ALBERT

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Description: The Industrial Internet of Things is growing fast. But the rapid growth of IIoT devices raises a number of security concerns, because the IIoT device is weak in defending against malware, and the method of managing a large number of IIoT devices is awkward and inconvenient. This article proposes a multi-level DDoS mitigation framework (MLDMF) to defend against DDoS attacks for IIoT, which includes the edge computing level, fog computing level, and cloud computing level. Software defined networking is used to manage a large number of IIoT devices and to mitigate DDoS attacks in IIoT. Experimental results show the effectiveness of the proposed framework.

Description: The efficient integration of optical switching in data center networks is being studied as a means to cope with surging traffic demands. Optically switched, flatter network architectures more efficiently handle the east-west traffic profiles of modern data centers. Limitations in the port count and reconfiguration speed of optical switches require novel network designs offering network scalability and dynamicity. Interaction of the optically switched data plane with a software-defined control and orchestration framework, meeting current common practices in data centers, necessitates the design of custom network control algorithms and software modules as well as the integration of novel functionalities. The approach of the European project NEPHELE is presented, offering an endto- end solution that addresses the optical data plane, the control plane, and its interaction with the application layer.

Description: The comprehensive evolution of information communication technologies on mobile sensing objects has led to the provision of versatile ubiquitous network services embedded with specific- purpose modern sensors and intelligent wearable devices. The universal Internet connectivity of such smart objects has brought about a new era of ubiquitous application development for the Internet of Things. Meanwhile, security has become critically important. In the past decade, academia and industry have dedicated great efforts to the design of continuous authentication for multi-modal networks. Multiform authentication bio-tokens have been introduced for continuous entity identification and verification. With the rapid growth and universality of wearable devices, in this article we target continuous authentication for the IoT-based environment with users possessing wearable healthcare (and wellness) related smart objects. To present the state of the art, we provide a comprehensive review of continuous authentication in recent years. Critical characteristics of new biometrics are then introduced. Second, we present a wearable plantar bio-feature extractor constructed via commercial pressure sensors and the Raspberry PI platform. The prototype is adopted to retrieve user plantar bio-data as the raw (and training) data in the proposed authentication system. Third, we apply machinelearning- based techniques to derive a user's plantar bio-features as authentication tokens in the system to support continual (and real-time) entity verification in the background without the user's notice.

Description: In IoT-based healthcare, medical devices are more vulnerable to numerous security threats and attacks than other network devices. Current solutions are able to provide protection to patients' data during data transmission to some extent, but cannot prevent some sophisticated threats and attacks such as collusion attacks and data leakage. In this article, we first investigate the challenges with privacy protected data collection. Then we propose a practical framework called PrivacyProtector, patient privacy protected data collection, with the objective of preventing these types of attacks. PrivacyProtector includes the ideas of secret sharing and share repairing (in case of data loss or compromise) for patients' data privacy. Since it is the first time, we apply the Slepian- Wolf-coding-based secret sharing (SW-SSS) in PrivacyProtector. In the framework, we use a distributed database consisting of multiple cloud servers, which ensures that the privacy of patients' personal data can remain protected as long as one of the servers remains uncompromised. We also present a patient access control scheme in which multiple cloud servers collaborate in shared construction to offer patients' data to healthcare providers without revealing the content of the data. The privacy performance analysis has shown that the PrivacyProtector framework is secure and privacy-protected against various attacks.

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Description: Widespread use of white light-emitting diodes and ubiquitous smart devices offer the opportunity to establish VLC, which has become a hot research topic based on the growing number of publications over the last decade. Camera-based VLC, namely OCC, provides many unique features when compared to a single-photodiode-based system, such as the ability to separate incident light in the spatial and color domains. OCC technology represents a promising approach to utilize the benefits of VLC in beyond-5G scenarios and is one of the key technologies of the Internet of Things. Establishing a long communication channel in OCC, as well as non-flickering illumination by using low-frame-rate camera detectors, requires special modulation schemes. This article provides an overview of the principles of three categories of modulation schemes for OCC systems using a low-frame-rate camera detector. In addition, a series of undersampled modulation schemes are proposed and discussed to achieve flicker-free OCC with higher spectral efficiency. In addition, framing structures are designed to solve problems occurring in OCC systems using particular modulation schemes. To evaluate the performance of these modulation schemes, measured bit error rate values are shown. Finally, challenges in the implementation of OCC systems are also outlined.

