluation system based on a trust network. The system includes a voting collection mechanism, a trustable node selection mechanism, a questioning response mechanism and a punishment mechanism. The experiments prove that the system has a strong ability to resist attacks and is superior to the existing reputation evaluation model in terms of its performance. It can effectively improve the collaborative efficiency of edge computing and guarantee the quality of network service.

Description: It has been widely acknowledged that network slicing is a key architectural technology to accommodate diversified services for the next generation network (5G). By partitioning the underlying network into multiple dedicated logical networks, 5G can support a variety of extreme business service needs. As network slicing is implemented in radio access networks (RAN), user handoff becomes much more complicated than that in traditional mobile networks. As both physical resource constraints of base stations and logical connection constraints of network slices should be considered in handoff decision, an intelligent handoff policy becomes imperative. In this paper, we model the handoff in RAN slicing as a Markov decision process and resort to deep reinforcement learning to pursue long-term performance improvement in terms of user quality of service and network throughput. The effectiveness of our proposed handoff policy is validated via simulation experiments.

Description: With the rapid development of cross-border e-commerce, the improvement of consumer satisfaction has become the focus of cross-border e-commerce platform optimization. Relying on advanced algorithm technology, it can realize the accurate and efficient matching between massive information and users, which is conducive to improving the user experience. Based on the consideration of consumer satisfaction, this paper constructs a dual channel supply chain composed of cross-border suppliers, cross-border e-commerce enterprises, retailers and consumers and studies the revenue and cost sharing contract of the supply chain under the conditions of centralized decision and decentralized decision. The research shows that cross-border e-commerce enterprises can choose to form revenue and expenditure sharing contracts through online and offline channels, optimize decentralized decision-making, and achieve win–win cooperation among supply chain entities.

Description: With the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.

Description: An amendment to this paper has been published and can be accessed via the original article.

Description: In the presence of power disturbance, the test accuracy of power is not good, in order to improve the performance of power testing, it is necessary to carry out the power disturbance detection design. A power disturbance detection algorithm based on wavelet analysis and neural network is proposed. The time domain and frequency domain decomposition are used to decompose the characteristic of the power disturbance signal, and the wavelet analysis method is used to improve the adaptive focusing performance of the power disturbance signal. The one-dimensional power disturbance function is mapped to the two-dimensional function of time scale and time shift by continuous wavelet transform, and the higher-order spectral characteristic quantity of the power disturbance signal is extracted, and the extracted characteristic quantity is automatically classified by neural network. The optimal identification and detection of power disturbance are realized. The simulation results show that the algorithm has higher accuracy and higher recognition ability, which improves the accurate probability of power disturbance detection and the anti-disturbance ability of power test.

Description: High-tech industrial agglomeration is conducive to boosting technological progress, promoting industrial structure upgrading and realizing economic transformation, and certainly affects the overall industrial environmental efficiency. However, few recent studies have focused on its impact on industrial environmental efficiency from a green perspective. In the context of promoting the development of green economy, it is of great significance to clarify the links between high-tech industrial agglomeration and industrial environmental efficiency. In this research, we first analyzed the theoretical mechanism of the impact of high-tech industrial agglomeration and its spatial spillover effects on industrial environmental efficiency and then made an empirical analysis based on the panel data of 29 provinces and cities in China from 2003 to 2016. During the research, Super-DEA method, ESDA method and spatial Dubin model are used. The result shows that: (1) There is a significant spatial positive correlation between China's industrial environmental efficiency and high-tech industrial agglomeration; (2) high-tech industrial agglomeration has improved the local industrial productivity and industrial technology level through scale effects and technical effects, which has accordingly significantly enhanced the corresponding environmental efficiency; (3) through the association of regional industries, the cross-regional cooperation of enterprises and the formation of innovation networks, high-tech industrial agglomeration promotes the spillover of knowledge and technology among regions, improves the level of industrial technology in neighboring regions, and enhances the industrial environmental efficiency in neighboring regions. All these three is helpful to re-evaluate the development mode of high-tech industry agglomeration and to formulate relevant government policies.

Description: Edge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst-case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer nonlinear programming problem, a decomposition into subproblems is performed and a two-stage heuristic algorithm is proposed. The simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queueing and end-to-end delay.

Description: The body area network is now the most challenging and most popular network for study and research. Communication about the body has undoubtedly taken its place due to a wide variety of applications in industry, health care, and everyday life in wireless network technologies. The body area network requires such smart antennas that can provide the best benefits and reduce interference with the same channel. The discovery of this type of antenna design is at the initiative of this research. In this work, to get a good variety, the emphasis is on examining different techniques. The ultra-wide band is designed, simulated, and manufactured because the ultra-wide band offers better performance compared to narrowband antennas. To analyze the specific absorption rate, we designed a multilayer model of human head and hand in the high-frequency structure simulator. In the final stage, we simulated our antennas designed with the head and hand model to calculate the results of the specific absorption rate. The analysis of the specific absorption rate for the head and hand was calculated by placing the antennas on the designed model.

Description: Countless data generated in Smart city may contain private and sensitive information and should be protected from unauthorized users. The data can be encrypted by Attribute-based encryption (CP-ABE), which allows encrypter to specify access policies in the ciphertext. But, traditional CP-ABE schemes are limited because of two shortages: the access policy is public i.e., privacy exposed; the decryption time is linear with the complexity of policy, i.e., huge computational overheads. In this work, we introduce a novel method to protect the privacy of CP-ABE scheme by keyword search (KS) techniques. In detail, we define a new security model called chosen sensitive policy security: two access policies embedded in the ciphertext, one is public and the other is sensitive and hidden. If user's attributes don't satisfy the public policy, he/she cannot get any information (attribute name and its values) of the hidden one. Previous CP-ABE schemes with hidden policy only work on the “AND-gate” access structure or their ciphertext size or decryption time maybe super-polynomial. Our scheme is more expressive and compact. Since, IoT devices spread all over the smart city, so the computational overhead of encryption and decryption can be shifted to third parties. Therefore, our scheme is more applicable to resource-constrained users. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.

Description: The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

Description: 5G mobile network technology together with edge computing will create new opportunities for developing novel road safety services in order to better support connected and automated driving in challenging situations. This paper studies the feasibility and benefits of localized mobile network edge applications for supporting vehicles in diverse conditions. We study a particular scenario, where vehicle sensor data processing, required by road safety services, is installed into the mobile network edge in order to extend the electronic horizon of the sensors carried by other vehicles. Specifically, we focus on a LiDAR data-based obstacle warning case where vehicles receive obstacle warnings from the mobile network edge. The proposed solution is based on a generic system architecture. In this paper, we first evaluate different connectivity and computing options associated with such a system using ns-3 simulations. Then, we introduce a proof-of-concept implementation of the LiDAR-based obstacle warning scenario together with first results from an experimental evaluation, conducted both in a real vehicle testbed environment and in a laboratory setting. As a result, we obtain first insights on the feasibility of the overall solution and further enhancements needed.

Description: In order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

Description: Clock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively.

Description: The addition of redundancy is a promising solution to achieve a certain quality of service (QoS) for ultra-reliable low-latency communications (URLLC) in challenging fast fading scenarios. However, adding more and more redundancy to the transmission results in severely increased radio resource consumption. Monitoring and prediction of fast fading channels can serve as the foundation of advanced scheduling. By choosing suitable resources for transmission, the resource consumption is reduced while maintaining the QoS. In this article, we present outage prediction approaches for Rayleigh and Rician fading channels. Appropriate performance metrics are introduced to show the suitability for URLLC radio resource scheduling. Outage prediction in the Rayleigh fading case can be achieved by adding a threshold comparison to state-of-the-art fading prediction approaches. A line-of-sight (LOS) component estimator is introduced that enables outage prediction in LOS scenarios. Extensive simulations have shown that under realistic conditions, effective outage probabilities of $$10^{-5}$$ 10 - 5 can be achieved while reaching up-state prediction probabilities of more than $${90}{\%}$$ 90 % . We show that the predictor can be tuned to satisfy the desired trade-off between prediction reliability and utilizability of the link. This enables our predictor to be used in future scheduling strategies, which achieve the challenging QoS of URLLC with fewer required redundancy.

