ALBERT

Beschreibung: Multi-frequency signals have been accessible for most Global Navigation Satellite Systems . Existing studies have verified that using multi-frequency extra-wide-lane and wide-lane observations can realize decimeter, sub-decimeter and even centimeter rapid positioning. In this paper, a tightly combined wide-lane real-time kinematic positioning method using triple-frequency GPS and BDS is proposed. The differential inter-system bias is taken into consideration so that an inter-system differencing model is formed. Due to the influence of different frequencies between GPS and BDS, the double-difference wide-lane ambiguity between GPS and BDS reference satellites and the single-difference ambiguity of the BDS reference satellite are estimated jointly with the wide-lane differential inter-system bias. Thus, a full-rank model can be obtained without any external calibration. Using the stability of wide-lane differential inter-system bias in the estimation from epoch to epoch, redundant observations can be introduced, therefore the strength of the positioning model can be enhanced. Positioning performance under simulated obstructed environments is evaluated. The results show that the inter-system model can effectively improve the positioning compared with the conventional intra-system model for the severely obstructed situations.

Beschreibung: This article focuses on how to effectively make full use of the storage resources in vehicular cloud. A trust mechanism called DI-Trust (Trust Model Based on Dynamic Incentive Mechanism) is proposed to schedule vehicular cloud storage resource. The discussion is under the scenario of the parking lot where vehicle nodes are in static state. The model can reasonably arrange the suitable scheduling algorithm according to the attribute characteristics of different kind of service requirements. The trust value of a vehicle is updated according to the model to fully utilize the vehicle idle storage resources. The simulation experiment results show that the model can work effectively. It can objectively evaluate trust values of vehicle nodes, and construct the effective resource schedule of the vehicular cloud storage resource to meet needs from users.

Beschreibung: Fog computing is viewed as an extended technique of cloud computing. In Internet of things–based collaborative fog computing systems, a fog node aggregating lots of data from Internet of things devices has to transmit the information to distributed cloud servers that will collaboratively verify it based on some predefined auditing policy. However, compromised fog nodes controlled by an adversary might inject bogus data to cheat or confuse remote servers. It also causes the waste of communication and computation resources. To further control the lifetime of signing capability for fog nodes, an appropriate mechanism is crucial. In this article, the author proposes a time-constrained strong multi-designated verifier signature scheme to meet the above requirement. In particular, a conventional non-delegatable strong multi-designated verifier signature scheme with low computation is first given. Based on its constructions, we show how to transform it into a time-constrained variant. The unforgeability of the proposed schemes is formally proved based on the famous elliptic curve discrete logarithm assumption. The security requirement of strong signer ambiguity for our substantial constructions is also analyzed by utilizing the intractable assumption of decisional Diffie–Hellman. Moreover, some comparisons in terms of the signature size and computational costs for involved entities among related mechanisms are made.

Beschreibung: Time series have broad usage in the wireless Internet of Things. This article proposes a nonlinear time series prediction algorithm based on the Small-World Scale-Free Network after the AIC-Optimized Subtractive Clustering Algorithm (AIC-DSCA-SSNET, AD-SSNET) to predict the nonlinear and unstable time series, which improves the prediction accuracy. The AD-SSNET is introduced as a reservoir based on the echo state network to improve the predictive capability of nonlinear time series, and combined with artificial intelligence method to construct the prediction model training samples. First, the optimal clustering scheme of randomly distributed neurons in the network is adaptively obtained by the AIC-DSCA, then the AD-SSNET is constructed according to the intra-cluster priority connection algorithm. Finally, the reservoir synaptic matrix is calculated according to the synaptic information. Experimental results show that the proposed nonlinear time series prediction algorithm extends the feasible range of spectral radii of the reservoir, improves the prediction accuracy of nonlinear time series, and has great significance to time series analysis in the era of wireless Internet of Things.

Beschreibung: Vehicular Ad hoc NETwork is a promising technology providing important facilities for modern transportation systems. It has garnered much interest from researchers studying the mitigation of attacks including distributed denial of service attacks. Machine learning techniques, which mainly rely on the quality of the datasets used, play a role in detecting many attacks with a high level of accuracy. We conducted a comprehensive literature review and found many limitations on the datasets available for distributed denial of service attacks on Vehicular Ad hoc NETwork including the following: unavailability of online versions, an absence of distributed denial of service traffic, unrepresentative of Vehicular Ad hoc NETwork, and no information regarding the network configurations. Therefore, in this article, we proposed a novel simulation technique to generate a valid dataset called Vehicular Ad hoc NETwork distributed denial of service dataset, which is dedicated to Vehicular Ad hoc NETworks. Vehicular Ad hoc NETwork distributed denial of service dataset holds information on distributed denial of service attack traffic considering Vehicular Ad hoc NETwork architecture, traffic density, attack intensity, and nodes mobility. Well-known simulation tools such as SUMO, OMNeT++, Veins, and INET were used to ensure that all the properties of Vehicular Ad hoc NETwork have been captured. We then compared Vehicular Ad hoc NETwork distributed denial of service dataset with several studies to prove its novelty and evaluated the dataset using several machine learning models. We confirmed that studied models using this dataset achieved high accuracy above 99.5% except support-vector machine that achieved 97.3%.

Beschreibung: Aiming at the problems existing in existing steganalysis algorithms, this article proposes Motion Vector Coding Cost Change video steganalysis features based on Improved Motion Vector Reversion-Based features and Subtractive Probability of Coding Cost Optimal Matching features based on Subtractive Probability of Optimal Matching features from the perspective of the change of coding cost. Motion Vector Coding Cost Change features can be well consistent with the coding cost before recoding by analyzing the sub-pixel coding cost of recoding. By counting the sub-pixel coding costs of motion vectors before and after video recoding, the Sum of Absolute Difference values of motion vectors instead of predicted residuals are applied to steganalysis and detection, and the steganographic algorithm based on motion vectors is effectively detected. Experiments show that Motion Vector Coding Cost Change features have higher detection accuracy than Add-or-Subtract-One, Improved Motion Vector Reversion-Based, and other typical features in various steganography methods, and Subtractive Probability of Coding Cost Optimal Matching features have higher detection effect and better robustness than Subtractive Probability of Optimal Matching features.

Beschreibung: In this study, an interval extension method of a bi-iterative is proposed to determine a moving source. This method is developed by utilising the time difference of arrival and frequency difference of arrival measurements of a signals received from several receivers. Unlike the standard Gaussian noise model, the time difference of arrival - frequency difference of arrival measurements are obtained by interval enclosing, which avoids convergence and initialisation problems in the conventional Taylor-series method. Using the bi-iterative strategy, the algorithm can alternately calculate the position and velocity of the moving source in interval vector form. Simulation results indicate that the proposed scheme significantly outperforms other methods, and approaches the Cramer-Rao lower bound at a sufficiently high noise level before the threshold effect occurs. Moreover, the interval widths of the results provide the confidence degree of the estimate.

Beschreibung: Aiming at the shortcomings of the research on individual identification technology of emitters, which is primarily based on theoretical simulation and lack of verification equipment to conduct external field measurements, an emitter individual identification system based on Automatic Dependent Surveillance–Broadcast is designed. On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter’s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster–Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements.

Beschreibung: Suppressing the trajectory data to be released can effectively reduce the risk of user privacy leakage. However, the global suppression of the data set to meet the traditional privacy model method reduces the availability of trajectory data. Therefore, we propose a trajectory data differential privacy protection algorithm based on local suppression Trajectory privacy protection based on local suppression (TPLS) to provide the user with the ability and flexibility of protecting data through local suppression. The main contributions of this article include as follows: (1) introducing privacy protection method in trajectory data release, (2) performing effective local suppression judgment on the points in the minimum violation sequence of the trajectory data set, and (3) proposing a differential privacy protection algorithm based on local suppression. In the algorithm, we achieve the purpose Maximal frequent sequence (MFS) sequence loss rate in the trajectory data set by effective local inhibition judgment and updating the minimum violation sequence set, and then establish a classification tree and add noise to the leaf nodes to improve the security of the data to be published. Simulation results show that the proposed algorithm is effective, which can reduce the data loss rate and improve data availability while reducing the risk of user privacy leakage.

Beschreibung: Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge to satisfy the increasing amounts of low-latency tasks. However, challenges such as service interruption caused by user mobility occur. In order to address this problem, in this article, we first propose a multiple service placement algorithm, which initializes the placement of each service according to the user’s initial location and their service requests. Furthermore, we build a network model and propose a based on Lyapunov optimization method with long-term cost constraints. Considering the importance of user mobility, we use the Kalman filter to correct the user’s location to improve the success rate of communication between the device and the server. Compared with the traditional scheme, extensive simulation results show that the dynamic service migration strategy can effectively improve the service efficiency of mobile edge computing in the user’s mobile scene, reduce the delay of requesting terminal nodes, and reduce the service interruption caused by frequent user movement.

Beschreibung: The healthcare budget is increasing day-by-day as the population of the world increases. The same is the case regarding the workload of health care workers, that is, doctors and other practitioners. Under such a scenario where workload and cost are increasing drastically, there is a dire need of integrating recent technological enhancements with the said domain. Since the last decade, a lot of work is in the process considering the said integration bringing revolutionary changes. For remote monitoring, existing systems use different types of Internet of things devices that measure different health parameters. One of the major problems in such a system is to find an optimum routing approach that can resolve energy and thermal issues that are taking the limelight in the research arena. In this article, a dynamic routing technique is proposed which is keen to connect multiple in vivo/ex vivo Internet of things devices and a sink (focusing thermal and energy problem) and then forwarding data from sink to remote location for monitoring. Performance parameters are kept energy efficiency and thermal awareness and analytical results show that the proposed protocol supersedes existing approaches in said metrics.

Beschreibung: In order to ensure the strong real-time information sharing of Aerial Ad hoc Network, a low-delay information sharing algorithm for multiple-radio-per-platform networking is proposed based on the directional transmission capability of phased-array antenna. The algorithm introduces virtual nodes and virtual links in the process of topology generation first. By extracting topology information and choosing link grouping, it can effectively reduce redundant transmission and transmission latency of information sharing. Then, it is verified through simulation that the algorithm can reduce the latency by up to 49.8% and eliminate transmission redundancy. In addition, a direction selection algorithm is proposed for the variation of antenna beam direction. Simulation results show that the algorithm can further reduce the latency of information sharing and ensure the real time of information sharing, thus further improving the network performance.