Description: This tutorial article presents an outline of image sensor communication (ISC) technologies realized by light sources and cameras. It discusses the revision of the IEEE 802.15.7-2011 standard, namely, the IEEE 802.15.7m Optical Wireless Communications Task Group, which has significantly influenced the development of the ISC technology. It also reviews the ISC technical proposals of the task group and compares them with related works. The essential technical considerations of the ISC specifications are presented, and the future directions of research and development are discussed.

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Description: The emergence of the Industrial Internet of Things (IIoT) has paved the way to real-time big data storage, access, and processing in the cloud environment. In IIoT, the big data generated by various devices such as-smartphones, wireless body sensors, and smart meters will be on the order of zettabytes in the near future. Hence, relaying this huge amount of data to the remote cloud platform for further processing can lead to severe network congestion. This in turn will result in latency issues which affect the overall QoS for various applications in IIoT. To cope with these challenges, a recent paradigm shift in computing, popularly known as edge computing, has emerged. Edge computing can be viewed as a complement to cloud computing rather than as a competition. The cooperation and interplay among cloud and edge devices can help to reduce energy consumption in addition to maintaining the QoS for various applications in the IIoT environment. However, a large number of migrations among edge devices and cloud servers leads to congestion in the underlying networks. Hence, to handle this problem, SDN, a recent programmable and scalable network paradigm, has emerged as a viable solution. Keeping focus on all the aforementioned issues, in this article, an SDN-based edge-cloud interplay is presented to handle streaming big data in IIoT environment, wherein SDN provides an efficient middleware support. In the proposed solution, a multi-objective evolutionary algorithm using Tchebycheff decomposition for flow scheduling and routing in SDN is presented. The proposed scheme is evaluated with respect to two optimization objectives, that is, the trade-off between energy efficiency and latency, and the trade-off between energy efficiency and bandwidth. The results obtained prove the effectiveness of the proposed flow scheduling scheme in the IIoT environment.

Description: Current forecasts predict that the Industrial Internet of Things will account for about 10 billion devices by 2020. Simultaneously, unlicensed low-power wide area networks are gaining momentum due to their low cost, low power, and long range characteristics, which are suitable for many IIoT applications, in addition to the usage of unlicensed bands. In this article, a solution is proposed to seamlessly integrate LoRaWAN, an open and standardized LPWAN technology, with 4G/5G mobile networks, thus allowing mobile network operators to reutilize their current infrastructures. This proposal is transparent to LoRaWAN end devices and to the EPC, since only the LoRaWAN gateway needs to be modified. The gateway acts as an evolved Node B from the core network perspective, implementing part of the eNB protocol stack. All data packets transported over the core network are both encrypted and integrity protected, hence achieving end-toend security. As a proof of concept, this solution has been implemented and validated with an open source EPC.

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Description: The development of smart cities and their fast-paced deployment is resulting in the generation of large quantities of data at unprecedented rates. Unfortunately, most of the generated data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards that benefit from the availability of such data. Moreover, the highly dynamic nature of smart cities calls for a new generation of machine learning approaches that are flexible and adaptable to cope with the dynamicity of data to perform analytics and learn from real-time data. In this article, we shed light on the challenge of underutilizing the big data generated by smart cities from a machine learning perspective. In particular, we present the phenomenon of wasting unlabeled data. We argue that semi-supervision is a must for smart cities to address this challenge. We also propose a three-level learning framework for smart cities that matches the hierarchical nature of big data generated by smart cities with a goal of providing different levels of knowledge abstraction. The proposed framework is scalable to meet the needs of smart city services. Fundamentally, the framework benefits from semi-supervised deep reinforcement learning where a small amount of data that has users' feedback serves as labeled data, while a larger amount without such users' feedback serves as unlabeled data. The framework utilizes a mix of labeled and unlabeled data to converge toward better control policies instead of wasting the unlabeled data. This article also explores how deep reinforcement learning and its shift toward semi-supervision can handle the cognitive side of smart city services and improve their performance by providing several use cases spanning the different domains of smart cities. We also highlight several challenges as well as promising future research directions for incorporating machine learning and high-level intelligence into smart city services.