Description: Cooperative, connected and automated mobility (CCAM) across Europe requires harmonized solutions to support cross-border seamless operation. The possibility of providing CCAM services across European countries has an enormous innovative business potential. However, the seamless provision of connectivity and the uninterrupted delivery of real-time services pose technical challenges which 5G technologies aim to solve. The situation is particularly challenging given the multi-country, multi-operator, multi-telco-vendor, multi-car-manufacturer and cross-network-generation scenario of any cross-border scenario. Motivated by this, the 5GCroCo project, with a total budget of 17 million Euro and partially funded by the European Commission, aims at validating 5G technologies in the Metz-Merzig-Luxembourg cross-border 5G corridor considering the borders between France, Germany and Luxembourg. The activities of 5GCroCo are organized around three use cases: (1) Tele-operated Driving, (2) high-definition map generation and distribution for automated vehicles and (3) Anticipated Cooperative Collision Avoidance (ACCA). The results of the project help contribute to a true European transnational CCAM. This paper describes the overall objectives of the project, motivated by the discussed challenges of cross-border operation, the use cases along with their requirements, the technical 5G features that will be validated and provides a description of the planned trials within 5GCroCo together with some initial results.

Description: Device-free passive (DfP) intrusion detection system is a system that can detect moving entities without attaching any device to the entities. To achieve good performance, the existing algorithms require proper access point (AP) deployment. It limits the applying scenario of those algorithms. We propose an intrusion detection system based on deep learning (IDSDL) with finer-grained channel state information (CSI) to free the AP position. A CSI phase propagation components decomposition algorithm is applied to obtain blurred components of CSI phase on several paths as a more sensitive detection signal. Convolutional neuron network (CNN) of deep learning is used to enable the computer to learn and detect intrusion without extracting numerical features. We prototype IDSDL to verify its performance and the experimental results indicate that IDSDL is effective and reliable.

Description: Local 5G networks are emerging as a new form for 5G deployment, targeting service delivery for vertical-specific purposes and other local users. These networks are also known as micro-operator networks for which prior work has established different deployment scenarios, namely Closed, Open and Mixed Networks. To achieve network flexibility, customization and privacy required by various vertical sectors, such as industry, health and energy, it is essential to have a well-defined network slicing architecture and adequate implementation procedure. In this paper, a sophisticated end-to-end network slicing architecture is proposed for different deployment scenarios of the local 5G micro-operator concept. The proposed architecture incorporates a broad four-layer concept, leveraging a multi-tenancy layer for different tenants and their end users, a descriptive service layer, a multi-domain slicing management and orchestration layer, and a resource layer. We further propose a network slice instance (NSI) communication service distribution technique for local 5G micro-operators. This is achieved by expanding/leveraging the communication service management function in the multi-tenant layer into a multi-tenant manager and an orchestrator of communication services. In addition, we describe how the communication service orchestrator will address all the possible multitenant-slice situations during the distribution of a network slice instance to multiple tenants. The novel methods described in the paper present a solution for not only network slice communication service distribution across different micro-operator’s tenants but also for future use cases, especially, when the allocated slice is responsible for multiple tenants or when a tenant requests multiple NSIs.

Description: This paper investigates a relay assisted simultaneous wireless information and power transfer (SWIPT) for downlink in cellular systems. Cooperative non-orthogonal multiple access (C-NOMA) is employed along with power splitting protocol to enable both energy harvesting (EH) and information processing (IP). A downlink model consists of a base station (BS) and two users is considered, in which the near user (NU) is selected as a relay to forward the received signal from the BS to the far user (FU). Maximum ratio combining is then employed at the FU to combine both the signals received from the BS and NU. Closed form expressions of outage propability, throughput, ergodic rate and energy efficiency (EE) are firstly derived for the SWIPT based C-NOMA considering both scenarios of with and without direct link between the BS and FU. The impacts of EH time, EH efficiency, power-splitting ratio, source data rate and distance between different nodes on the performance are then investigated. The simulation results show that the C-NOMA with direct link achieves an outperformed performance over C-NOMA without direct link. Moreover, the performance of C-NOMA with direct link is also higher than that for OMA. Specifically, (1) the outage probability for C-NOMA in both direct and relaying link cases is always lower than that for OMA. (2) the outage probability, throughput and ergodic rate vary according to $$ eta$$ β , (3) the EE of both users can obtain in SNR range of from $$-10$$ - 10 to 5 dB and it decreases linearly as SNR increases. Numerical results are provided to verify the findings.

Description: Information security has become a hot topic in Internet of Things (IoT), and traditional centralized access control models are faced with threats such as single point failure, internal attack, and central leak. In this paper, we propose a model to improve the access control security of the IoT, which is based on zero-knowledge proof and smart contract technology in the blockchain. Firstly, we deploy attribute information of access control in the blockchain, which relieves the pressure and credibility problem brought by the third-party information concentration. Secondly, encrypted access control token is used to gain the access permission of the resources, which makes the user's identity invisible and effectively avoids attribute ownership exposure problem. Besides, the use of smart contracts solves the problem of low computing efficiency of IoT devices and the waste of blockchain computing power resources. Finally, a prototype of IoT access control system based on blockchain and zero-knowledge proof technology is implemented. The test analysis results show that the model achieves effective attribute privacy protection, compared with the Attribute-Based Access Control model of the same security level, the access efficiency increases linearly with the increase of access scale.

Description: For the global telecom operators, mobile data services have gradually taken the part of traditional voice services to become the main revenue growth point. However, during the upgrading period of new generation networks (such as 5G), new mobile data services are still at the stage of exploration; the network capabilities and the application scenarios are unmatured. In this phase, it is incomplete and misleading to simply measure the performance of new services from one dimension, such as data traffic or revenue, and the measurement should be dynamically changed according to the development of the new services. Therefore, telecom operators want to improve the existing performance measurement from the aspect of integrity and dynamics. In this paper, we propose mobile-data-service development index (MDDI) and build a quantitative model to dynamic measure the overall performance of mobile data services. To approach a fuller understanding, we creatively bring investment indicators and networks reliability indicators into performance indicators system and discuss the relationships among subindices and the selection of outcome criteria in MDDI. In the part of empirical research, we use the model to analyze the dynamic characteristics of a new mobile data service in China and summarize the development strategies of every stage. The findings can also give guidelines for new services of 5G and other new generation networks in the future.

Description: With the rise of 5G and Internet of things, especially the key technology of 5G, network slice cuts a physical network into multiple virtual end-to-end networks, each of them can obtain logically independent network resources to support richer services. 5G mobile data and sensor data converge to form a growing network traffic. Traffic explosion evolved into a mixed network type, and network viruses, worms, network theft and malicious attacks are also involved. How to distinguish traffic types, block malicious traffic and make effective use of sensor data under the background of 5G network slice, and also the significance of this study.