Beschreibung: For wireless sensor network, the localization algorithm based on Voronoi diagram has been applied. However, the location accuracy node position in wireless sensor network needs to be optimized by the analysis of the literature, a node location algorithm based on Voronoi diagram and support vector machine is proposed in this article. The basic idea of the algorithm is to first divide the region into several parts using Voronoi diagram and anchor node in the localization region. The range of the initial position of the target node is obtained by locating the target node in each region and then the support vector machine is used to optimize the position of the target node accurately. The localization performance of the localization algorithm is analyzed by simulation and real-world experiments. The experimental results show that the localization algorithm proposed in this article is better than the optimal region selection strategy based on Voronoi diagram-based localization scheme and Weighted Voronoi diagram-based localization scheme localization algorithms in terms of localization accuracy. Therefore, the performance of the localization algorithm proposed in this article is verified.

Beschreibung: Clustering technology is one of the crucial technologies to prolong the lifetime in wireless sensor networks. However, most cluster schemes choose cluster head randomly to send data without considering load balancing and security. In addition, some cluster heads in the highly active area may be overloaded, while others in the low active area may be overloaded, which may easily lead to extreme imbalance in task allocation. Our research on relevant literature shows that the existing authentication schemes do not fully consider the load balancing of cluster heads, while the load balancing schemes ignore the security authentication of cluster heads. Therefore, this article effectively combines load balancing and security verification, and proposes a lightweight load balancing and verification scheme (secure load and energy balancing) based on clustered wireless sensor networks. Secure load and energy balancing implements cluster head’s authentication and confidentiality and integrity of all messages in load balancing. This scheme not only effectively maintains the energy balance of the whole network but also successfully improves the security overhead, thus prolonging the network lifetime. The simulation results show that compared with other similar schemes, this scheme has higher packet forwarding rate, longer network life, and lower overhead. This further proves that the scheme is energy-saving, safe, dynamic, stable, and sustainable.

Beschreibung: Ultrasonic computerized tomography is a promising technique for damage detection by enabling ultrasonic waves via multiple measurement paths leading to accurate localization of structural damage. Unlike traditional ultrasonic computerized tomography that requires numerous measurements and costly computation, a compressive sampling advancing both the measuring phase and the imaging phase is proposed in this study to achieve accurate identification with no low-speed traditional ultrasonic computerized tomography technique measurements or costly computation in real-world applications. The proposed rapid ultrasonic computerized tomography approach advances both the measuring phase and the imaging phase. In the measuring phase, far few ultrasonic measurement paths are randomly selected to capture the characteristics of the ultrasonic waves carrying the underlying damaged information. And in the imaging phase, ℓ1-norm minimization optimization algorithm is used to reconstruct the internal damage, rendering the sparest solution related to the physical damages. The functionality of the proposed approach is validated by both numerical simulation and experimental testing. The results indicate that the improved ultrasonic computerized tomography technique in compressive sampling framework has a great potential for rapid damage detection, which is a game-changing technique for accurate and cost-efficient damage detection in real-world applications.

Beschreibung: Aiming at the key optimization problems of wireless sensor networks in complex industrial application environments, such as the optimum coverage and the reliability of the network, a novel topology optimization of coverage-oriented strategy for wireless sensor networks based on the wolf pack algorithm is proposed. Combining the characteristics of topology structure of wireless sensor networks and the optimization idea of the wolf pack algorithm redefines the group’s wandering and surprise behavior. A novel head wolf mutation strategy is proposed, which increases the neighborhood search range of the optimal solution, enhances the uniformity of wolf pack distribution and the ergodicity ability of the wolf pack search, and greatly improves the calculation speed and the accuracy of the wolf pack algorithm. With the same probability, the cluster heads are randomly selected periodically, and the overall energy consumption of wireless sensor networks is evenly distributed to the sensor node to realize the balanced distribution of the data of the member nodes in the cluster and complete the design of the topology optimization of wireless sensor networks. Through algorithm simulation and result analysis, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the wolf swarm algorithm shows its advantages in terms of the residual energy of the sensor node, the average transmission delay, the average packet delivery rate, and the coverage of the network. Among them, compared with the particle swarm optimization algorithm and artificial fish swarm algorithm, the remaining energy of nodes has increased by 9.5% and 15.5% and the average coverage of the network has increased by 10.5% and 5.6%, respectively.

Beschreibung: This article describes the implementation of a multiple-input multiple-output acoustic communication link in shallow water conditions to enable a software-defined acoustic modem with a maximum transmission rate of 20 kbps in a 5-kHz bandwidth. The reliability improvement of a low-complexity Alamouti space–time block code is evaluated to improve the diversity in a high-rate transmission mode using single carrier modulation, as well as in a low-rate transmission mode relying on continuous-phase frequency-shift keying. Using measurements in realistic subsea conditions, the effect of the spatial channel correlation is demonstrated. It is found that for the space–time block code/continuous-phase frequency-shift keying, the spatial diversity is significantly degraded due to the high spatial correlation. In contrast, for the high-mode transmission rate, space–time block code with single carrier modulation offers a bit error rate improvement by a factor over hundred, in comparison to a single transmit element, demonstrating that the multiple-input multiple-output optimal code depends on the software-defined acoustic modem transmission mode.

Beschreibung: Partial discharges are the major cause of deterioration in the insulation characteristics of switchgears. Therefore, timely detection of partial discharge in switchgear and potential insulation faults is an urgent problem that needs to be addressed in the power supervision industry. In this study, a device was proposed for online monitoring of high-voltage switchgears based on pulse current method and ozone (O3) detection. The pulse current method obtains the PD signal by monitoring the phase holes on the switch indicator. Occurrence of a partial discharge in a certain phase leads to the production of a discharge pulse, which can be coupled out by a capacitive sensor. The current spectrum and the O3 produced by partial discharge were processed via fast Fourier transform for accurate diagnosis of the occurrence of partial discharge and its severity in switchgears. The proposed method allows for convenient acquisition of the partial discharge signal, simple installation of the device, and realization with inexpensive sensors.

Beschreibung: It is the most important and challenging problem to share the data safely in cloud computing. Some so-called trusted third parties may also infringe users’ data privacy. It is an urgent problem for data owners to share data safely with the designated users rather than the third party or other users. Traditional encryption schemes utilize different keys to produce multiple encrypted copies of the same data for users. It is no longer applicable for cloud data sharing security. Attribute-based encryption can solve above problems, but it needs to rely on trusted third parties to protect the users’ privacy. In this article, in order to address the above problems, we propose a blockchain-based ciphertext-policy attribute-based encryption scheme for cloud data secure sharing without relying on any trusted third parties. Blockchain-based ciphertext-policy attribute-based encryption scheme can protect the rights and security of data owner. Compared with existing cloud security schemes, the proposed scheme has more advantages in terms of the six aspects: (1) data owners have the authority to decide who can decrypt the data; (2) the operations of users are retained permanently, and all records are tamper-proof; (3) our proposed scheme has the characteristic of “one-to-many” encryption, and data is encrypted only once; (4) our scheme does not rely on any trusted third party; (5) in terms of the discrete logarithm problem and decisional q parallel-bilinear Diffie–Hellman exponent problem, we prove that our proposed scheme is secure; and (6) experiment shows that our proposed scheme is more efficient than the comparative scheme.

Beschreibung: Aiming at the problems of low data transmission efficiency and uneven energy consumption caused by unreliable link communication in the routing process of wireless sensor networks, this article designs a routing game algorithm based on link quality. In this article, the index for evaluating link quality is defined first. Then, the link quality, node residual energy, and minimum jump transmission strategy are integrated into the utility function to establish a game model to determine the best next hop transmission node. Finally, the routing optimal transmission path is obtained according to the analysis of the existence of Nash equilibrium in the game. In the simulation experiment, the influence of the change of link quality parameters on the performance of the algorithm is analyzed, and the proposed algorithm is compared with non-linear weight particle swarm optimization (NWPSO) algorithm and Low Energy Adaptive Clustering Hierarchy-Improvement (LEACH-IMPT) algorithm in three aspects: the number of surviving nodes, network lifetime, and network energy consumption. The results show that the network lifetime of this method is 16.8% longer than that of LEACH-IMPT algorithm and 7.5% longer than that of NWPSO algorithm. This shows that the algorithm can effectively balance the network energy consumption and prolong the network life cycle. In addition, according to the routing path obtained in the simulation experiment, the optimality of its link quality is verified in the real experimental environment, and the experimental results prove the feasibility of the method in this article in practice.

Beschreibung: Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

Beschreibung: While it is well understood that the emerging Social Internet of Things offers the capability of effectively integrating and managing massive heterogeneous IoT objects, it also presents new challenges for suggesting useful objects with certain service for users due to complex relationships in Social Internet of Things, such as user’s object usage pattern and various social relationships among Social Internet of Things objects. In this study, we focus on the problem of service recommendation in Social Internet of Things, which is very important for many applications such as urban computing, smart cities, and health care. We propose a graph-based service recommendation framework by jointly considering social relationships of heterogeneous objects in Social Internet of Things and user’s preferences. More exactly, we learn user’s preference from his or her object usage events with a latent variable model. Then, we model users, objects, and their relationships with a knowledge graph and regard Social Internet of Things service recommendation as a knowledge graph completion problem, where the “like” property that connects users to services needs to be predicted. To demonstrate the utility of the proposed model, we have built a Social Internet of Things testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

Beschreibung: A routing-benefited deployment algorithm combining static and dynamic layouts is proposed, and its comprehensive performance evaluation is given in this article. The proposed routing-benefited deployment algorithm is intended to provide a suitable network deployment and subsequent data transmission approach for underwater optical networking and communication. Static nodes are anchored for long-term monitoring, and movable nodes can adjust their depths based on the virtual force and move with the variation of area-of-interest changing. Then, nodes begin to collect data that they can monitor and transmit to sink nodes. Here, the underwater wireless optical communication model is described to actualize the real environment, and the vector-based forwarding protocol is particularly considered to compare the impact of different deployment algorithms on routing. It is shown by simulation experiment results that routing-benefited deployment algorithm outperforms several existing traditional virtual force deployment algorithms in terms of coverage, lifetime, energy consumption balance, packet-loss rate, and time-delay.