Description: In recent years, the smart city concept, which involves multiple disciplines, for example, smart healthcare, smart transportation, and smart community, has become popular because of its ability to improve urban citizens' quality of life. However, most services in these areas of smart cities have become data-driven, thus generating big data that require seamless real-time access, sharing, storing, processing, and analysis anywhere at any time for intelligent decision making to improve living standards. In this scenario, MCC can play a vital role by allowing a mobile device to access and offload big-data-related tasks to powerful cloudlet servers attached to many wireless APs, thus ensuring that the QoS demands of end users are met. However, the connectivity of mobile devices with a given AP is not continuous, but rather sporadic with varying signal strengths. Furthermore, the heterogeneity of the cloudlet resources and the big data application requests place additional challenges in making optimal code execution decision. To cope with this problem, this article proposes a mobility- aware optimal resource allocation architecture, namely Mobi-Het, for remote big data task execution in MCC that offers higher efficiency in timeliness and reliability. The system architecture and key components of the proposed resource allocation service are presented and evaluated. The results of experiments and simulations have demonstrated the effectiveness and efficiency of the proposed Mobi-Het architecture for mobile big data applications.

Description: In order to increase the security of edge computing, all data transmitted to and from edge devices, as well as all data stored on edge devices, must be encrypted. Especially when the transmitted or stored data contains sensitive personal information, long-term protection over periods of ten or more years may be required, which can only be achieved with post-quantum cryptography. This article first gives a brief overview of post-quantum public-key cryptosystems based on hard mathematical problems related to hash functions, error-correcting codes, multivariate quadratic systems, and lattices. Then the suitability of lattice-based cryptosystems for resource-constrained devices is discussed and efficient implementations for 8 and 32-bit microcontrollers are outlined.

Description: The increase in the number and diversity of smart objects has raised substantial cybersecurity challenges due to the recent exponential rise in the occurrence and sophistication of attacks. Although cloud computing has transformed the world of business in a dramatic way, its centralization hammers the application of distributed services such as security mechanisms for IoT applications. The new and emerging IoT applications require novel cybersecurity controls, models, and decisions distributed at the edge of the network. Despite the success of the existing cryptographic solutions in the traditional Internet, factors such as system development flaws, increased attack surfaces, and hacking skills have proven the inevitability of detection mechanisms. The traditional approaches such as classical machine-learning-based attack detection mechanisms have been successful in the last decades, but it has already been proven that they have low accuracy and less scalability for cyber-attack detection in massively distributed nodes such as IoT. The proliferation of deep learning and hardware technology advancement could pave a way to detecting the current level of sophistication of cyber-attacks in edge networks. The application of deep networks has already been successful in big data areas, and this indicates that fog-tothings computing can be the ultimate beneficiary of the approach for attack detection because a massive amount of data produced by IoT devices enable deep models to learn better than shallow algorithms. In this article, we propose a novel distributed deep learning scheme of cyber-attack detection in fog-to-things computing. Our experiments show that deep models are superior to shallow models in detection accuracy, false alarm rate, and scalability.

Description: The SK Telecom Company of South Korea recently introduced the concept of IoST to its business model. The company deployed IoST, which constantly generates data via the LoRa wireless platform. The increase in data rates generated by IoST is escalating exponentially. After attempting to analyze and store the massive volume of IoST data using existing tools and technologies, the South Korean company realized the shortcomings immediately. The current article addresses some of the issues and presents a big data analytics architecture for its IoST. A system developed using the proposed architecture will be able to analyze and store IoST data efficiently while enabling better decisions. The proposed architecture is composed of four layers, namely the small things layer, infrastructure layer, platform layer, and application layer. Finally, a detailed analysis of a big data implementation of the IoST used to track humidity and temperature via Hadoop is presented as a proof of concept.

Description: The use of fiber optic cable in support of bandwidth expansion has become de rigeur. Not only does optical fiber offer tremendous bandwidth, it is also low in attenuation, small in cross section, inexpensive, and impervious to electromagnetic interference, and there are significant economies of scale on components developed for the myriad of optical communications markets (long haul, fiber to the home, metro networks, etc.). Shorthaul requirements continue to grow and are expected to exceed the capacity of simple single mode fiber transmission. Spatial multiplexing is a new technique in fiber communications allowing fiber capacity to grow in another dimension by carrying signals on orthogonal modes of light. The short length and high capacity requirements for data center and fronthaul links make them well suited for exploitation of orbital angular momentum (OAM) of light in spatial multiplexing. We report experiments using commercial transceivers for OOK at 10 Gb/s on three channels (30 Gb/s total on fundamental and two OAM modes), and 10 Gb/s OOK on fundamental and DP-QPSK at 100 Gb/s on two OAM modes. OOK transmission was error free (〈 10 -12 ), while DP-QPSK had error below 10 -7 .