Description: This paper studies the communication problem between UAVs and cellular base stations in a 5G IoT scenario where multiple UAVs work together. We are dedicated to the uplink channel modeling and the performance analysis of the uplink transmission. In the channel model, we consider the impact of 3D distance and multi-UAVs reflection on wireless signal propagation. The 3D distance is used to calculate the path loss, which can better reflect the actual path loss. The power control factor is used to adjust the UAV's uplink transmit power to compensate for different propagation path losses, so as to achieve precise power control. This paper proposes a binary exponential power control algorithm suitable for 5G networked UAV transmitters and presents the entire power control process including the open-loop phase and the closed-loop phase. The effects of power control factors on coverage probability, spectrum efficiency and energy efficiency under different 3D distances are simulated and analyzed. The results show that the optimal power control factor can be found from the point of view of energy efficiency.

Description: We consider a real continuous-time bandlimited additive white Gaussian noise channel with 1-bit output quantization. On such a channel the information is carried by the temporal distances of the zero-crossings of the transmit signal. We derive an approximate lower bound on the capacity by lower-bounding the mutual information rate for input signals with exponentially distributed zero-crossing distances, sine-shaped transition waveform, and an average power constraint. The focus is on the behavior in the mid-to-high signal-to-noise ratio (SNR) regime above 10 dB. For hard bandlimited channels, the lower bound on the mutual information rate saturates with the SNR growing to infinity. For a given SNR the loss with respect to the unquantized additive white Gaussian noise channel solely depends on the ratio of channel bandwidth and the rate parameter of the exponential distribution. We complement those findings with an approximate upper bound on the mutual information rate for the specific signaling scheme. We show that both bounds are close in the SNR domain of approximately 10–20 dB.

Description: In this article, a novel two-way double-relay selection strategy with its bit error rate (BER) performance analysis is proposed. In this novel strategy, as a first step we choose two relays out of a set of relay-nodes in a way to maximize the system performance in terms of BER and complexity. In the second step, the selected relays apply orthogonal space-time coding scheme using the three-phase protocol to establish a two-way communication between the transceivers, which will lead to a significant improvement in the achievable diversity and coding gain with a very low decoding complexity by using a symbol-wise decoder. Furthermore, the performance of the overall system is further enhanced through the use of a network coding method at the selected relay-nodes. Moreover, this paper proposes the analytical approximation of the BER performance. As well, we show that the analytical results match perfectly the simulated ones. In addition, we prove that our strategy outperforms the current state-of-the-art ones by proposing a better cooperative communication system in terms of BER.

Description: Sparse-aware (SA) detectors have attracted a lot attention due to its significant performance and low-complexity, in particular for large-scale multiple-input multiple-output (MIMO) systems. Similar to the conventional multiuser detectors, the nonlinear or compressive sensing based SA detectors provide the better performance but are not appropriate for the overdetermined multiuser MIMO systems in sense of power and time consumption. The linear SA detector provides a more elegant tradeoff between performance and complexity compared to the nonlinear ones. However, the major limitation of the linear SA detector is that, as the zero-forcing or minimum mean square error detector, it was derived by relaxing the finite-alphabet constraints, and therefore its performance is still sub-optimal. In this paper, we propose a novel SA detector, named single-dimensional search-based SA (SDSB-SA) detector, for overdetermined uplink MIMO systems. The proposed SDSB-SA detector adheres to the finite-alphabet constraints so that it outperforms the conventional linear SA detector, in particular, in high SNR regime. Meanwhile, the proposed detector follows a single-dimensional search manner, so it has a very low computational complexity which is feasible for light-ware Internet of Thing devices for ultra-reliable low-latency communication. Numerical results show that the the proposed SDSB-SA detector provides a relatively better tradeoff between the performance and complexity compared with several existing detectors.

Description: Online task assignment is one of the core research issues of spatio-temporal crowdsourcing technology. The current researches on minimizing travel cost all focus on the scenario of two objectives (task requesters and workers). This paper proposes a two-stage framework (GH) based on Greedy algorithm and Hungarian algorithm for three-objective online task assignment to minimize travel cost. In order to further optimize the efficiency and average travel cost, this paper proposes GH-AT (Adaptive Threshold) algorithm based on GH algorithm, and redesigns the Hungarian algorithm into the sHungarian algorithm. sHungarian algorithm has lower time complexity than Hungarian algorithm. sHungarian algorithm is not only suitable for the problem studied in this paper, but also for all task assignment problems with constraints. Compared with Greedy algorithm, GH-AT algorithm has lower travel cost and higher total utility. In terms of the number of matches, GH-AT is slightly lower than Greedy algorithm. In terms of time cost, GH-AT algorithm is higher than Greedy algorithm, but much lower than GH algorithm.

Description: The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there exists an explicit channel model for training that matches the actual channel model in the online transmission. The variation of the actual channel indeed imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. Specifically, we partition channel coefficient values into sub-intervals, train an AE for each partition in the offline phase, and constitute a bank of AEs. Then, based on the actual channel condition in the online phase and the average block error rate (BLER), the optimal pair of encoder and decoder is selected for data transmission. To gain knowledge about the actual channel conditions, we assume a realistic scenario in which the instantaneous channel is not known, and propose to blindly estimate it at the Rx, i.e., without any pilot symbols. Our simulation results confirm the superiority of the proposed adaptive scheme over existing methods in terms of the average power consumption. For instance, when the target average BLER is equal to $$10^{-4}$$ 10 - 4 , our proposed algorithm with 5 pairs of AE can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme.

Description: Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multi-hop communication and a shorter transmission range. Clustering and routing are treated separately in different solutions and, therefore, efficient solutions in terms of energy consumption and network lifetime could not be provided. This work focuses data collection from IoT-nodes distributed in an area and connected through WSN. We address two interlinked issues, clustering and routing, for large-scale IoT-based WSN and propose an improved clustering and routing protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. During process of clustering, cluster-heads are selected in such a way that provide fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop-count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity.

Description: Clustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on the manual identification of the elbow points on the visualization curve. Thus, experienced analysts cannot clearly identify the elbow point from the plotted curve when the plotted curve is fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to yield a statistical metric that estimates an optimal cluster number when clustering on a dataset. First, the average degree of distortion obtained by the Elbow method is normalized to the range of 0 to 10. Second, the normalized results are used to calculate the cosine of intersection angles between elbow points. Third, this calculated cosine of intersection angles and the arccosine theorem are used to compute the intersection angles between elbow points. Finally, the index of the above-computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a well-known public dataset (Iris Dataset) demonstrated that the estimated optimal cluster number obtained by our newly proposed method is better than the widely used Silhouette method.

Description: Running exercise can increase the basal metabolic rate and increase the time of aerobic exercise. Based on the current needs of the general public for running auxiliary training, this paper combines wireless sensing and blockchain technology in the design scheme, and designs and implements a running training auxiliary technology. First, it obtains the user's gait information and other related parameters in the process through the wireless sensor network, and optimize the calculation gait in different states through the noise processing algorithm. Then, we use the blockchain technology to design a data transmission and storage plan for the protection and analysis of the user's personal privacy data. The proposed method builds a new type of sports training assistance system for the masses of modern society and contributes to the masses' physical exercise.

Description: Deinterleaving or radar pulse separation is a very important goal in terms of radar sources for identifying and implementing electronic warfare systems. In order to separate radar pulses, parameters measured by electronic warfare receivers such as electronic warfare support measures (ESM) and electronic signals intelligence (ELINT) are used in pulse separation. This paper presents a multi-parameter improved method for separating the pulse sequence of radar signals based on time of arrival (TOA) processing with sorting the other pulse descriptor words (PDW) parameters. In the proposed method, after extracting all the pulse repetition intervals (PRIs) based on TOA, the parameters of the angle of arrival, pulse width and carrier frequency (RF) are being used in pulse sorting to separate the received interleaved pulse sequences. The sequential difference histogram (SDIF) algorithm or cumulative difference histogram (CDIF) algorithm is used to extract all pulse repetition intervals. Also, in order to separate the sequence of the received pulses from all surroundings emitters, in addition to matching the potential PRI among the TOAs of the pulses and the similarity measurement in the other parameters of the pulse sequence (pulse sorting) have been used. This proposed algorithm is implemented in the integrated and complete design for deinterleaving of the radar pulses. The proposed method by considering low-cost computing sources considers a fast and low-complexity solution that can be used for edge-enabled distributed processors in aerial radar platforms as edge devices for military/combat unmanned aerial vehicles or networked missiles. The simulation results show that our method is completely effective.