Beschreibung: An important node identification algorithm based on an improved structural hole and K-shell decomposition algorithm is proposed to identify important nodes that affect security in complex networks. We consider the global structure of a network and propose a network security evaluation index of important nodes that is free of prior knowledge of network organization based on the degree of nodes and nearest neighborhood information. A node information control ability index is proposed according to the structural hole characteristics of nodes. An algorithm ranks the importance of nodes based on the above two indices and the nodes’ local propagation ability. The influence of nodes on network security and their own propagation ability are analyzed by experiments through the evaluation indices of network efficiency, network maximum connectivity coefficient, and Kendall coefficient. Experimental results show that the proposed algorithm can improve the accuracy of important node identification; this analysis has applications in monitoring network security.

Beschreibung: In the past decades, emerging technologies such as unmanned driving and indoor navigation have developed rapidly, and simultaneous localization and mapping has played unparalleled roles as core technologies. However, dynamic objects in complex environments will affect the positioning accuracy. In order to reduce the influence of dynamic objects, this article proposes an improved simultaneous localization and mapping algorithm combined with semantic segmentation model. First, in the pre-processing stage, in order to reduce the influence of dynamic features, fully convolutional network model is used to find the dynamic object, and then the output image is masked and fused to obtain the final image without dynamic object features. Second, in the feature-processing stage, three parts are improved to reduce the computing complexity, which are extracting, matching, and eliminating mismatching feature points. Experiments show that the absolute trajectory accuracy in high dynamic scene is improved by 48.58% on average. Meanwhile, the average processing time is also reduced by 21.84%.

Beschreibung: In this article, based on wavelet reconstruction and fractal dimension, a medical image authentication method is implemented. According to the local and global methods, the regularity of the mutation structure in the carrier information is analyzed through a measurement defined in the medical image transformation domain. To eliminate the redundancy of the reconstructed data, the fractal dimension is used to reduce the attributes of the reconstructed wavelet coefficients. According to the singularity of the fractal dimension of the block information, the key features are extracted and the fractal feature is constructed as the authentication feature of the images. The experimental results show that the authentication scheme has good robustness against attacks, such as JPEG compression, multiplicative noise, salt and pepper noise, Gaussian noise, image rotation, scaling attack, sharpening, clipping attack, median filtering, contrast enhancement, and brightness enhancement.

Beschreibung: In this article, a comprehensive parameter analysis for the polling-based medium access control protocol is executed. The theoretical expressions of the relationship between network parameters and performance including delay and energy consumption are given for the first time. The specific conclusions in this article are as follows: (1) awake duration is the parameter that has the greatest impact on delay and energy consumption. Increasing the duty cycle (awake duration) will effectively reduce the delay, but will also increase the energy consumption within a certain range; (2) increasing polling duration can reduce the delay, but it will also increase the energy consumption; and (3) more forwarding nodes cause a smaller delay, and it can save the energy with modest increase of delay by reducing the polling duration. An adaptive parameter optimization polling-based medium access control protocol is proposed to optimize network performance. In this protocol, the residual energy gets fully used to increase awake duration and polling duration, which makes the delay smaller, and the network maintains a long lifetime meanwhile. Based on the results of the analysis, the adaptive parameter optimization polling-based medium access control protocol proposed in this article reduces the delay by 22.40% and increases the energy efficiency by 23.25%.

Beschreibung: The existing equipment of civil aircraft cargo fire detection mainly uses photoelectric smoke detectors, which has a high false alarm rate. According to Federal Aviation Agency’s statistics, the false alarm rate is as high as 99%. Since, in the cargo of civil aircraft, visible image processing technology cannot be used to detect smoke in the event of a fire due to the closed dark environment, a novel smoke detection method using infrared image processing technology is presented. Experiments were conducted under different environment pressures in the full-size cargo of civil aircraft. The results show that the proposed method can effectively detect smoke at the early stage of fire which is applicable for fire detection in civil aircraft cargoes.

Beschreibung: For aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved.

Beschreibung: Effective scheduling of limited communication resources is one of the critical methods for data transmission in the Internet of things. However, the time slot utilization rate of many existing resource scheduling methods of Internet of things is not high. This article proposes a new efficient resource scheduling based on routing tree and detection matrix for Internet of things. In heterogeneous Internet of things, according to the different working modes and functions, the nodes are divided into Internet of things devices, routing nodes, and base station. We use time slot multiplexing to improve the time slot utilization of continuous transmission in Internet of things. First, the time slot allocation table in a round is obtained by the time slot scheduling based on the routing tree. Then, the collision matrix and the transmission matrix are established based on the time slot allocation table in a round. Finally, the minimum time slot scheduling in continuous rounds is determined based on the routing tree and the detection matrix. The experimental results show that the resource scheduling based on routing tree and detection matrix effectively improves the utilization of time slots and improves the throughput of the Internet of things.

Beschreibung: Power management in wireless sensor networks is very important due to the limited energy of batteries. Sensor nodes with harvesters can extract energy from environmental sources as supplemental energy to break this limitation. In a clustered solar-powered sensor network where nodes in the network are grouped into clusters, data collected by cluster members are sent to their cluster head and finally transmitted to the base station. The goal of the whole network is to maintain an energy neutrality state and to maximize the effective data throughput of the network. This article proposes an adaptive power manager based on cooperative reinforcement learning methods for the solar-powered wireless sensor networks to keep harvested energy more balanced among the whole clustered network. The cooperative strategy of Q-learning and SARSA( λ) is applied in this multi-agent environment based on the node residual energy, the predicted harvested energy for the next time slot, and cluster head energy information. The node takes action accordingly to adjust its operating duty cycle. Experiments show that cooperative reinforcement learning methods can achieve the overall goal of maximizing network throughput and cooperative approaches outperform tuned static and non-cooperative approaches in clustered wireless sensor network applications. Experiments also show that the approach is effective in response to changes in the environment, changes in its parameters, and application-level quality of service requirements.

Beschreibung: Due to the complex environments in real fields, it is challenging to conduct identification modeling and diagnosis of plant leaf diseases by directly utilizing in-situ images from the system of agricultural Internet of things. To overcome this shortcoming, one approach, based on small sample size and deep convolutional neural network, was proposed for conducting the recognition of cucumber leaf diseases under field conditions. One two-stage segmentation method was presented to acquire the lesion images by extracting the disease spots from cucumber leaves. Subsequently, after implementing rotation and translation, the lesion images were fed into the activation reconstruction generative adversarial networks for data augmentation to generate new training samples. Finally, to improve the identification accuracy of cucumber leaf diseases, we proposed dilated and inception convolutional neural network that was trained using the generated training samples. Experimental results showed that the proposed approach achieved the average identification accuracy of 96.11% and 90.67% when implemented on the data sets of lesion and raw field diseased leaf images with three different diseases of anthracnose, downy mildew, and powdery mildew, significantly outperforming those existing counterparts, indicating that it offered good potential of serving field application of agricultural Internet of things.

Beschreibung: The spatiotemporal dynamic changes of forest biomass can provide scientific reference and scheme for improving the quality of forest resources and the ecological environment in karst areas. In this article, the China’s National Forest Continuous Inventory data (from 1984 to 2015) was used to analyze the dynamic changes of forest biomass with the univariate linear slope k, barycenter trajectory, improved hot spots detection which was applied in the analysis of forest biomass dynamic change, and geospatial detector method in Guizhou in the first time. The results showed that the total forest biomass had a steady upward trend, 29.3% unit biomass of the forest had significantly increased, while 1.4% decreased dramatically. The forest biomass gravity center shifted from Qiandongnan to Qiannan, with a total distance of 54.1 km. Thus, the following conclusions were drawn: (1) benefiting from the effective implementation of forestry-related policies, the forest biomass had significant increased in a long time series, especially for the artificial shelter forest; (2) the gravity center shifted to the northwest and the number of level 1 forest biomass hot spots increased year by year, which showed a generalized symmetric pattern along the Wujiang River mainstream; and (3) the results of geographical survey showed that the change of forest biomass was greatly affected by topography, climate and human activities, among which terrain factors had the greatest impact.

Beschreibung: Low-power lossy networks performance relies heavily on the wireless node battery status. Furthermore, Routing Protocol for Low-Power and Lossy Network routing protocol was not optimally designed with sustainable energy consumption in mind to suit these networks. Prolonging the lifespan of these networks is of utmost priority. This article introduces a solar energy harvesting module to power energy-constrained network devices and quantifies the effect of using harvested energy on prolonging their network lifetime when Routing Protocol for Low-Power and Lossy Network routing protocol is used. Simulation of the new developed module is conducted in three different scenarios using Contiki Cooja simulator sporting Zolertia Z1 motes. Furthermore, the harvested energy used was fed from a Cooja-based Simulation model of actual PV supercapacitor circuit design. All battery levels were set to 1% of their total capacity for all nodes in the network to speed up observing the energy harvesting effect. The performance evaluation results showed that the network with no-energy harvesting operated for time duration of 4:08:04 time units (i.e. hour:minute:second) with a dramatic decrease in connection between nodes in the network. However, the same network, when using the harvested energy to back up the battery operation, lasted for 6:40:01 in time units with improved connectivity, a total extended network lifetime of 2:31:97-time units. Furthermore, for the Routing Protocol for Low-Power and Lossy Network routing metrics, OF0 outperformed ETX in term of throughput, packet delivery ratio, energy consumption, and network connectivity. Results indicate that the developed harvested energy module fits perfectly for any Cooja-based simulation and mimics actual photovoltaic-based supercapacitor battery. It should also help researchers introduce and quantify accurately new energy consumption-based routing metrics for Routing Protocol for Low-Power and Lossy Network.

Beschreibung: Wireless body area network can be employed to collect patient’s electronic health data. To guarantee the reliability and confidentiality of the collected data, secure data transmission in wireless body area network is required. In wireless body area network, a mutual authentication process has to be carried out between the controller and sensors to ensure their legitimacy, and a key distribution mechanism is required to secure communication after successful mutual authentication. Li et al. proposed a cryptographic solution, which allows group device pairing authentication and key agreement but has low authentication efficiency and key leakage problems. To address these issues, a group authentication and key distribution scheme is proposed in this article. It enables effectively mutual authentication between controller and sensors, supports all signatures of sensors in the group to be checked by the controller through aggregation verification to achieve efficient authentication, and allows key distribution during authentication to improve the computation efficiency. Security analysis indicates that the proposed scheme enjoys existentially unforgeability, and theoretical and experimental comparison demonstrates its practicality in terms of computation and communication cost.