Description: Fog computing has emerged as an attractive solution to the distributed application of the Internet of Things, and could provide low-latency, highly mobile, and geo-distributed services distinguished from the cloud. While fog computing offers numerous benefits, it also faces various security challenges, of which access control is one of the fundamental requirements since it is concerned with authorizing users to access resources in the open fog environment. This article provides a comprehensive survey of access control of users' data in the environment of fog computing with the aim of highlighting security problems and challenges. It discusses the definition, architecture, and characteristics of fog computing, based on which typical requirements and essential models of access control are addressed. Finally, it highlights known access control schemes in the environment of fog computing, and identifies existing unresolved problems as future directions.

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Description: Recently, the Industrial Internet of Things (IIoT) is attracting growing attention from both academia and industry. Meanwhile, trust-based communication is widely utilized in various systems. In this article, studying the performance of IIoT, we investigate trust-based communication for IIoT. In particular, devoting attention to sensor- cloud, which is a paradigm of IIoT, we propose three types of trust-based communication mechanisms for sensor-cloud. Furthermore, with numerical results, we show that trust-based communication can greatly enhance the performance of sensor-cloud. Eventually, open research issues with respect to trust-based communication for sensor-cloud are discussed.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Intensive video and cloud computing services are putting much pressure on metro networks to meet stringent requirements such as low latency, low power consumption, and high spectral efficiency, where the system cost is sensitive. To address this challenge, we introduce a special elastic optical network paradigm called the ultradense wavelength switched network (UD-WSN) for metro optical networks. The architecture supports a spectrum granularity (e.g., 6.25 GHz or even 5 GHz) finer than the current smallest standardized 12.5 GHz, which enables more efficient spectrum utilization when provisioning metro lowspeed service connections (e.g., sub-1G/1G/10G services). The performance of UD-WSN is evaluated from the techno-economic perspective in comparison with the conventional OTN over DWDM network. Case studies demonstrate the merits of the proposed architecture. Considering the promising potential of UD-WSN, we also suggest several open research issues for it.

Description: In the modern era of RF spectrum crunch, VLC is a recent and promising alternative technology that operates at the visible light spectrum. Thanks to its unlicensed and large bandwidth, VLC can deliver high throughput, better energy efficiency, and low-cost data communications. In this article, a hybrid RF/VLC architecture is considered that can simultaneously provide lighting and communication coverage across a room. The considered architecture involves a novel multi-element hemispherical bulb design, which can transmit multiple data streams over LED modules. Simulations considering various VLC transmitter configurations and topologies show that good link quality and high spatial reuse can be maintained in typical indoor communication scenarios.

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Description: The cyber-physical production system (CPPS), which combines information communication technology, cyberspace virtual technology, and intelligent equipment technology, is accelerating the path of Industry 4.0 to transform manufacturing from traditional to intelligent. The Industrial Internet of Things integrates the key technologies of industrial communication, computing, and control, and is providing a new way for a wide range of manufacturing resources to optimize management and dynamic scheduling. In this article, OLE for process control technology, software defined industrial network, and device-to-device communication technology are proposed to achieve efficient dynamic resource interaction. Additionally, the integration of ontology modeling with multiagent technology is introduced to achieve dynamic management of resources. We propose a load balancing mechanism based on Jena reasoning and Contract-Net Protocol technology that focuses on intelligent equipment in the smart factory. Dynamic resources management for IoT-based manufacturing provides a solution for complex resource allocation problems in current manufacturing scenarios, and provides a technical reference point for the implementation of intelligent manufacturing in Industry 4.0.