Description: This paper documents a simple parametric polynomial line-of-sight channel model for 100–450 GHz band. The band comprises two popular beyond fifth generation (B5G) frequency bands, namely, the D band (110–170 GHz) and the low-THz band (around 275–325 GHz). The main focus herein is to derive a simple, compact, and accurate molecular absorption loss model for the 100–450 GHz band. The derived model relies on simple absorption line shape functions that are fitted to the actual response given by complex but exact database approach. The model is also reducible for particular sub-bands within the full range of 100–450 GHz, further simplifying the absorption loss estimate. The proposed model is shown to be very accurate by benchmarking it against the exact response and the similar models given by International Telecommunication Union Radio Communication Sector. The loss is shown to be within ±2 dBs from the exact response for one kilometer link in highly humid environment. Therefore, its accuracy is even much better in the case of usually considered shorter range future B5G wireless systems.

Description: In the next generation of mobile communication networks, unprecedented challenges are required to be met, such as much higher data rates and spectrum efficiency, lower latency, and massive connectivity. Non-orthogonal multiple access (NOMA) has recently been proposed as a promising technology to achieve much superior spectral efficiency compared to conventional orthogonal multiple access techniques employed in present communication systems. A salient feature of NOMA is its use of successive interference cancellation (SIC) to decode users’ information when multiple users are allowed to transmit in same time/frequency/code domain. In this paper, we aim to exploit an aspect of SIC, namely the availability of other users’ data to realize a cooperative NOMA system. EXtrinsic information transfer (EXIT) charts are utilized to examine the performance of proposed system in terms of user fairness while employing IRregular convolutional codes (IRCC)s. The EXIT chart using IRCC evaluates the convergence analysis for the proposed system. Further, to evaluate the system performances in cooperative NOMA system, we have derived the expressions for the achievable rates which are obtained independently and utilized them in evaluating the experimental data for the proposed NOMA model.

Description: Over the last few years, energy optimization in wireless sensor networks (WSNs) has drawn the attention of both the research community and actual users. Sensor nodes are powered by attached batteries that are considered as a critical aspect of sensor nodes design. Besides, the constraint of the limited battery capacity is associated with the concern on how to reduce the energy consumption of nodes to extend the network lifetime. In this context, the purpose of this study is to implement an adaptive medium access control (MAC) for energy saving and traffic control enhancement. This program was designed to arrange nodes into two priority groups according to their traffic rate and data transmission packet delay. This fuzzy algorithm depends on their queue length where it is implemented into the carrier sense multiple access with collision avoidance (CSMA/CA) algorithm. However, other types of nodes should send their data during the contention-free period with a GTS reallocation scheme. Those nodes are classified as low priority access to the medium, and their data transmission is scheduled using time division multiple access methods. Moreover, this proposed scheme dynamically adjusts the contention access period length to ensure that nodes can complete their data transmission during the same super-frame. Simulation results are done using the network simulator tool (NS-2), and it has improved efficiency regarding the IEEE-802.15.4 standard.

Description: In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis (DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in hazy and snowy days, which can be stable at about 93%.

Description: As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The quantum generative adversarial network (QGAN) is considered to be one of the quantum machine learning algorithms with great application prospects, which also should be improved to conform to the human-centered paradigm. The generation process of QGAN is relatively random and the generated model does not conform to the human-centered concept, so it is not quite suitable for real scenarios. In order to solve these problems, a hybrid quantum-classical conditional generative adversarial network (QCGAN) algorithm is proposed, which is a knowledge-driven human–computer interaction computing mode that can be implemented in cloud. The purposes of stabilizing the generation process and realizing the interaction between human and computing process are achieved by inputting artificial conditional information in the generator and discriminator. The generator uses the parameterized quantum circuit with an all-to-all connected topology, which facilitates the tuning of network parameters during the training process. The discriminator uses the classical neural network, which effectively avoids the “input bottleneck” of quantum machine learning. Finally, the BAS training set is selected to conduct experiment on the quantum cloud computing platform. The result shows that the QCGAN algorithm can effectively converge to the Nash equilibrium point after training and perform human-centered classification generation tasks.

Description: In this paper, the blind signal separation problem of complex baseband signal is addressed. A widely linear complex autoregressive process of order one is employed to represent the temporal structure of complex sources. We formulate a new contrast function by a convex combination of generalized autocorrelations and the statistics of the innovation. And the proposed contrast function is optimized by gradient method. Simulation results show that the proposed algorithm is better than the comparison algorithm in convergence speed and convergence accuracy.

Description: Stable routing and energy conservation over a wireless sensor network (WSN) is a major issue in Internet of Things applications. The network lifetime can be increased when studying this issue with interest. Data transmission is a dominant factor in IoT networks for communication overhead and energy consumption. A proposed efficient node stable routing ($$ENSR$$ ENSR ) protocol is introduced to guarantee the stability of transmission data between the source and destination nodes, in a dynamic WSN conditions. $$ENSR$$ ENSR minimizes energy consumption and selects more stable nodes for packets forwarding. Stability becomes the most important factor that qualifies the node's centrality. A node’s stability is characterized by residual energy, link quality, and number of hops needed to reach the destination from the node. To calculate node's stability, an enhanced centrality concept, known as stable betweenness centrality ($$SBC$$ SBC ) is introduced. In $$ENSR$$ ENSR , at first, some nodes will be selected as the stable forwarding nodes, usually with maximum $$SBC$$ SBC between their neighbors within a limited communication radio range of a particular region. Furthermore, each stable forwarding node then broadcasts its identity, including $$SBC$$ SBC , to the source node separately. The source node can compute a stable path to forward packets to the corresponding stable forwarding node, based on a proper designed stable path routing metric ($$SPRM$$ SPRM ). Then, the stable forwarding node will behave as a new source node and start another stable path routing process until the packets are forwarded and reached to the destination node. In addition, the change of stable nodes over time balances and conserves node energy consumption, thereby mitigating “hot spots”. The proposed routing protocol is validated through simulation. The numerical results show that the proposed protocol outperforms the existing algorithms, global and local reliability-based routing ($$GLRR$$ GLRR ) and reliable energy-aware routing protocol $$(RER)$$ ( R E R ) , in terms of network efficiency and reliability.

Description: Nonlinear behavior and power efficiency of the Power Amplifier (PA) contradictorily depend on the input signal amplitude distribution. The transmitted signal in multi-carrier modulation exhibits high Peak-to-Average Power Ratio (PAPR) and large bandwidths, leading to the degradation of the radio link and additional generation out-of-band interferences, which degrade the quality of the transmission. Practical solutions exist, like a power back-off, but with unacceptable efficiency performances of the transmitter. This paper deals with efficiency and linearity improvement using a new PAPR reduction method based on the combination of Discrete Cosine Transform and shaping technique. The main principle is to determine an optimal coding scheme according to a trade-off between coding complexity and performance benefits in the presence of PA nonlinearities. Simulation and experimental results in the context of OFDM signal and using a 20 W–3.7 GHz Radio-Frequency Power Amplifier show an improvement on PAPR reduction of about 3.25 dB. Also, the communication criteria like Bit Error Rate and Error Vector Magnitude are improved by about one decade and a half and $$8\%$$ 8 % , respectively.