Beschreibung: Intrusion detection systems play a vital role in traffic flow monitoring on Internet of Things networks by providing a secure network traffic environment and blocking unwanted traffic packets. Various intrusion detection systems approaches have been proposed previously based on data mining, fuzzy techniques, genetic, neurogenetic, particle swarm intelligence, rough sets, and conventional machine learning. However, these methods are not energy efficient and do not perform accurately due to the inappropriate feature selection or the use of full features of datasets. In general, datasets contain more than 10 features. Any machine learning–based lightweight intrusion detection systems trained with full features turn into an inefficient and heavyweight intrusion detection systems. This case challenges Internet of Things networks that suffer from power efficiency problems. Therefore, lightweight (energy-efficient), accurate, and high-performance intrusion detection systems are paramount instead of inefficient and heavyweight intrusion detection systems. To address these challenges, a new approach that can help to determine the most effective and optimal feature pairs of datasets which enable the development of lightweight intrusion detection systems was proposed. For this purpose, 10 machine learning algorithms and the recent BoT-IoT (2018) dataset were selected. Twelve best features recommended by the developers of this dataset were used in this study. Sixty-six unique feature pairs were generated from the 12 best features. Next, 10 full-feature-based intrusion detection systems were developed by training the 10 machine learning algorithms with the 12 full features. Similarly, 660 feature-pair-based lightweight intrusion detection systems were developed by training the 10 machine learning algorithms via each feature pair out of the 66 feature pairs. Moreover, the 10 intrusion detection systems trained with 12 best features and the 660 intrusion detection systems trained via 66 feature pairs were compared to each other based on the machine learning algorithmic groups. Then, the feature-pair-based lightweight intrusion detection systems that achieved the accuracy level of the 10 full-feature-based intrusion detection systems were selected. This way, the optimal and efficient feature pairs and the lightweight intrusion detection systems were determined. The most lightweight intrusion detection systems achieved more than 90% detection accuracy.

Beschreibung: Source localisation is an important component in the application of wireless sensor networks, and plays a key role in environmental monitoring, healthcare and battlefield surveillance and so on. In this article, the source localisation problem based on time-of-arrival measurements in asynchronous sensor networks is studied. Because of imperfect time synchronisation between the anchor nodes and the signal source node, the unknown parameter of start transmission time of signal source makes the localisation problem further sophisticated. The derived maximum-likelihood estimator cost function with multiple local minimum is non-linear and non-convex. A novel two-step method which can solve the global minimum is proposed. First, by leveraging dimensionality reduction, the maximum (minimum) distance maximum (minimum) time-of-arrival matching-based second-order Monte Carlo method is applied to find a rough initial position of the signal source with low computational complexity. Then, the rough initial position value is refined using trust region method to obtain the final positioning result. Comparative analysis with state-of-the-art semidefinite programming and min–max criterion-based algorithms are conducted. Simulations show that the proposed method is superior in terms of localisation accuracy and computational complexity, and can reach the optimality benchmark of Cramér–Rao Lower Bound even in high signal-to-noise ratio environments.

Beschreibung: Equipped with micro wireless sensor nodes, a unmanned aerial vehicle) cluster can form an emergency communication network, which can have several applications such as environmental monitoring, disaster relief, military operations and so on. However, situations where there is excessive aggregation and small amount of dispersion of the unmanned aerial vehicle cluster may occur when the network is formed. To mitigate these, a solution based on a 3D virtual force driven by self-adaptive deployment (named as 3DVFSD) is proposed. As a result, the three virtual forces of central gravity, uniform force, and boundary constraint force are combined to act on each node of the communication network. By coordinating the distance between the nodes, especially the threshold of the distance between the boundary node and the boundary, the centralized nodes can be relatively dispersed. Meanwhile, the nodes can be prevented from being too scattered by constraining the distance from the boundary node to the end. The simulation results show that the 3DVFSD algorithm is superior to the traditional virtual force-driven deployment strategy in terms of convergence speed, coverage, and uniformity.

Beschreibung: With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.

Beschreibung: Climate change brings many changes in a physical environment like plants and leaves. The flowers and plants get affected by natural climate and local weather extremes. However, the projected increase in the frost event causes sensitivity in plant reproduction and plant structure vegetation. The timing of growing and reproduction might be an essential tactic by which plant life can avoid frost. Flowers are more sensitive to hoarfrost than leaves but more sensitive to frost in most cases. In most cases, frost affects the size of the plant, its growth, and the production of seeds. In this article, we examined that how frost affects plants and flowers? How it affects the roots and prevents the growth of plants, vegetables, and fruits? Furthermore, we predicted how the frost will grow and how we should take early precautions to protect our crops? We presented the convolutional neural network model framework and used the conv1d algorithm to evaluate one-dimensional data for frost event prediction. Then, as part of our model contribution, we preprocessed the data set. The results were comparable to four weather stations in the United States. The results showed that our convolutional neural network model configuration is reliable.

Beschreibung: Applications of quantum computing are growing at a very fast pace, for example, from quantum computers to quantum algorithms and even to the development of the quantum Internet. However, the use of quantum technology in wireless sensor networks has not been thoroughly investigated just yet. This is in part due to the complexity of using big, costly, and highly energy-consuming machines that are quantum computers to this date, compared to the nodes used in wireless sensor networks which are small, inexpensive, and operate with very low energy consumption requirements. However, we can expect that in the future (possibly in the next decade) quantum computers will be commercial and reduced in size, and hence, they can be used for sensor network applications which are the basis of the Internet of Things. In this review, we study the road from quantum computing to quantum wireless sensor networks and how the analysis and design of these systems have to change to accommodate quantum capabilities in sensors, processors, communication links, and overall performance of these monitoring networks.

Beschreibung: “Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.

Beschreibung: Wireless Sensor Networks are very convenient to monitor structures or even materials, as in McBIM project (Materials communicating with the Building Information Modeling). This project aims to develop the concept of “communicating concretes,” which are concrete elements embedding wireless sensor networks, for applications dedicated to Structure Health Monitoring in the construction industry. Due to applicative constraints, the topology of the wireless sensor network follows a chain-based structure. Node batteries cannot be replaced or easily recharged, it is crucial to evaluate the energy consumed by each node during the monitoring process. This area has been extensively studied leading to different energy models to evaluate energy consumption for chain-based structures. However, no simple, practical, and analytical network energy models have yet been proposed. Energy evaluation models of periodic data collection for chain-based structures are proposed. These models are compared and evaluated with an Arduino XBee–based platform. Experimental results show the mean prediction error of our models is 5%. Realizing aggregation at nodes significantly reduces energy consumption and avoids hot-spot problem with homogeneous consumptions along the chain. Models give an approximate lifetime of the wireless sensor network and communicating concretes services. They can also be used online by nodes for a self-assessment of their energy consumptions.

Beschreibung: As concrete is a building material that is widely used in the field of infrastructure construction, and its quality is related to the quality and service life in infrastructure engineering, concrete strength is an important reference index that reflects the concrete quality. Based on this, two commercial concretes A and B are selected in Henan Province, China, to perform the concrete strength test under same condition curing and standard condition curing in the pull-out post-insert method, cubic compression, rebound method, and drilling core method. The relationships between the different curing conditions, cubic compressive strength, core sample strength, rebound strength, and pull-out force of different commercial concretes are compared and analyzed. Through a comparative analysis and while considering the convenience and accuracy requirements, the strength curves of the two different concretes based on the pull-out post-insert method were fitted and analyzed, and the local strength curve test in Henan based on the pull-out post-insert method was established. The research results provide technical support for evaluating the strength of concrete structures using the pull-out post-insert method, which has important engineering significance for improving the popularize and application of the pull-out post-insert method.

Beschreibung: This correspondence proposes a jointly-designed quasi-cyclic (QC) low-density parity-check (LDPC)-coded multi-relay cooperation with a destination node realized by multiple receive antennas. First, a deterministic approach is utilized to construct different classes of binary QC-LDPC codes with no length-4 cycles. Existing methods put some limitations in terms of code length and rate in order to provide high error-correction performance. Therefore, this article gives three classes of QC-LDPC codes based on a combinatoric design approach, known as cyclic difference packing (CDP), with flexibility in terms of code-length and rate selection. Second, the proposed CDP-based construction is utilized to jointly-design QC-LDPC codes for coded-relay cooperation. At the receiver, the destination node is realized by multiple receive antennas, where maximal-ratio combining (MRC) and sum-product algorithm (SPA)-based joint iterative decoding are utilized to decode the corrupted sequences coming from the source and relay nodes. Simulation results show that the proposed QC-LDPC coded-relay cooperations outperform their counterparts with a coding gain of about 0.25 dB at bit-error rate (BER) [Formula: see text] over a Rayleigh fading channel in the presence of additive white Gaussian noise. Furthermore, the extrinsic-information transfer (EXIT) chart analysis has been used to detect the convergence threshold of proposed jointly-designed QC-LDPC codes. Numerical analysis shows that the proposed jointly-designed QC-LDPC codes provide a better convergence as compared to their counterparts under the same conditions.

Beschreibung: Vehicular ad-hoc networks allow vehicles to exchange messages pertaining to safety and road efficiency. Building trust between nodes can, therefore, protect vehicular ad-hoc networks from malicious nodes and eliminate fake messages. Although there are several trust models already exist, many schemes suffer from varied limitations. For example, many schemes rely on information provided by other peers or central authorities, for example, roadside units and reputation management centers to ensure message reliability and build nodes’ reputation. Also, none of the proposed schemes operate in different environments, for example, urban and rural. To overcome these limitations, we propose a novel trust management scheme for self-organized vehicular ad-hoc networks. The scheme is based on a crediting technique and does not rely on other peers or central authorities which distinguishes it as an economical solution. Moreover, it is hybrid, in the sense it is data-based and entity-based which makes it capable of revoking malicious nodes and discarding fake messages. Furthermore, it operates in a dual-mode (urban and rural). The simulation has been performed utilizing Veins, an open-source framework along with OMNeT++, a network simulator, and SUMO, a traffic simulator. The scheme has been tested with two trust models (urban and rural). The simulation results prove the performance and security efficacy of the proposed scheme.