Description: In a smart factory environment, a much larger amount of data is transmitted in the workshop networks bringing big challenges to data transfer capability and energy usage efficiency. In the workshop, two main networks (i.e., wired/wireless fieldbus networks and wireless sensor networks) are usually used to collect and transmit data separately; thus, this work proposes to integrate these two networks by also taking advantage of the existing mobile intelligences in smart factories, such as automatic guided vehicles, to implement a novel data and materials delivery scheme well suited for modern industrial wireless sensor networks (IWSNs). In particular, data collected by sensor nodes can be transferred to nearby fieldbus nodes first; then all data in fieldbus nodes can be handled according to different priorities. High-priority data are still transferred to the central base station through fieldbus while low-priority data are delivered through mobile intelligence by solving a materials and data delivery problem. Simulation experiments demonstrate how the proposed approach, running within the IWSN, significantly increases data delivery efficiency, also achieving better energy usage by five times compared to separate networks without any mobile intelligence support.

Description: Today's existing smart city research involves many overtly futuristic applications such as smart transportation, in which smart roads warn drivers of bad traffic conditions ahead, smart parking, which communicates the location of unoccupied parking spaces to drivers, and smart environment, which enables fully automated homes and workplaces to adjust their temperature to conserve energy. The realization of these applications hinges on a data acquisition structure that gathers its data from a countless number of sensors, either deployed for predefined tasks (hard sensing) or built into the mobile devices of smart city residents (soft sensing). At the core of this big data infrastructure lie the 5Vs: veracity, volume, velocity, variety, and value. The soft sensing component of a smart city sensing network is particularly affected by 3Vs: veracity, volume, and velocity. To address the unique challenges of big data, recommender systems, statistical reputation systems, and context analysis are used to ensure the veracity of acquired data, machine learning algorithms are applied to handle the data volume, and data analytics algorithms are implemented to manage data velocity. Despite its seemingly insurmountable size, the acquired data is highly redundant, and systematic use of machine intelligence and data analytics can facilitate processing by extracting only the relevant information; in this article, we study the role of these algorithms through the lens of the 3Vs in facilitating soft sensing within the framework of smart city applications.

Description: Industrial wireless sensor networks (IWSNs) are an expansion of the Internet of Things paradigm that fully exploits the collective intelligence of smart nodes in industrial processes. The data acquisition of IWSNs in harsh industrial environments is susceptible to transmission failures and time delay while being strict in the performance of reliability and timeliness. This article proposes an SNR-assured anti-jamming clustering routing (SA-AJCR) protocol to address the problem of data transmission in an environment of high ambient noise and complex electromagnetic interference. To ease the influence of the industrial field on wireless communication, the irregular radio range of each node is modeled through field testing, and then its communication range is artificially constrained to ensure that each signal is received under a satisfying SNR. Through mapping out an approximation of jammed areas at the cluster level, SA-AJCR enables IWSNs to be tolerant of node or link failure caused by jamming. Routing paths can be adjusted to greedily bypass the mapped jammed regions in the network without interrupting the data transmission or overriding the CSMA limitation. The simulation results demonstrate that the proposed protocol is effectively able to provide high packet delivery ratio in hostile electromagnetic environments with the existence of jammers.

Description: In the era of big data, the applications/services of the smart city are expected to offer end users better QoE than in a conventional smart city. Nevertheless, various types of sensors will produce an increasing volume of big data along with the implementation of a smart city, where we face redundant and diverse data. Therefore, providing satisfactory QoE will become the major challenge in the big-data-based smart city. In this article, to enhance the QoE, we propose a novel big data architecture consisting of three planes: the data storage plane, the data processing plane, and the data application plane. The data storage plane stores a wide variety of data collected by sensors and originating from different data sources. Then the data processing plane filters, analyzes, and processes the ocean of data to make decisions autonomously for extracting high-quality information. Finally, the application plane initiates the execution of the events corresponding to the decisions delivered from the data processing plane. Under this architecture, we particularly use machine learning techniques, trying to acquire accurate data and deliver precise information to end users. Simulation results indicate that our proposals could achieve high QoE performance for the smart city.

Description: The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-time data analytics, data integration, transmission of meaningful data, edge analytics, real-time fusion of streaming data, and security and privacy).