Description: This article presents an experimental demonstration of a high-capacity millimeter-wave 5G NR signal transmission with analog radio-over-fiber (ARoF) fronthaul over multi-core fiber and full real-time processing. The demonstration validates the core of the blueSPACE fronthaul architecture which combines ARoF fronthaul with space division multiplexing in the optical distribution network to alleviate the fronthaul capacity bottleneck and maintain a centralized radio access network with fully centralized signal processing. The introduction of optical beamforming in the blueSPACE architecture brings true multi-beam transmission and enables full spatial control over the RF signal. The proposed ARoF architecture features a transmitter that generates the ARoF signal and an optical signal carrying a reference local oscillator employed for downconversion at the remote unit from a single RF reference at the central office. A space division multiplexing based radio access network with multi-core fibre allows parallel transport of the uplink ARoF signal and reference local oscillator at the same wavelength over separate cores. A complete description of the real-time signal processing and experimental setup is provided and system performance is evaluated. Transmission of an 800 MHz wide extended 5G NR fronthaul signal over a 7-core fibre is shown with full real-time signal processing, achieving 1.4 Gbit/s with a bit error rate $$

Description: The sixth generation (6G) mobile systems will create new markets, services, and industries making possible a plethora of new opportunities and solutions. Commercially successful rollouts will involve scaling enabling technologies, such as cloud radio access networks, virtualization, and artificial intelligence. This paper addresses the principal technologies in the transition towards next generation mobile networks. The convergence of 6G key-performance indicators along with evaluation methodologies and use cases are also addressed. Free-space optics, Terahertz systems, photonic integrated circuits, softwarization, massive multiple-input multiple-output signaling, and multi-core fibers, are among the technologies identified and discussed. Finally, some of these technologies are showcased in an experimental demonstration of a mobile fronthaul system based on millimeter 5G NR OFDM signaling compliant with 3GPP Rel. 15. The signals are generated by a bespoke 5G baseband unit and transmitted through both a 10 km prototype multi-core fiber and 4 m wireless V-band link using a pair of directional 60 GHz antennas with 10° beamwidth. Results shown that the 5G and beyond fronthaul system can successfully transmit signals with both wide bandwidth (up to 800 MHz) and fully centralized signal processing. As a result, this system can support large capacity and accommodate several simultaneous users as a key candidate for next generation mobile networks. Thus, these technologies will be needed for fully integrated, heterogeneous solutions to benefit from hardware commoditization and softwarization. They will ensure the ultimate user experience, while also anticipating the quality-of-service demands that future applications and services will put on 6G networks.

Description: Aiming at the problem of strong impact, short response period and wide resonance frequency bandwidth of transient vibration signals, a transient feature extraction method based on adaptive tunable Q-factor wavelet transform (TQWT) was proposed. Firstly, the characteristic frequency band of the vibration signal was selected according to the time–frequency distribution. Based on the characteristic frequency band, the sub-band average energy weighted wavelet Shannon entropy was used to optimize the number of decomposition layers, quality factor and redundancy of TQWT, so as to achieve the adaptive optimal matching of the impact characteristic components in the vibration signal. Then, according to the characteristics of the transient impact of the telemetry vibration signal, the TQWT decomposition coefficients were sparse reconstructed to obtain more sparse impact characteristics, and the weighted power spectrum kurtosis was used as the impact characteristic index to select the optimal sub-band, Finally, the inverse transform of TQWT was used to reconstruct the optimal sub-band to enhance its weak impact features. The simulation and measured signal processing results verify the effectiveness of the algorithm.

Description: At present, unmanned aerial vehicles (UAVs) have been widely used in communication systems, and the fifth-generation wireless system (5G) has further promoted the vigorous development of them. The trajectory planning of UAV is an important factor that affects the timeliness and completion of missions, especially in scenarios such as emergency communications and post-disaster rescue. In this paper, we consider an emergency communication network where a UAV aims to achieve complete coverage of potential underlaying device-to-device (D2D) users. Trajectory planning issues are grouped into clustering and supplementary phases for optimization. Aiming at trajectory length and sum throughput, two trajectory planning algorithms based on K-means are proposed, respectively. In addition, in order to balance sum throughput with trajectory length, we present a joint evaluation index. Then relying on this index, a third trajectory optimization algorithm is further proposed. Simulation results show the validity of the proposed algorithms which have advantages over the well-known benchmark scheme in terms of trajectory length and sum throughput.

Description: Increased number of the vehicles on the streets around the world has led to several problems including traffic congestion, emissions, and huge fuel consumption in many regions. With advances in wireless and traffic technologies, the Intelligent Transportation System (ITS) has been introduced as a viable solution for solving these problems by implementing more efficient use of the current infrastructures. In this paper, the possibility of using cellular-based Low-Power Wide-Area Network (LPWAN) communications, LTE-M and NB-IoT, for ITS applications has been investigated. LTE-M and NB-IoT are designed to provide long range, low power and low cost communication infrastructures and can be a promising option which has the potential to be employed immediately in real systems. In this paper, we have proposed an architecture to employ the LPWAN as a backhaul infrastructure for ITS and to understand the feasibility of the proposed model, two applications with low and high delay requirements have been examined: road traffic monitoring and emergency vehicle management. Then, the performance of using LTE-M and NB-IoT for providing backhaul communication infrastructure has been evaluated in a realistic simulation environment and compared for these two scenarios in terms of end-to-end latency per user. Simulation of Urban MObility has been used for realistic traffic generation and a Python-based program has been developed for evaluation of the communication system. The simulation results demonstrate the feasibility of using LPWAN for ITS backhaul infrastructure mostly in favor of the LTE-M over NB-IoT.

Description: The emergence of 5G communication systems will not replace existing radio access networks but will gradually merge to form ultra-dense heterogeneous networks. In heterogeneous networks, the design of efficient vertical handoff (VHO) algorithms for 5G infrastructures is necessary to improve quality of service (QoS) and system resource utilization. In this paper, an optimized algorithm based on a multi-objective optimization model is proposed to solve the lack of a comprehensive consideration of user and network impacts during the handoff process in existing VHO algorithms. The Markov chain model of each base station (BS) is built to calculate a more accurate value of the network state that reflects the network performance. Then, a multi-objective optimization model is derived to maximize the value of the network state and the user data receiving rate. The multi-objective genetic algorithm NSGA-II is finally employed to turn the model into a final VHO strategy. The results of the simulation for the throughput and blocking rate of networks demonstrate that our algorithm significantly increases the system throughput and reduces the blocking rate compared to the existing VHO strategies.

Description: In wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

Description: With the acceleration of informatization and the coverage of wireless networks, homes, conferences, schools and other places have a higher pursuit of the wireless transmission capabilities of electronic devices. Wireless screen transmission technology is used more frequently in life, work and study. This article mainly discusses the practical application of network multimedia courseware in college basketball teaching. This article first elaborates the teaching plan of multimedia courseware, including teaching content, teacher guidance, student learning and multimedia courseware. Secondly, the multimedia courseware of basketball tactics basic teaching is completed by using Flash mx2004 plug-in. After that, it specifically introduces the process of how to transmit basketball teaching content through multimedia equipment to the video network for students to learn under the wireless network environment. It emphasizes that the “wireless multimedia communication” course is an important course in the electronic information subject. Finally, through the teaching experiment, the accuracy of the multimedia teaching method was tested, and the validity of the courseware content was tested by the empirical validity evaluation method. At the same time, after the teaching experiment, in order to test the two groups of students’ mastery of the basic coordination theory of basketball tactics, the basic coordination theory of basketball tactics was tested. The experimental group had 22 students with a score of 90 or more, accounting for 27.5%, and the control group had 13 students with a score of 90 or more, accounting for 16.5%. The results show that wireless network multimedia computer-assisted teaching has a positive effect on improving students’ interest in learning.