Beschreibung: In underwater wireless sensor networks, sensor position information has important value in network protocols and collaborative detection. However, many challenges were introduced in positioning sensor nodes due to the complexity of the underwater environment. Aiming at the problem of the stratification effect of underwater acoustic waves, the long propagation delay of messages, as well as the mobility of sensor nodes, a mobile target localization scheme for underwater wireless sensor network is proposed based on iterative tracing. Four modules are established in the mobile target localization based on iterative tracing: the data collection and rough position estimation, the estimation and compensation of propagation delay, the node localization, and the iteration. The deviation of distance estimation due to the assumption that acoustic waves propagate along straight lines in an underwater environment is compensated by the mobile target localization based on iterative tracing, and weighted least squares estimation method is used to perform linear regression. Moreover, an interacting multiple model algorithm is put forward to reduce the positioning error caused by the mobility of sensor nodes, and the two services of node time synchronization and localization assist each other during the iteration to improve the accuracy of both parties. The simulation results show that the proposed scheme can achieve higher localization accuracy than the similar schemes, and the positioning errors caused by the above three problems can be reduced effectively.

Beschreibung: In the hybrid communication scenario of the Heterogeneous Cloud Radio Access Network and Device-to-Device in 5G, spectrum efficiency promotion and the interference controlling caused by spectrum reuse are still challenges. In this article, a novel resource management method, consisting of power and channel allocation, is proposed to solve this problem. An optimization model to maximum the system throughput and spectrum efficiency of the system, which is constrained by Signal to Interference plus Noise Ratio requirements of all users in diverse layers, is established. To solve the non-convex mixed integer nonlinear optimization problem, the optimization model is decomposed into two sub-problems, which are all solvable quasi-convex power allocation and non-convex channel allocation. The first step is to solve a power allocation problem based on solid geometric programming with the vertex search method. Then, a channel allocation constructed by three-dimensional hypergraph matching is established, and the best result of this problem is obtained by a heuristic greed algorithm based on the bipartite conflict graph and µ-claw search. Finally, the simulation results show that the proposed scheme improves the throughput performance at least 6% over other algorithms.

Beschreibung: To improve the accuracy and generalization of tunnel personnel positioning systems, this article proposes a quadratic weighted centroid algorithm. By adopting a Gaussian filter model to improve the range accuracy of the received signal strength indicator algorithm and combining the centroid algorithm and weighting factor with a trilateration positioning model, a quadratic weighted centroid algorithm is proposed to improve the positioning accuracy of unknown positioning nodes. The key ideas behind the quadratic weighted centroid algorithm include an optimization of the received signal strength indicator range value scheme, a centroid algorithm based on trilateral measurement positioning, and a weighting factor to improve the positioning accuracy of the trilateral centroid positioning algorithm. Compared with the centroid algorithm, the Min-Max multilateration algorithm, and the weighted centroid based on distance algorithm, the simulation results showed that (1) the positioning performance of the quadratic weighted centroid algorithm was superior to the other three algorithms; (2) when the reference nodes were symmetrically arranged, the positioning accuracy was higher than a fold line layout; and (3) when the lateral reference node spacing was extended from 20 to 30 m, the average positioning error met positioning accuracy requirements, which could reduce overall system costs.

Beschreibung: Tensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this article proposes a tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction, which mainly includes three parts: tensor dictionary representation, dictionary preprocessing, and dictionary update. Specifically, the tensor is respectively performed by the sparse representation and Tucker decomposition, from which one can obtain the dictionary, sparse coefficient, and core tensor. Furthermore, the sparse representation can be obtained through the relationship between sparse coefficient and core tensor. In addition, the dimensionality of the input tensor is reduced by using the concentrated dictionary learning. Finally, some experiments show that, compared with other algorithms, the proposed algorithm has obvious advantages in preserving the original data information and denoising ability.

Beschreibung: With the emergence of new smart technologies, including the Internet of Things, wireless media are playing an important role to connect numerous devices to fulfill the requirements of newly developed communication systems. The massive connectivity, therefore, made the wireless spectrum too crowded and gave several challenges to resisting against potential wireless jammers. Note that, the two main challenges that have always been a part of any communication system, especially in the case of wireless communication, are information security and information jamming. Carefully considering the given challenges, this study uses a new advanced anti-jamming approach, a modulation technique based on the frequency-hopping spread spectrum, which has notably high resistance accounted against various potential jammers. The objective of this study is two-fold. First, the physical channel properties are considered, and the random bits are transmitted, employing a cryptographic secured hoping-spread pattern, having a set of carrier frequencies, known at both sides of the transmission. Second, the hashing code is computed only for the key, and transmitted along the original hopset, but with distinct frequencies set. The deployed practical anti-jamming approach, therefore, computed a high efficiency to examine the information secrecy well and primarily the connection availability even in the presence of the jammers. Moreover, this study considered and modeled a communication system and evaluated the proposed system’s performance, applying the theories of Shannon’s entropy and Wyner’s entropy (i.e. Wyner’s wiretap channel), to anticipate the system’s perfect secrecy, even in the worst case when jammer has unlimited computational capabilities.

Beschreibung: Histogram shifting is an effective manner to achieve reversible watermarking, which works by shifting pixels between the peak point and its nearest zero point in histogram to make room for watermark embedding. However, once zero point is absent, the algorithm suffers from overflowing problem. Even though some works attempt to deal with this risk by introducing auxiliary information, such as a location map, they occupy a lot of embedding capacity inevitably. In this article, in order to deal with overflowing problem efficiently, we propose a border following–based reversible watermarking algorithm for images. With the help of border following algorithm and pre-processing, available regions with at least one zero point are recognized to embed watermark so that auxiliary information is not needed any more. And the algorithm utilized also ensures the same border can be re-recognized from the watermarked image without error, thus the correctness is also guaranteed. The performance of the proposed algorithm is evaluated using classic image datasets in this area, and the results not only validate the effectiveness of the proposed algorithm but also indicate its advantages compared with the classic histogram shifting–based reversible watermarking algorithm as well as the state of the art.

Beschreibung: The architecture model of the Internet of thing system is the primary foundation for the design and implementation of the Internet of thing system. This article discusses the method and practice of time automaton modeling and model checking for the architecture of the Internet of thing system from the state and time dimensions. This article introduces the theory and method of modeling using time automata. And then, combined with the actual need of the elderly health cabin Internet of thing system, a dynamic and fault-tolerant time automaton model is established through a relatively complete architecture modeling. The model checking method verifies that the designed Internet of thing system has no deadlock system activity, service correctness, and timeliness correctness. The results of modeling experiments and model validation show that the reference model of time automata Internet of thing architecture established in this article can better reflect the nature of interaction with the physical world, heterogeneity and large-scale, dynamic, and incompleteness of the Internet of thing system.

Beschreibung: It is a potential threat to persons and companies to reveal private or company-sensitive data through the Internet of Things by the color image steganography. The existing rich model features for color image steganalysis fail to utilize the fact that the content-adaptive steganography changes the pixels in complex textured regions with higher possibility. Therefore, this article proposes a variant of spatial rich model feature based on the embedding change probabilities in differential channels. The proposed feature is extracted from the residuals in the differential channels to reduce the image content information and enhance the stego signals significantly. Then, the embedding change probability of each element in the differential channels is added to the corresponding co-occurrence matrix bin to emphasize the interference of the residuals in textured regions to the improved co-occurrence matrix feature. The experimental results show that the proposed feature can significantly improve the detection performances for the WOW and S-UNIWARD steganography, especially when the payload size is small. For example, when the payload size is 0.05 bpp, the detection errors can be reduced respectively by 5.20% and 4.90% for WOW and S-UNIWARD by concatenating the proposed feature to the color rich model feature CRMQ1.

Beschreibung: Aimed at the problem of the boundary defense easily being out of availability caused by the static network structure, a novel dynamic enterprise network defense system based on holographic transformation is designed and implemented. To increase the uncertainty of network structure and the position of target nodes, the network view is dynamically changed by constantly transforming endpoint information. Virtual endpoint mutation and hopping period selection are achieved by the hopping address generation module. It takes the advantages of anti-collision and good randomicity of the Chinese national SM3 hash algorithm. The full-cycle hopping period is managed and controlled by the hopping period management mechanism based on the ciphertext policy attribute-based broadcast holographic transformation. Experiments show that the system achieves expected security goals and has good interactivity and high stability.

Beschreibung: This article aims to provide an efficient fault diagnosis method for gearbox. A self-organizing map–based fault model is developed to provide effective diagnosis of the faults of gearboxes using the gear signals extracted from gearboxes operating with zero and three different types of faults. The gear signals are collected by vibration and acoustic sensors, and pre-denoised using wavelet denoising and wavelet packet decomposition. The characteristic values are subsequently obtained using fast Fourier transform and infinite impulse response filtering. The results showed of the self-organizing map neural network diagnosis model can effectively diagnose gear fault information with a 95% diagnostic accuracy using four input characteristic values: (1) Y-axis vibration displacement amplitude, (2) Y-axis vibration acceleration amplitude, (3) acoustic emission energy amplitude, and (4) acoustic emission signal peak value. The proposed approach provides a novel method to more accurate diagnosis of gear fault pattern and improvement of working efficiency of mechanical instruments.

Beschreibung: Mobile ad hoc networks are the “spontaneous networks” which create a temporary network in any place and any time without using any extra fixed radio device of a full infrastructure network. Each device in this network works as a router to develop end-to-end communication connections and move independently in any direction. Mostly, mobile ad hoc networks use the IEEE 802.11b protocol with carrier-sense multiple access with collision avoidance medium access control layer protocol for sharing a common medium among the nodes simultaneously. Due to this distributed medium, the routing and medium access control layer of the mobile ad hoc network are prone to attacks. Among several attackers, blackhole attacker is the dangerous one which causes the loss of all data packets of devices in the network. Efficient medium access control protocol designs in this respect play a key role in determining channel utilization, network delay, and, more importantly, network security. In the proposed work, preamble information is used with time-division multiple access medium access control. The preamble time-division multiple access uses time synchronization for each time slot and does not assign much time to the blackhole attacker due to a fixed time slot. As a result, blackhole is not stable in all communications and such an attack is effectively defended. Simulation results show that, in the presence of the blackhole attacker, carrier-sense multiple access with collision avoidance has a high packet loss ratio and low network throughput as compared to the proposed preamble time-division multiple access.