Description: The unabated flurry of research activities dedicated to gaining business insights from a flood of data generated by heterogeneous mobile sources, such as the Internet of Vehicles, sensors, and smartphones, has instigated a new research domain called MBD. At the core of this mobile environment, scalability, cost effectiveness, reliability, analytics, and security are important concerns. Coping with these issues in handling MBD requires understanding the challenges associated with it. Mobile computing and big data have been widely studied separately; however, very few studies have explored the convergence of these two domains. In this article, we critically review recent research efforts directed at MBD. We also classify the MBD by devising a thematic taxonomy that is based on source, analytics, applications, characteristics, security, and data type. Furthermore, we discuss the opportunities offered by MBD in terms of analytics. Some potential uses of MBD in healthcare, telecommunication, digital advertising, and transportation are also presented. Several open research challenges are discussed as future research directions.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: With an exponential increase in smart device users, there is an increase in the bulk amount of data generation from various smart devices, which varies with respect to all the essential V's used to categorize it as big data. Generally, most service providers, including Google, Amazon, Microsoft and so on, have deployed a large number of geographically distributed data centers to process this huge amount of data generated from various smart devices so that users can get quick response time. For this purpose, Hadoop, and SPARK are widely used by these service providers for processing large datasets. However, less emphasis has been given on the underlying infrastructure (the network through which data flows), which is one of the most important components for successful implementation of any designed solution in this environment. In the worst case, due to heavy network traffic with respect to data migrations across different data centers, the underlying network infrastructure may not be able to transfer data packets from source to destination, resulting in performance degradation. Focusing on all these issues, in this article, we propose a novel SDN-based big data management approach with respect to the optimized network resource consumption such as network bandwidth and data storage units. We analyze various components at both the data and control planes that can enhance the optimized big data analytics across multiple cloud data centers. For example, we analyze the performance of the proposed solution using Bloom-filter-based insertion and deletion of an element in the flow table maintained at the OpenFlow controller, which makes most of the decisions for network traffic classification using the rule-and-action-based mechanism. Using the proposed solution, developers can deploy and analyze real-time traffic behavior for the future big data applications in MCE.

Description: The Internet has crossed new frontiers with access to it getting faster and cheaper. Considering that the architectural foundations of today's Internet were laid more than three decades ago, the Internet has done remarkably well until today coping with the growing demand. However, the future Internet architecture is expected to support not only the ever growing number of users and devices, but also a diverse set of new applications and services. Departing from the traditional host-centric access paradigm, where access to a desired content is mapped to its location, an information-centric model enables the association of access to a desired content with the content itself, irrespective of the location where it is being held. UMOBILE tailors the information-centric communication model to meet the requirements of opportunistic communications, integrating those connectivity approaches into a single architecture. By pushing services near the edge of the network, such an architecture can pervasively operate in any networking environment and allows for the development of innovative applications, providing access to data independent of the level of end-to-end connectivity availability.

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Description: Presents the ComSoc upcoming calendar of events and meetings.

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Description: The following two books are reviewed: Optimization of Computer Networks - Modeling and Algorithms: A Hands-On Approach (Marino, P.P.; 2016) and Wireless-Powered Communication Networks (Niyato D., et al, Eds.; 2017).

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Description: Unmanned aerial vehicles allow rapidly deploying a multihop communication backbone in challenging environments with applications in public safety, search and rescue missions, crowd surveillance, and disaster area monitoring. Due to environmental obstructions in the above scenarios or intentional jamming, the communication links between peer unmanned aerial vehicless are susceptible to outages. This necessitates resiliency measures to be closely integrated into the network design. To address the needs of efficient and robust end-to-end data relaying, we propose an aerial network management protocol built on top of an SDN architecture. Unique to our design, each unmanned aerial vehicle becomes an SDN switch that performs under directives sent by a centralized controller. Using a novel 3D spatial coverage-related metric, the controller calculates diverse multiple paths among unmanned aerial vehicles so that isolated and localized failures do not interrupt the overall network performance. The controller issues directives to the unmanned aerial vehicle switches through flow entries in Openflow v1.5 protocol for immediate and effective switching to the best available path. Results reveal that the proposed multi-path routing algorithm reduces the average end-to-end outage rate by 18 percent while increasing the average endto- end delay by 12 percent when compared to the traditional multi-path routing algorithms.