Description: Blockchain technology has completely changed the area of cryptocurrency with a Peer-to-Peer system named Bitcoin. It can provide a distributed, transparent and highly confidential database by recording immutable transactions. Currently, the technique has obtained great research interest on other areas, including the Internet of vehicles (IoVs). In order to solve some centralized problems and improve the architecture of the IoVs, the blockchain technology is utilized to build a decentralized and secure vehicular environment. In this survey, we aim to construct a comprehensive analysis on the applications of blockchain in the IoV. This paper starts with the introduction of the IoVs and the blockchain. Additionally, some existing surveys on the blockchain enabled IoVs are reviewed. Besides, the combination of the blockchain technology and the IoVs is analyzed from seven aspects to describe how the blockchain is implemented in the IoVs. Finally, the future research directions related to the integration are highlighted.

Description: In order to solve the problem between low power of Internet of Things devices and the high cost of cryptography, lightweight cryptography is required. The improvement of the scalar multiplication can effectively reduce the complexity of elliptic curve cryptography (ECC). In this paper, we propose a fast formula for point septupling on elliptic curves over binary fields using division polynomial and multiplexing of intermediate values to accelerate the computation by more than 14%. We also propose a scalar multiplication algorithm based on the step multi-base representation using point halving and the septuple formula we proposed, which significantly reduces the computational cost. The experimental results show that our method is more efficient over binary fields and contributes to reducing the complexity of ECC.

Description: The fifth generation (5G) of mobile networks is designed to accommodate different types of use cases, each of them with different and stringent requirements and key performance indicators (KPIs). To support the optimization of the network performance and validation of the KPIs, there exist the necessity of a flexible and efficient monitoring system and capable of realizing multi-site and multi-stakeholder scenarios. Nevertheless, for the evolution from 5G to 6G, the network is envisioned as a user-driven, distributed Cloud computing system where the resource pool is foreseen to integrate the participating users. In this paper, we present a distributed monitoring architecture for Beyond 5G multi-site platforms, where different stakeholders share the resource pool in a distributed environment. Taking advantage of the usage of publish-subscribe mechanisms adapted to the Edge, the developed lightweight monitoring solution can manage large amounts of real-time traffic generated by the applications located in the resource pool. We assess the performance of the implemented paradigm, revealing some interesting insights about the platform, such as the effect caused by the throughput of monitoring data in performance parameters such as the latency and packet loss, or the presence of a saturation effect due to software limitations that impacts in the performance of the system under specific conditions. In the end, the performance evaluation process has confirmed that the monitoring platform suits the requirements of the proposed scenarios, being capable of handling similar workloads in real 5G and Beyond 5G scenarios, then discussing how the architecture could be mapped to these real scenarios.

Description: In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL.

Description: Multi-kernel polar codes have recently received considerable attention since they can provide more flexible code lengths than do the original ones. The construction process of them can be simplified by obtaining the Bhattacharyya parameter bounds of the kernels employed. However, there has been currently no generic method for seeking such bounds. In this paper, therefore, we focus on the upper Bhattacharyya parameter bounds of the standard binary polar code kernels with an arbitrary dimension of $$lge 2$$ l ≥ 2 . A calculation process composing of four steps, the common column binary tree construction for the channel inputs, the common factor extraction, the calculation feasibility testing, and the upper bound calculation based on pattern matching, is formulated with a computational complexity of $$O(2^l)$$ O ( 2 l ) . It is theoretically proved that the upper bounds obtained by the proposed method are tight, which can lay the foundation to compare the reliability of the synthesized channels in polar codes.

Description: The lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

Description: Considering the issue with respect to the high data redundancy and high cost of information collection in wireless sensor nodes, this paper proposes a data fusion method based on belief structure to reduce attribution in multi-granulation rough set. By introducing belief structure, attribute reduction is carried out for multi-granulation rough sets. From the view of granular computing, this paper studies the evidential characteristics of incomplete multi-granulation ordered information systems. On this basis, the positive region reduction, belief reduction and plausibility reduction are put forward in incomplete multi-granulation ordered information system and analyze the consistency in the same level and transitivity in different levels. The positive region reduction and belief reduction are equivalent, and the positive region reduction and belief reduction are unnecessary and sufficient conditional plausibility reduction in the same level, if the cover structure order of different levels are the same the corresponding equivalent positive region reduction. The algorithm proposed in this paper not only performs three reductions, but also reduces the time complexity largely. The above study fuses the node data which reduces the amount of data that needs to be transmitted and effectively improves the information processing efficiency.

Description: The advantages of e-commerce and information technology play an extremely important role in enhancing the competitiveness of the tourism industry and adapting to the needs of global economic integration. The development of e-commerce has played a huge role in all walks of life. For the tourism industry, the role of e-commerce is even more important. This article analyzes the influence of e-commerce on tourism production factors, such as optimizing tourism production factors, optimizing industrial structure, improving the competitiveness of tourism enterprises and playing the leading role of the government. This article attempts to find out the fundamental reason why e-commerce can enhance the competitiveness of China’s tourism industry, so as to find a better way for e-commerce to promote the development of China’s tourism industry. In order to accurately predict the scale and quantity of domestic tourism, an optimized neural network model is proposed to analyze and predict tourism data, and then analyze and research the data. Tourism development factors such as tourism development factors, changes in tourism demand and the optimization of industrial structure have effectively promoted the development of China’s tourism industry.

Description: The aim of this work is to study the impact of small receiver displacement on a signal propagation in a typical conference room environment at a millimeter wave frequency of 60 GHz. While channel measurements provide insights on the propagation phenomena, their use for the wireless system performance evaluation is challenging. Whereas, carefully executed three-dimensional ray tracing (RT) simulations represent a more flexible option. Nevertheless, a careful validation of simulation methodology is needed. The first target of this article is to highlight the benefits of an in-house built three-dimensional RT tool at 60 GHz and shows the effectiveness of simulations in predicting different characteristics of the channel. To validate the simulation results against the measurements, two different transmitter (Tx) positions and antenna types along with ten receiver (Rx) positions are considered in a typical conference room. In first system configuration, an omnidirectional antenna is placed in the middle of the table, while in the second system configuration a directed horn antenna is located in the corner of the meeting room. After validating the simulation results with the measurement data, in the second part of this work, the impact of a small change, i.e., 20 cm in the receiver position, is studied. To characterize the impact, we apply as performance indicators the received power level, root mean square delay spread (RMS-DS) and RMS angular spread (RMS-AS) in azimuth plane. The channel characteristics are considered with respect to the direct orientation (DO), i.e., the Rx antenna is directed toward the strongest incoming path. Different antenna configurations at the Tx and Rx side are applied to highlight the role of antenna properties on the considered channel characteristics. Especially, in the second system configuration the impact of different antenna half power beamwidth on different considered channel characteristics is highlighted through acquired simulation results. The validation of results shows the RMS error of only 2–3 dB between the measured and simulated received power levels for different Tx configurations in the direction of DO. Results indicate that only a small change of the Rx position may result a large difference in the received power level even in the presence of line-of-sight between the Tx and Rx. It is found that the STD of received power level across the room increases with the decrease in HPBW of the antenna. As can be expected, directed antennas offer lower value of RMS-DS and RMS-AS compared with isotropic antenna.