Beschreibung: The growing interest in precision livestock farming is prompted by a desire to understand the basic behavioural needs of the animals and optimize the contribution of each animal. The aim of this study was to develop a system that automatically generated individual animal behaviour and localization data in sheep. A sensor-fusion-system tracking individual sheep position and detecting sheep standing/lying behaviour was proposed. The mean error and standard deviation of sheep position performed by the ultra-wideband location system was 0.357 ± 0.254 m, and the sensitivity of the sheep standing and lying detection performed by infrared radiation cameras and three-dimenional computer vision technology were 98.16% and 100%, respectively. The proposed system was able to generate individual animal activity reports and the real-time detection was achieved. The system can increase the convenience for animal behaviour studies and monitoring of animal welfare in the production environment.

Beschreibung: In the era of 5G mobile communication, radio environment maps are increasingly viewed as a powerful weapon for the optimization of spectrum resources, especially in the field of autonomous vehicles. However, due to the constraint of limited resources when it comes to sensor networks, it is crucial to select a suitable scale of sensor measurements for radio environment map construction. This article proposes an adaptive ordinary Kriging algorithm based on affinity propagation clustering as a novel spatial interpolation method for the construction of the radio environment map, which can provide precise awareness of signal strength at locations where no measurements are available. Initially, a semivariogram is obtained from all the sensor measurements. Then, in order to select the minimum scale of measurements and at the same time guarantee accuracy, the affinity propagation clustering is introduced in the selection of sensors. Moreover, the sensor estimation groups are created based on the clustering result, and estimation results are obtained by ordinary Kriging. In the end, the simulation of the proposed algorithm is analyzed through comparisons with three conventional algorithms: inverse distance weighting, nearest neighbor, and ordinary Kriging. As a result, the conclusion can be drawn that the proposed algorithm is superior to others in accuracy as well as in efficiency.

Beschreibung: With the wide application of Internet of things technology and era of large data in agriculture, smart agricultural design based on Internet of things technology can efficiently realize the function of real-time data communication and information processing and improve the development of smart agriculture. In the process of analyzing and processing a large amount of planting and environmental data, how to extract effective information from these massive agricultural data, that is, how to analyze and mine the needs of these large amounts of data, is a pressing problem to be solved. According to the needs of agricultural owners, this article studies and optimizes the data storage, data processing, and data mining of large data generated in the agricultural production process, and it uses the k-means algorithm based on the maximum distance to study the data mining. The crop growth curve is simulated and compared with improved K-means algorithm and the original k-means algorithm in the experimental analysis. The experimental results show that the improved K-means clustering method has an average reduction of 0.23 s in total time and an average increase of 7.67% in the F metric value. The algorithm in this article can realize the functions of real-time data communication and information processing more efficiently, and has a significant role in promoting agricultural informatization and improving the level of agricultural modernization.

Beschreibung: The availability of massive amount of dam safety monitoring data can make it difficult to analyze and characterize dam behavior. This article describes the use of the Cloud model to transform quantitative monitoring data into qualitative information. Each monitoring point returning dam safety data is regarded as a cloud drop, and parameters such as the expectation, entropy, and hyper-entropy of the monitoring data are obtained through a backward cloud generator to represent the operational state of the dam. The monitoring points are then treated as vectors, and the cloud similarity is calculated using the cosine value of the angle between them. The cloud similarity coefficient is then determined to characterize the similarity of dam behavior. Experimental analysis shows that the process of identifying cloud parameters has a good effect on the discovery of abnormal monitoring values regarding dam safety and demonstrates the feasibility of characterizing the dam behavior. Clustering analysis is applied to the similarity coefficients to further achieve the hierarchical management of dam monitoring points.

Beschreibung: This article focuses on the evaluation of geometric dilution of precision for three-dimensional angle-of-arrival target localization in wireless sensor networks. We calculate a general analytical expression for the geometric dilution of precision for three-dimensional angle-of-arrival target localization. Unlike the existing works in the literature, in this article, no assumptions are made regarding the observation ranges, noise variances, or the number of sensors in the derivation of the geometric dilution of precision. Necessary and sufficient conditions regarding the existence of geometric dilution of precision are also derived, which can be readily used to evaluate the observability of three-dimensional angle-of-arrival target localization in wireless sensor networks. Moreover, a concise procedure is also presented to calculate the geometric dilution of precision when it exists. Finally, several examples are used to illustrate our results, and it is shown that the performance of the proposed regular deployment configurations of angle-of-arrival sensors is better than the one with random deployment patterns.

Beschreibung: Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

Beschreibung: The unmanned aerial vehicle features with high flexibility and easy deployment. It could be used as an air base station and provide fast communication services for the ground users. It plays an important role in some special occasions such as natural disasters, emergency communications and temporary large-scale activities. A single unmanned aerial vehicle equipped with base station has limited range of services, but a multiple unmanned aerial vehicle equipped with base station network can serve a wider range of users. The research goal of unmanned aerial vehicle equipped with base station network coverage control is to maximize the network coverage under the condition of maintaining the service quality. In view of the low dynamic coverage ratio of unmanned aerial vehicle equipped with base station network, this article proposes a relative distance–based unmanned aerial vehicle equipped with base station deployment method. The unmanned aerial vehicle realizes on-demand coverage and maintains a stable network topology under the influence of three relative distances by sensing the uncovered area of the ground, the neighbouring unmanned aerial vehicles and the location of the coverage boundary or obstacles. In addition, the algorithm is also adapted to a variety of scenarios. The simulation results show that the coverage of the proposed algorithm is 22.4% higher than that of random deployment, and it is 9.9%, 4.7% and 2.1% higher than similar virtual force-oriented node, circular binary segmentation and hybrid local virtual force algorithms.

Beschreibung: The sorting mechanism underlying the traditional evaluation grid method with attractive factors mainly represents the time or the paired comparison method. However, the current approach employed to identify abstract factors of attractiveness may not be comprehensive. The objective of this article is to propose a hybrid method with an integrated fuzzy Kano model and fuzzy importance–performance analysis to evaluate attractive factors. Fuzzy importance–performance analysis is a more accurate quantitative–qualitative method for two-dimensional analysis, and integrated fuzzy Kano model compensates for the low-resolution problem of the traditional Kano model. A combination of both models arrives at a more comprehensive and reliable evaluation grid method evaluation mechanism. The results indicate that the attractive factors sorted through integrated fuzzy Kano model–fuzzy importance–performance analysis have deconstructed abstract factors and feature factors of the customer service robot. Moreover, the key factors of the products sorted by through integrated fuzzy Kano model–fuzzy importance–performance analysis provide a better understanding of customer expectations associated with the products, which consequently enables developers and designers to accurately understand the design style and conceive new ideas.

Beschreibung: With the rapid development of intelligent perception and other data acquisition technologies in the Internet of things, large-scale scientific workflows have been widely used in geographically distributed multiple data centers to realize high performance in business model construction and computational processing. However, insider threats pose very significant privacy and security risks to systems. Traditional access-control models can no longer satisfy the reasonable authorization of resources in these new cross-domain environments. Therefore, a dynamic and semantic-aware access-control model is proposed for privacy preservation in multiple data center environments, which implements a semantic dynamic authorization strategy based on an anomaly assessment of users’ behavior sequences. The experimental results demonstrate that this dynamic and semantic-aware access-control model is highly dynamic and flexible and can improve the security of the application system.

Beschreibung: Technology evolves quickly. Low-cost and ready-to-connect devices are designed to provide new services and applications. Smart grids or smart health care systems are some examples of these applications. In this totally connected scenario, some security issues arise due to the large number of devices and communications. In this way, new solutions for monitoring and detecting security events are needed to address new challenges brought about by this scenario, among others, the real-time requirement allowing quick security event detection and, consequently, quick response to attacks. In this sense, Intrusion Detection Systems are widely used though their evaluation often relies on the use of predefined network datasets that limit their application in real environments. In this work, a real-time and ready-to-use tool for monitoring and detecting security events is introduced. The Multivariate Statistical Network Monitoring–Sensor is based on the Multivariate Statistical Network Monitoring methodology and provides an alternative way for evaluating Multivariate Statistical Network Monitoring–based Intrusion Detection System solutions. Experimental results based on the detection of well-known attacks in hierarchical network systems prove the suitability of this tool for complex scenarios, such as those found in smart cities or Internet of Things ecosystems.

Beschreibung: Partial discharges are the main insulation defects encountered in gas-insulated switchgears. When it occurs inside the gas-insulated switchgear cavity, it degrades insulation, and, sooner or later, causes a breakdown. Therefore, it is important to discover insulation defects as early as possible, locate the discharge, and perform both defect identification and maintenance. Current ultra high frequency-based partial discharge location methods mainly use time delay. To obtain accurate delay times, however, a very high sampling rate is needed, which requires expensive hardware and greatly limits its application. Therefore, in this article, a localization method based on received signal strength indicator ranging is proposed, and location estimation is carried out. An easily implementable particle swarm optimization algorithm with high positioning accuracy is selected to compensate for the low positioning accuracy of current received signal strength indicator ranging methods. To further improve positioning accuracy, the convergence conditions of the particle swarm optimization are investigated, and, considering their constraints, an improved particle swarm optimization algorithm is proposed. By combining the characteristics of ultra high frequency wireless sensor array positioning, the particle size is optimized. The simulation results show that the location accuracy using the ultra high frequency switchgear partial discharge location method based on received signal strength indicator ranging with the improved particle swarm optimization algorithm performs significantly better.