Description: The articles in this special section present the most recent advances in amateur drone surveillance network architecture and technologies. The advancement in communication, networking and sensing technologies has attracted researchers, hobbyists, and investors to deploy mini-drones, a.k.a. unmanned air vehicles (UAVs). Drones have boundless viable applications due to their small size and ability to fly without an on-board pilot such as in agriculture, surveillance, and numerous public services. The use of drones for achieving high speed wireless communication is one of the most significant applications for next-generation communication systems (5G). However, their deployment poses several public safety threats to national institutions such as atomic plants, historic places, and the homes of public officials because of their ability to carry explosives and other destructive chemicals. To cope with these security threats, the deployment of surveillance drones is required to monitor amateur drones. The main motivation of deploying surveillance drones is to keep an eye on amateur drones, which can cause serious disasters if no precautionary measures are taken on time. The increasing use of surveillance drones presents challenges such as robust detection, tracking, intruder localization, and jamming. The accuracy of detection is a basic requirement of the system. In general, accurate detection is time-consuming. In fact, a precise moving object detection method makes tracking more reliable and faster, and supports correct classification, which is quite important for surveillance drones to be successful.

Description: Unmanned aerial vehicles, also called drones, are recently gaining increased research attention across various fields due to their flexibility and application potential. The steady increase in the number of amateur drones demands more stringent regulations on their allowed route, mass, and load. However, these regulations may be violated accidentally or deliberately. In these cases, spying with drones, transfer of dangerous payloads, or losing reliable drone control can represent a new hazard for people, governments, and business sector. The technologies to detect, track, and disarm possible aerial threats are therefore in prompt demand. To this end, ubiquitous cellular networks, and especially 5G infrastructures based on the use of millimeter-wave radio modules, may be efficiently leveraged to offer the much needed drone detection capabilities. In this work, we propose to exploit 5G millimeter-wave deployments to detect violating amateur drones. We argue that the prospective 5G infrastructure may provide all the necessary technology elements to support efficient detection of small-sized drones. We therefore outline a novel technology and system design perspective, including such considerations as the density of base stations, their directional antennas, and the available bandwidth, among others, as well as characterize their impact with our ray-based modeling methods.

Description: The use of amateur drones is expected to significantly increase over the upcoming years. However, regulations do not allow such drones to fly over all areas, in addition to typical altitude limitations. As a result, there is an urgent need for amateur drone surveillance solutions. These solutions should include means of accurate detection, classification, and localization of the unwanted drones in a no-fly zone. In this article, we give an overview of promising techniques for modulation classification and signal-strength-based localization of amateur drones by using surveillance drones. By introducing a generic altitude-dependent propagation model, we show how detection and localization performance depend on the altitude of surveillance drones. Particularly, our simulation results show a 25 dB reduction in the minimum detectable power or 10 times coverage enhancement of a surveillance drone by flying at the optimum altitude. Moreover, for a target no-fly zone, the location estimation error of an amateur drone can be remarkably reduced by optimizing the positions of the surveillance drones. Finally, we conclude the article with a general discussion about the future work and possible challenges in aerial surveillance systems.

Description: Presents the President's message for this issue of the publication.

Description: Nowadays, mini-drones, officially called unmanned aerial vehicles, are widely used in many military and civilian fields. Compared to traditional ad hoc networks, the mobile ad hoc networks established by UAVs are more efficient in completing complex tasks in harsh environments. However, due to the unique characteristics of UAVs (e.g., high mobility and sparse deployment), existing protocols or algorithms cannot be directly used for UAVs. In this article, we focus on the routes designed for UAVs, and aim to present a somewhat complete survey of the routing protocols. Moreover, the performance of existing routing protocols is compared in detail, which naturally leads to a great number of open research problems that are outlined afterward.

Description: Drones, also known as mini-unmanned aerial vehicles, have attracted increasing attention due to their boundless applications in communications, photography, agriculture, surveillance, and numerous public services. However, the deployment of amateur drones poses various safety, security, and privacy threats. To cope with these challenges, amateur drone surveillance has become a very important but largely unexplored topic. In this article, we first present a brief survey to show the stateof- the-art studies on amateur drone surveillance. Then we propose a vision, named Dragnet, tailoring the recently emerging Cognitive Internet of Things framework for amateur drone surveillance. Next, we discuss the key enabling techniques for Dragnet in detail, accompanied by the technical challenges and open issues. Furthermore, we provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where, for example, an important event is being held and only authorized drones are allowed to fly over.