Description: The security of wireless routers receives much attention given by the increasing security threats. In the era of Internet of Things, many devices pose security vulnerabilities, and there is a significant need to analyze the current security status of devices. In this paper, we develop WNV-Detector, a universal and scalable framework for detecting wireless network vulnerabilities. Based on semantic analysis and named entities recognition, we design rules for automatic device identification of wireless access points and routers. The rules are automatically generated based on the information extracted from the admin webpages, and can be updated with a semi-automated method. To detect the security status of devices, WNV-Detector aims to extract the critical identity information and retrieve known vulnerabilities. In the evaluation, we collect information through web crawlers and build a comprehensive vulnerability database. We also build a prototype system based on WNV-Detector and evaluate it with routers from various vendors on the market. Our results indicate that the effectiveness of our WNV-Detector, i.e., the success rate of vulnerability detection could achieve 95.5%.

Description: With the massive growth of wireless networks comes a bigger impact of collisions and interference, which has a negative effect on throughput and energy efficiency. To deal with this problem, we propose an in-band wireless collision and interference detection scheme based on full-duplex technology. To study its performance, we compare its throughput and energy efficiency with the performance of traditional half-duplex and symmetric in-band full-duplex transmissions. Our analysis considers a realistic protocol and overhead modeling, and a measurement-based self-interference model. Our results indicate that our proposed collision detection scheme can provide significant gains in terms of throughput and energy efficiency in large wireless networks. Moreover, when compared to half-duplex and symmetric full-duplex, our analysis shows that this scheme allows up to 45% more nodes in the network for the same energy consumption per bit. These results suggest that this could be an enabling technology towards efficient, dense wireless networks.

Description: As the PHY/MAC-layer IR-HARQ and RLC-layer ARQ error recovery procedures, adopted in LTE, may impose additional delay when their code-block retransmissions occur, the arising question is whether these significantly contribute to IP and consequently RTP packet delays, and finally degrade the overall application-layer end-to-end QoE, especially when voice is transmitted over LTE? With this regard, we propose and demonstrate a VoLTE QoS and QoE test procedure based on PHY/MAC/RLC/IP/TCP-UDP/RTP cross-layer protocol analysis and perceptual speech quality QoE measurements. We identified monotonic relationship between the paired observations: QoE and HARQ RTT, i.e. between the PESQ voice quality rating and the IP/RTP packet latency, for given BLER of the received MAC/RLC code-blocks. Specifically, we found out that, for the HARQ RTT value of about 8 ms, only up to 2 HARQ retransmissions (and consequently no RLC-ARQ one) is appropriate during any voice packet, otherwise delay accumulation might not be accordingly “smoothed out” by jitter/playback buffers along the propagation path.

Description: Combining unmanned aerial vehicles (UAVs) with 6G, Internet of Things (IoT) and other emerging communication technologies could better satisfy various IoT applications and create more innovative services. This paper develops a novel hierarchical 6G IoT network with UAVs in the sky and intelligent reflective surface (IRS) equipped. The system employs backscattering communication (BackCom) to transmit data in a free-ride manner. Through beamforming, IRS enhances the energy of the reflectable signal, thereby improving the distance and performance of the BackCom. Simulation results reveal that our approach makes a significant improvement to the performance of the whole system and takes obvious advantage over traditional solutions.

Description: Energy efficiency and privacy preserving have become essential for the wireless sensor networks. In this paper, a joint energy and time resource allocation problem for the cognitive users (CUs) in a non-selfish symbiotic cognitive relaying scheme (NSCRS) is considered. We aim to maximize the total energy efficiency (EE) of the primary user and CUs with the consideration of information privacy under the total energy constraints of CUs. With full channel state information (CSI), an optimal energy and time resource allocation algorithm is proposed based on the exhaustive searching. Besides, in order to reduce the overhead of CSI feedback, a suboptimal algorithm, in which only the partial instantaneous CSI is required, is additionally proposed. Simulation results demonstrate the EE of primary and CUs in the NSCRS with consideration of information privacy can be greatly improved by the proposed algorithms.

Description: The distributed clustering method is widely used to extend network lifetime in traditional wireless sensor networks. However, it is difficult to achieve the idea performance of the whole network, such as transmission rate, energy consumption, and control overhead, neglecting the global stability of the network. To tackle this problem, a centralized mobility-based clustering (CMBC) protocol is proposed in the software defined sensor network. The method of CMBC mainly consists of two aspects: first, CMBC clusters the nodes with the connection time between the mobile nodes (i.e., noncluster head nodes, non-CH) and the cluster head (CH) and establishes stable topological structures between the non-CHs and the CH; second, when emergencies occur, the centralization management control center sends the configuration files to replace the CH. Compared to the distributed network of MBC, WCRA and IMP-MECA protocol, the proposed method can be applied in scenarios with high-speed mobile nodes to improve the network performance in terms of transmission successful rate, average power consumption, and average control overhead.

Description: As a technology integrated with Internet of things, mobile edge computing (MEC) can provide real-time and low-latency services to the underlying network and improve the storage and computation ability of the networks instead of central cloud infrastructure. In mobile edge computing-based Internet of Vehicle (MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for MEC-IoV is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

Description: In this paper, we propose an ambient backscatter communication-based smart 5G IoT network. The network consists of two parts, namely a real-time data transmission system based on ambient backscatter communication and a real-time big data analysis system based on the combination of shallow neural networks and deep neural networks. The real-time data transmission system based on ambient backscatter communication can extend the standby time of data collection equipment, reduce the size of the equipment, and increase the comfort of wearing. The real-time big data analysis system combining the shallow neural network and the deep neural network can greatly reduce the pressure caused by the frequent deep neural network calculations of the MEC and greatly reduce the energy consumed by the MEC for remote real-time monitoring.

Description: Vehicular Ad-hoc Networks (VANETs) can improve the road safety by transmitting safety-critical messages such as beacons and emergency messages. IEEE 802.11p VANETs have adopted the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism for the multiple access control. The 802.11p media access control (MAC) protocol, however, can not guarantee the reliability of broadcasting data, since the reception of transmitted messages are not acknowledged. Moreover, the backoff scheme of the 802.11p MAC utilizes a fixed-size contention window for safety message broadcasting, which causes high collision probabilities especially in dense environments. In order to improve such drawbacks, we propose a hybrid access method as follows: Nodes are equipped to reserve time slots for the next round of broadcasting, while unoccupied time slots are preserved for those which have emergency needs. In addition, implicit feedbacks are enabled for detecting collisions incurred during random channel accesses in preserved time slots. We devise a mathematical model which optimally controls the parameters of our scheme while minimizes the cost caused by idle channels and collisions. Extensive simulations show that our mechanism can remarkably improve the performance of VANETs in broadcasting of the safety messages.

Description: The transmission performance of spatial multiplexing cross-polarized MIMO is studied to determine how to increase the capacity of digital terrestrial broadcasting and make it more robust. In this paper, the performance was evaluated in laboratory experiments and large-scale field experiments in central Tokyo under three deployment scenarios. The results showed that the required received power can be improved by 7.3 dB when MIMO is introduced to enhance transmission robustness, i.e., maintaining the same transmission capacity as SISO. Alternatively, the transmission capacity can be doubled by introducing MIMO for the same robustness as SISO, requiring a slight increase in received power of 0.9 dB compared with SISO. The field experiments were performed by using a prototype transmission system for advanced digital terrestrial TV broadcasting in Japan, but the results obtained are also valid for 5G broadcasting. Regarding the feasibility of the advanced terrestrial broadcasting system operating at a target bit rate of about 60 Mbps to distribute a VVC-based 8 K program within a 6-MHz channel bandwidth, an increased received power of 3.7 dB compared with SISO was required for MIMO to achieve a higher transmission capacity in the worst case for 28 reception points evaluated in urban areas.