Beschreibung: Sparse decomposition has excellent adaptability and high flexibility in describing arbitrary complex signals based on redundant and over-complete dictionary, thus having the advantage of being free from the limitations of traditional signal processing methods such as wavelet and fast Fourier transform being imposed by orthogonal basis. Sparse decomposition provides an effective approach for feature extraction of intricate vibration signals collected from rotating machinery. Self-learning over-complete dictionary and pre-defined over-complete dictionary are the two dictionary construction modes of sparse decomposition. Normally, the former mode owns the virtues of much more adaptive and flexible than the latter one, and several kinds of classical self-learning over-complete dictionary methods have been arising in recent years. K-means singular value decomposition is a classical self-learning over-complete dictionary method and has been used in image processing, speech processing, and vibration signal processing. However, K-means singular value decomposition has relative low reconstruction accuracy and poor stability to enhance the desired features. To overcome the above-mentioned shortcomings of K-means singular value decomposition, a new K-means singular value decomposition sparse representation method based on traditional K-means singular value decomposition method was proposed in this article, which uses the sparse adaptive matching pursuit algorithm and an iterative method based on the minimum similarity of atomic structure. The effectiveness and advantage of the proposed method were verified through simulation and experiment.

Beschreibung: Nowadays, wireless sensor network technology is being increasingly popular which is applied to a wide range of Internet of Things. Especially, Power Internet of Things is an important and rapidly growing section in Internet of Thing systems, which benefited from the application of wireless sensor networks to achieve fine-grained information collection. Meanwhile, the privacy risk is gradually exposed, which is the widespread concern for electricity power consumers. Non-intrusive load monitoring, in particular, is a technique to recover state of appliances from only the energy consumption data, which enables adversary inferring the behavior privacy of residents. There can be no doubt that applying local differential privacy to achieve privacy preserving in the local setting is more trustworthy than centralized approach for electricity customers. Although it is hard to control the risk and achieve the trade-off between privacy and utility by traditional local differential privacy obfuscation mechanisms, some existing obfuscation mechanisms based on artificial intelligence, called advanced obfuscation mechanisms, can achieve it. However, the large computing resource consumption to train the machine learning model is not affordable for most Power Internet of Thing terminal. In this article, to solve this problem, IFed was proposed—a novel federated learning framework that let electric provider who normally is adequate in computing resources to help Power Internet of Thing users. First, the optimized framework was proposed in which the trade-off between local differential privacy, data utility, and resource consumption was incorporated. Concurrently, the following problem of privacy preserving on the machine learning model transport between electricity provider and customers was noted and resolved. Last, users were categorized based on different levels of privacy requirements, and stronger privacy guarantee was provided for sensitive users. The formal local differential privacy analysis and the experiments demonstrated that IFed can fulfill the privacy requirements for Power Internet of Thing users.

Beschreibung: Linear predictive coding is an extremely effective voice generation method that operates through simple process. However, linear predictive coding–generated voices have limited variations and exhibit excessive noise. To resolve these problems, this article proposes an artificial intelligence model that combines a denoise autoencoder with generative adversarial networks. This model generates voices with similar semantics through the random input from the latent space of generator. The experimental results indicate that voices generated exclusively by generative adversarial networks exhibit excessive noise. To solve this problem, a denoise autoencoder was connected to the generator for denoising. The experimental results prove the feasibility of the proposed voice generation method. In the future, this method can be applied in robots and voice generation applications to increase the humanistic language expression ability of robots and enable robots to demonstrate more humanistic and natural speaking performance.

Beschreibung: With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account correlations between anomalies, most disregard the fact that the anomaly status of sensor data changes over time. In this article, we propose an unsupervised contextual anomaly detection method in Internet of Things through wireless sensor networks. This method accounts for both a dynamic anomaly status and correlations between anomalies based contextually on their spatial and temporal neighbors. We then demonstrate the effectiveness of the proposed method in an anomaly detection model. The experimental results show that this method can accurately and efficiently detect not only individual anomalies but also anomalous events.

Beschreibung: Social Internet of things is one of the most up-to-date research issues in the applications of Internet of things technologies. In social Internet of things, accuracy and reliability are standard features to discerning decisions. We assume that decision support systems based on social Internet of things could leverage research from recommender systems to achieve more stable performance. Therefore, we propose a trust-aware recommender systems suitable for social Internet of things. Trust-aware recommender systems adapt the concept of social networking service and utilize social interaction information. Trust information not only improves recommender systems from opinion spam problems but also more accurately predicts users’ preferences. We confirm that the performance of a recommender system becomes more improved when implicit trust is able to satisfy the properties of trust in the social Internet of things environment. The structure and amount of social link information are context-sensitive, so applying the concept of trust into social Internet of things environments requires a method to optimize implicit and explicit trust with minimal social link information. Our proposed method configures an asymmetric implicit trust network utilizing user–item rating matrix and transforms trust propagation metrics for a directional and weighted trust network. Through experiments, we confirm that the proposed methods enable higher accuracy and wider coverage compared to the existing recommendation methods.

Beschreibung: With the development of modern communication, available spectrum resources are becoming increasingly scarce, which reduce network throughput. Moreover, the mobility of nodes results in the changes of network topological structure. Hence, a considerable amount of control information is consumed, which causes a corresponding increase in network power consumption and exerts a substantial impact on network lifetime. To solve the real-time transmission problem in large-scale wireless mobile sensor networks, opportunistic spectrum access is applied to adjust the transmission power of sensor nodes and the transmission velocity of data. A cognitive routing and optimization protocol based on multiple channels with a cross-layer design is proposed to study joint optimal cognitive routing with maximizing network throughput and network lifetime. Experimental results show that the cognitive routing and optimization protocol based on multiple channels achieves low computational complexity, which maximizes network throughput and network lifetime. This protocol can be also effectively applied to large-scale wireless mobile sensor networks.

Beschreibung: We design a greedy routing scheme specifically for GPS-free large-scale wireless sensor networks deployed on surfaces of complex-connected three-dimensional settings. Compared with other greedy embedding–based surface network routing scheme, the proposed one is cut free such that no pair of nodes suffers a long detour to reach each other. The routing scheme is designed to be resilient to node or link failures especially under random node or link failure model where each node in a network has an equal and independent probability of failure during some time interval. The proposed algorithm is fully distributed and scalable to both the size and the topological complexity of a network. Each sensor node requires only limited and constant storage. Simulation results show the proposed routing scheme with a higher successful delivery ratio, a lower average stretch factor, and a lower normalized communication cost compared with other resilient routing methods.

Beschreibung: In order to improve the control ability of the human body in the process of physical training, it is necessary to carry out the inverse simulation analysis of the physical training process and establish the process control model of the physical training. The complex problem of high-dimensional spatial motion planning involved in physical training is decomposed into a series of sub-problems in low-dimensional space, and the inertial attitude parameter fusion is carried out according to the position and pose state of the human body in the end of the workspace during the process of physical training. The design of sensor node and base station in the system can realize real-time collection of motion parameters of motion collectors. The multi-dimensional control of physical training process is carried out by fuzzy constraint and inverse integral control, and the attitude parameters of human body are adjusted by means of mechanical analysis model and inertial parameter analysis method. The simulation results show that the inversion simulation control has better convergence, higher control quality, and better inverse simulation performance in the process of physical training, which can effectively guide physical training and improve the effect of physical training.

Beschreibung: The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.

Beschreibung: The mobile crowdsourcing technology has been widely researched and applied with the wide popularity of smartphones in recent years. In the applications, the smartphone and its user act as a whole, which called as the composite node in this article. Since smartphone is usually under the operation of its user, the user’s participation cannot be excluded out the applications. But there are a few works noticed that humans and their smartphones depend on each other. In this article, we first present the relation between the smartphone and its user as the conditional decision and sensing. Under this relation, the composite node performs the sensing decision of the smartphone which based on its user’s decision. Then, this article studies the performance of the composite sensing process under the scenario which composes of an application server, some objects, and users. In the progress of the composite sensing, users report their sensing results to the server. Then, the server returns rewards to some users to maximize the overall reward. Under this scenario, this article maps the composite sensing process as the partially observable Markov decision process, and designs a composite sensing solution for the process to maximize the overall reward. The solution includes optimal and myopic policies. Besides, we provide necessary theoretical analysis, which ensures the optimality of the optimal algorithm. In the end, we conduct some experiments to evaluate the performance of our two policies in terms of the average quality, the sensing ratio, the success report ratio, and the approximate ratio. In addition, the delay and the progress proportion of optimal policy are analyzed. In all, the experiments show that both policies we provide are obviously superior to the random policy.

Beschreibung: Proxy signature is a very useful technique which allows the original signer to delegate the signing capability to a proxy signer to perform the signing operation. It finds wide applications especially in the distributed environment where the entities such as the wireless sensors are short of computational power and needed to be convinced to the authenticity of the server. Due to less proxy signature schemes in the post-quantum cryptography aspect, in this article, we investigate the proxy signature in the post-quantum setting so that it can resist against the potential attacks from the quantum adversaries. A general multivariate public key cryptographic proxy scheme based on a multivariate public key cryptographic signature scheme is proposed, and a heuristic security proof is given for our general construction. We show that the construction can reach Existential Unforgeability under an Adaptive Chosen Message Attack with Proxy Key Exposure assuming that the underlying signature is Existential Unforgeability under an Adaptive Chosen Message Attack. We then use our general scheme to construct practical proxy signature schemes for three well-known and promising multivariate public key cryptographic signature schemes. We implement our schemes and compare with several previous constructions to show our efficiency advantage, which further indicates the potential application prospect in the distributed network environment.

Beschreibung: In designing a real-time MAC protocol for wireless sensor networks, it is desirable to shorten the data acquisition cycle time to satisfy more applications with a tighter time constraint in data collection. To achieve this, one often tries to improve throughput by employing a slot reuse technique. Meanwhile, the slotted sense multiple access protocol that uses a sharable slot was proven to be advantageous for timely and reliable data delivery. However, since the nodes at lower tree levels process more packets or bigger packets with data aggregation, they need a bigger sharable slot. Thus, it is not easy to employ a slot reuse technique due to the size variation of sharable slots. To tackle this problem, we try to reduce the size of an aggregated packet by limiting the number of packets for aggregation using two channels. If every node is allowed to send a single packet during data acquisition cycle time, an equal-sized sharable slot can be allocated to every tree level. It is shown that the proposed approach can reduce data acquisition cycle time significantly while maintaining good reliability in data transmission compared with slotted sense multiple access and also reduce energy consumption greatly compared with another multi-channel MAC protocol.

Beschreibung: With the widespread use of fog-to-cloud computing–based Internet of things devices, how to ensure the integrity of the data uploaded to the cloud has become one of the most important security issues. This article proposes an efficient and secure data auditing scheme based on fog-to-cloud computing for Internet of things scenarios, which can better meet performance and security requirements. The proposed scheme realizes data sharing under the condition of protecting privacy by encrypting sensitive information. Using the private key separation method, the private key is divided into two parts using identity information generation and random selection which are, respectively, held by the user and the fog center. Then, using the two-time signature method, the Internet of things and fog computing center use two parts of the private key to generate the original signature and final signature in two separate times. Since the fog computing center only has a part of the private key generated using the identity information, the security of the system will not be damaged due to the leakage of part of the private key held by the fog center, and the fog center significantly participates in the signature generation process, which significantly reduces the computation and communication overhead of the Internet of things device. Security analysis and performance evaluation show that the proposed scheme is safe and efficient.

Beschreibung: The automatic detection of falls within environments where sensors are deployed has attracted considerable research interest due to the prevalence and impact of falling people, especially the elderly. In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. Second, we include fuzzy representation of thermal information. Third, we enable the generation of a large data set from a set of few images using ad hoc data augmentation, which increases the original data set size, generating new synthetic images. Fourth, we define three types of convolutional neural networks which are adapted for each thermal vision sensor in order to evaluate the impact of the architecture on fall detection performance. The results show encouraging performance in single-occupancy contexts. In multiple occupancy, the low-resolution thermal vision sensor with a wide viewing angle obtains better performance and reduction of learning time, in comparison with the high-resolution thermal vision sensors with a central viewing angle.

Beschreibung: In recent years, Internet of things (IoT) devices are playing an important role in business, education, medical as well as in other fields. Devices connected to the Internet is much more than the number of world population. However, it may face all kinds of attacks from the Internet easily for its accessibility. As we all know, most attacks against IoT devices are based on Web applications. So protecting the security of Web services can effectively improve the situation of IoT ecosystem. Conventional Web attack detection methods highly rely on samples, and artificial intelligence detection results are uninterpretable. Hence, this article introduced a supervised detection algorithm based on benign samples. Seq2Seq algorithm is been chosen and applied to detect malicious web requests. Meanwhile, the attention mechanism is introduced to label the attack payload and highlight labeling abnormal characters. The results of experiments show that on the premise of training a benign sample, the precision of proposed model is 97.02%, and the recall is 97.60%. It explains that the model can detect Web attack requests effectively. Simultaneously, the model can label attack payload visually and make the model “interpretable.”

Beschreibung: Classification of imbalanced data is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies.

Beschreibung: In this article, we consider a directional data forwarding problem to multiple destinations under distinct deadline constraints in vehicular ad hoc networks. We present a simple yet effective data forwarding algorithm based on only vehicle-to-vehicle communications in infrastructure-less and map-less environments. Our algorithm consists of two phases: relay selection and proliferation. We design a relay selection algorithm that encourages a shared ride for data delivery toward a certain common intermediate point from time to time for forwarding efficiency. It chooses a strong next relay candidate among nearby connected vehicles by considering their current position, velocity, and also the current progress toward the destination. In case that one of the progress lagging indicators becomes signaled, the number of vehicle relays increases under control depending on the degree of deterioration during a packet replication procedure called proliferation. Embedding two essential parts in designing a timely data forwarding scheme validates its accurate on-time data delivery performance and forwarding efficiency in network overhead based on real-world data-driven experiments.

Beschreibung: In the research of industrial robot design, designing using only the perceptual thinking and creativity of an industrial designer or overemphasizing the intervention of quantitative data research in the field of emotional cognition is relatively one sided. In this article, research on how to combine the above two aspects effectively will be conducted. The aim is to present a design method which provides artistic creativity and scientific support for industrial robot design. Therefore, a method for representing perceptual image spaces of industrial robots through pictures and semantics by evaluating the perceptual images and using statistical approaches such as factor analysis will be proposed. Perceptual design elements of industrial robots are decomposed from the perspective of style and color. After the quantitative type I analysis, the numerical relationships between the semantics of images and design elements are identified. Also, a method for mapping relationships between the perceptual image spaces and design elements of industrial robots is developed. After three-dimensional modeling and simulation, the semantic difference methods are used in combination with the emotional evaluation and measurement methods for physiological experiments such as eye tracking, skin conductance, heart rate, and electroencephalography experiments with the aid of virtual reality. Finally, a perceptual design method is extracted for smart industrial robots based on virtual reality and synchronous quantitative physiological signals.

Beschreibung: Users are each day more aware of their privacy and data protection. Although this problem is transversal to every digital service, it is especially relevant when critical and personal information is managed, as in eHealth and well-being services. During the last years, many different innovative services in this area have been proposed. However, data management challenges are still in need of a solution. In general, data are directly sent to services but no trustworthy instruments to recover these data or remove them from services are available. In this scheme, services become the users’ data owners although users keep the rights to access, modify, and be forgotten. Nevertheless, the adequate implementation of these rights is not guaranteed, as services use the received data with commercial purposes. In order to address and solve this situation, we propose a new trustworthy personal data protection mechanism for well-being services, based on privacy-by-design technologies. This new mechanism is based on Blockchain networks and indirection functions and tokens. Blockchain networks execute transparent smart contracts, where users’ rights are codified, and store the users’ personal data which are never sent or given to external services. Besides, permissions and privacy restrictions designed by users to be applied to their data and services consuming them are also implemented in these smart contracts. Finally, an experimental validation is also described to evaluate the Quality of Experience (in terms of user satisfaction) and Quality of Service (in terms of processing delay) compared to traditional service provision solutions.

Beschreibung: In this article, versatile video coding, the next-generation video coding standard, is combined with a deep convolutional neural network to achieve state-of-the-art image compression efficiency. The proposed hierarchical grouped residual dense network exhaustively exploits hierarchical features in each architectural level to maximize the image quality enhancement capability. The basic building block employed for hierarchical grouped residual dense network is residual dense block which exploits hierarchical features from internal convolutional layers. Residual dense blocks are then combined into a grouped residual dense block exploiting hierarchical features from residual dense blocks. Finally, grouped residual dense blocks are connected to comprise a hierarchical grouped residual dense block so that hierarchical features from grouped residual dense blocks can also be exploited for quality enhancement of versatile video coding intra-coded images. Various non-architectural and architectural aspects affecting the training efficiency and performance of hierarchical grouped residual dense network are explored. The proposed hierarchical grouped residual dense network respectively obtained 10.72% and 14.3% of Bjøntegaard-delta-rate gains against versatile video coding in the experiments conducted on two public image datasets with different characteristics to verify the image compression efficiency.

Beschreibung: Irregularities in microphone distribution enrich the diversity of spatial differences to decorrelate interferences from the beamforming target. However, the large degrees of freedom of irregular placements make it difficult to analyse and optimize array performance. This article proposes fast and feasible optimal irregular array design methods with improved beamforming performance for human speech. Important geometric features are extracted to be used as the input vector of the neural network structure to determine the optimal irregular arrangements of sensors. In addition, a hyperbola design method is proposed to directly cluster microphones in the hyperbola areas to produce rich differential distance entropies and yield significant signal-to-noise ratio improvements. These methods can be easily applied to guide non-computer-aided optimal irregular array designs for human speech in acoustic scenes such as immersive cocktail party environments.

Beschreibung: Due to the special characteristics of highway tunnels and vehicles, the interior of the tunnel is required to provide appropriate lighting to ensure the safety of driving vehicles, especially at the entrance section of the tunnel. At present, most of the tunnel entrance lighting control system only considers one single factor, the brightness outside the tunnel. However, in practice, the required lighting brightness in the tunnel is also related to traffic flow, speed, and other factors. Comprehensively utilizing these factors to improve the control strategy is urgently needed. To deal with this problem, this article has designed a multi-source information acquisition system for tunnel lighting based on the Internet of things technology, which combined with fuzzy control theory in order to develop an intelligent control system for LED lighting at the entrance section of the tunnel. The designed system was implemented and long-term tested in a real highway tunnel. The experimental results have shown that the system designed in this article can automatically control the brightness of the lighting inside the tunnel according to the real-time measurements of the brightness outside the tunnel, traffic flow, speed, and so on. Furthermore, the utilizations of the system can minimize the human and power consumption of tunnel lighting while ensuring the safety of tunnel traffic.

Beschreibung: In relational databases, embedding watermarks in integer data using traditional histogram shifting method has the problem of large data distortion. To solve this problem, a reversible database watermarking method without redundant shifting distortion is proposed, taking advantage of a large number of gaps in the integer histogram. This method embeds the watermark bit by bit on the basis of grouping. First, an integer data histogram is constructed with the absolute value of the prediction error of the data as a variable. Second, the positional relationship between each column and the gap in the histogram is analyzed to find out all the columns adjacent to the gap. Third, the highest column is selected as the embedded point. Finally, a watermark bit is embedded on the group by the histogram non-redundant shifting method. Experimental results show that compared with existing reversible database watermarking methods, such as genetic algorithm and histogram shift watermarking and histogram gap–based watermarking, the proposed method has no data distortion caused by the shifting redundant histogram columns after embedding watermarks on forest cover type data set and effectively reduces the data distortion rate after embedding watermarks.

Beschreibung: Since the working situation of photovoltaic modules cannot be tracked in the real time and defective components cannot be located and controlled specifically, a remote monitoring system of photovoltaic modules based on wireless sensor networks is designed to improve the management efficiency of photovoltaic power plants. The monitoring data (current, voltage, and illumination, etc.) of photovoltaic modules are collected by the wireless sensor network nodes in real time, and then, there are orderly transmitted to the coordinator and server by the ZigBee communication protocol with the time synchronization algorithm. Time synchronization algorithm based on Gaussian delay model performs best in synchronization accuracy and energy-efficient reference broadcast synchronization algorithm is the most advantageous in synchronization energy consumption. Adaptive energy–efficient reference broadcasting synchronization algorithm is proposed with a balance between energy consumption and accuracy, which is applicable to large networks and thus conducive to the application of wireless sensor network in large-scale photovoltaic module monitoring. With the analysis and processing of the received data, the situation of the photovoltaic modules can be accordingly judged and thus the information management of photovoltaic power plant can be realized. For a photovoltaic power plant with a total power output of 100 kW and with 400 photovoltaic modules, experimental results show that the overall efficiency of the photovoltaic power plant can be increased by a wide margin.