Description: The data of coal mine safety field are massive, multi-source and heterogeneous. It is of practical importance to extract information from big data to achieve disaster precaution and emergency response. Existing approaches need to build more features and rely heavily on the linguistic knowledge of researchers, leading to inefficiency, poor portability, and slow update speed. This paper proposes a new relation extraction approach using recurrent neural networks with bidirectional minimal gated unit (MGU) model. This is achieved by adding a back-to-front MGU layer based on original MGU model. It does not require to construct complex text features and can capture the global context information by combining the forward and backward features. Evident from extensive experiments, the proposed approach outperforms the existing initiatives in terms of training time, accuracy, recall and F value.

Description: Medium-length LDPC codes are in demand in certain areas such as mobile environment (Wi-Fi and Mobile WiMAX) and in telecommand links from the ground to space because of their lower latency properties. However, because of the length of these codes is rather short, decoding error rates are worse than those of long-length codes. In this paper, we show that the combination of shortened LDPC codes, whose shortened positions are properly selected, and ordered statistic decoding (OSD) significantly improves the decoding error. For the best choice of shortening positions, we used the integer programming approach. In particular, we utilized Feldman–Wainwright–Karger code polytope for this purpose. Some studies have independently reported the efficiency of shortened LDPC codes and OSD methods. This paper emphasizes that their combination results in multiplicative effectiveness.

Description: Abstract Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective To explore the utility of Internet of Things in power transformer fault diagnosis system. Methods A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

Description: In order to realize the automation of transport equipment, according to the automatic guided vehicle (AGV) kinematics model, the trajectory tracking control of AGV with front wheel steering and rear wheel drive is studied. Lyapunov function is used to ensure the global stability of the system. Time-varying state feedback method based on integral backstepping is adopted, introducing virtual feedback, a trajectory tracking controller for AGV with nonholonomic constraints is proposed. In order to ensure the smooth motion of AGV, the speed and acceleration limited strategy of AGV is introduced into the controller. Simulation results show that the algorithm is fast, accurate and globally stable for different paths.

Description: Network slicing, as a key technique of 5G, provides a way that network operators can segment multiple virtual logic on the same physical network and each customer can order specific slicing which can meet his requirement of 5G service. The service level agreement of network slicing (NS-SLA) of 5G, as a business agreement signed between the network operators and the customers, specifies the relevant requirements for the 5G services provided by the network operators. However, the authenticity of auditing results may not be guaranteed and the customer’s data may be leaked in the existing NS-SLA audit scheme. In this paper, a blockchain-based 5G network slicing NS-SLA audit model is proposed to address the above problems. The blockchain is applied as a public platform and all the dual monitored service parameters will be stored on the blockchain to ensure the authenticity of data. A trapdoor order-revealing encryption algorithm is introduced to audit strategy, which can encrypt the monitored parameters, realize the comparison over ciphertexts and prevent the privacy of data from leaking. Besides, an NS-SLA audit smart contract is designed to implement the audit task and execute corresponding punishment strategies automatically. We make experiments to exam the cost of the blockchain-based system and the results found clear support for the feasibility of the proposed model.

Description: The pervasive cooperation of a group of UAVs has attracted increasing attention due to the reduced cost and widespread availability. When working in an untrusted or adversarial environment, the mutual authentication of UAVs in the cooperative process is imperative. However, there are some major challenges, including changes in the network environment before and during task performing, and the weak connection network state faced by UAVs. Therefore, a novel task-oriented authentication model for UAVs based on blockchain (ToAM) is proposed, which divides UAVs authentication into group building authentication and intra-group authentication with a two-stage authentication framework. And two lightweight authentication protocols are presented, respectively, corresponding to two stages. Finally, analyses demonstrate that our model realizes secure and lightweight authentication function for the whole process of UAVs requisition and task performing.

Description: Machine learning is a branch of the field of artificial intelligence. Deep learning is a complex machine learning algorithm that has unique advantages in image recognition, speech recognition, natural language processing, and industrial process control. Deep learning has It is widely used in the field of wireless communication. Prediction of geological disasters (such as landslides) is currently a difficult problem. Because landslides are difficult to detect in the early stage, this paper proposes a GPS-based wireless communication continuous detection system and applies it to landslide deformation monitoring to achieve early treatment and prevention. This article introduces the GPS multi-antenna detection system based on deep learning wireless communication, and introduces the time series analysis method and its application. The test results show that the GPS multi-antenna detection system of the wireless communication network has great advantages in response time, with high accuracy and small error. The horizontal accuracy is controlled at 0–2 mm and the vertical accuracy is about 1 mm. The analysis method is simple and efficient, and can obtain good results for short-term deformation prediction.

Description: Public key encryption scheme with keyword search is a promising technique supporting search on encrypted data without leaking any information about the keyword. In real applications, it’s critical to find an effective revocation method to revoke users in multi-user cryptosystems, when user’s secret keys are exposed. In this paper, we propose the first designated server-aided revocable identity-based encryption scheme with keyword search (dSR-IBKS) from lattice. The dSR-IBKS model requires each user to keep just one private key corresponding with his identity and does not need to communicate with the key generation center or the server during key updating. We have proved that our scheme can achieve chosen keyword indistinguishability in the standard model. In particular, our scheme can designate a unique tester to test and return the search results, therefore no other entity can guess the keyword embedded in the ciphertext by generating search queries and doing the test by itself. We provide a formal security proof of our scheme assuming the hardness of the learning with errors problem on the standard model.

Description: As a comprehensive utilization of pipeline resources, communication intelligent manhole cover (CIMC) can effectively real-time monitor communication manhole cover and protect the safety of communication pipeline. Due to the complex working environment of manhole cover and the random error of sensor, the traditional monitoring method usual leads to frequent false alarm in actual applications. In order to ensure the monitoring service quality and improve the service efficiency, a new alarm method of CIMC with multiple event fusion in this paper via jointing analysis of multi-sensor status signals is proposed based on the equipment status signals generated by the CIMC terminal and abnormal alarm events definition. The experimental result shows that the proposed CIMC alarm method by means of multiple sensor signals in this paper can not only make up for the defect of a single sensor, but also reduces the false alarm rate caused by the random error of sensor and CIMC system. It can promote the intelligent monitoring efficiency of the manhole cover and be conducive to the construction of intelligent transportation and smart city.

Description: Measuring the consumption of electronic devices is a difficult and sensitive task. Data acquisition (DAQ) systems are often used to determine such consumption. In theory, measuring energy consumption is straight forward, just by acquiring current and voltage signals we can determine the consumption. However, a number of issues arise when a fine analysis is required. The main problem is that sampling frequencies have to be high enough to detect variations in the assessed signals over time. In that regard, some popular DAQ systems are based on RISC ARM processors for microcontrollers combined with analog-to-digital converters to meet high-frequency acquisition requirements. The efficient use of direct memory access (DMA) modules combined with pipelined processing in a microcontroller allows to improve the sample rate overcoming the processing time and the internal communication protocol limitations. This paper presents a novel approach for high-frequency energy measurement composed of a DMA rate improvement (data acquisition logic), a data processing logic and a low-cost hardware. The contribution of the paper is the combination of a double-buffered signal acquisition mechanism and an algorithm that computes the device’s energy consumption using parallel data processing. The combination of these elements enables a high-frequency (continuous) energy consumption measurement of an electronic device, improving the accuracy and reducing the cost of existing systems. We have validated our approach by measuring the energy consumed by elemental circuits and wireless sensors networks (WSNs) motes. The results indicate that the energy measurement error is less than 5% and that the proposed method is suitable to measure WSN motes even during sleep cycles, enabling a better characterization of their consumption profile.