Description: Extensive use of amateur drones (ADrs) poses a threat to the public safety due to their possible misuse. Hence, surveillance drones (SDrs) are utilized to detect and eliminate potential threats. However, limited battery, and lack of efficient communication and networking solutions degrade the quality of surveillance. To this end, we conceptualize the Energy Neutral Internet of Drones (enIoD) to enable enhanced connectivity between drones by overcoming energy limitations for autonomous and continuous operation. Power provisioning with recharging stations is introduced by wireless power transfer to energize the drones. Renewable energy harvesting is utilized to realize energy neutrality, which is minimization of deficit in harvested and consumed energy in enIoD. Communication and networking architectures and protocols for realization of multi-dimensional objectives are presented. Finally, possible application areas are explained with a case study to show how enIoD operates.

Description: Wireless networks comprising unmanned aerial vehicles can offer limited connectivity in a cost-effective manner to disaster-struck regions where terrestrial infrastructure might have been damaged. While these drones offer advantages such as rapid deployment to far-flung areas, their operations may be rendered ineffective by the absence of an adequate energy management strategy. This article considers the multi-faceted applications of these platforms and the challenges thereof in the networks of the future. In addition to providing an overview of the work done by researchers in determining the features of the air-to-ground channel, the article explores the use of drones in fields as diverse as military surveillance and network rehabilitation for disaster-struck areas. It also presents a case study that envisages a scenario in which drones operate alongside conventional wireless infrastructure, thereby allowing a greater number of users to establish a line-of-sight link for communication. This study investigates a power allocation strategy for the microwave base station and the small base stations operating at 28 GHz frequency band. The self-adaptive power control strategy for drones is dependent on the maximum allowable interference threshold and minimum data rate requirements. This study highlights the importance of incorporating the drones in the multi-tier heterogeneous network to extend the network coverage and capacity.

Description: Presents key events and topics in the global communications industry.

Description: A smart community is a collection of interdependent human-cyber-physical systems, in which the states of these systems are estimated and adapted by IoT technology. It enables sustainable societies that can offer increased well being, safety, and security. However, a smart community may be susceptible to novel forms of cyber-attacks. This article highlights GPS security vulnerabilities of unmanned ground vehicles (UGVs), which will become more popular in smart communities. We show that UGVs do not have adequate protection against GPS spoofing attacks. Consequently, they can easily be penetrated by attackers.

Description: Various measurements and studies have shown that some licensed frequency bands are underutilized at certain times and in certain locations. In particular, TV bands have been shown to have unused spectrum. Such observations have triggered strong interest in white space dynamic spectrum access among researchers, wireless equipment manufacturers, and standards development organizations. The IEEE Standards Committee on Dynamic Spectrum Access Networks created the White Space Radio Working Group in June 2011 to develop the IEEE 1900.7 standard for white space dynamic spectrum access radio systems. The standard was published in February 2016. This article gives an overview of key concepts and technologies of the IEEE 1900.7 standard, including use cases and requirements, the physical layer, and the MAC sublayer.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: The MEC vision leverages the availability of powerful and low-cost middleboxes, statically deployed at suitable edges of the network and acting as local proxies for the centralized cloud backbone; this potentially enables, among other things, better scalability and better reactivity in the interaction with mobile nodes via local control decisions and actuation. MEC has already been proposed as an enabler for several Internet of Things and cyber-physical systems application scenarios, and also mutual benefits due to the integration of MEC and mobile crowdsensing (MCS). The article originally proposes human-driven edge computing (HEC) as a new model to ease the provisioning and to extend the coverage of traditional MEC solutions. From a methodological perspective, we show how it is possible to exploit MCS i) to support the effective deployment of fixed MEC (FMEC) proxies and ii) to further extend their coverage through the introduction of impromptu and human-enabled mobile MEC (M2EC) proxies. In addition, we describe how we have implemented these novel concepts in the MCS ParticipAct platform through the integration of the MEC Elijah platform in the ParticipAct living lab, an ongoing MCS real-world experiment that involved about 170 students at the University of Bologna for more than two years. Reported experimental results quantitatively show the effectiveness of the proposed techniques in elastically scaling the load at edge nodes according to runtime provisioning needs.

Description: The flying ad hoc network (FANET) is a new paradigm of wireless communication that governs the autonomous movement of UAVs and supports UAV-to-UAV communication. A FANET can provide an effective real-time communication solution for the multiple UAV systems considering each flying UAV as a router. However, existing mobile ad hoc protocols cannot meet the needs of FANETs due to high-speed mobility and frequent topology change. In addition, the complicated flight environment and varied flight tasks lead to the traditional built-in-rules protocols no longer meeting the demands of autonomy. Hence, we have proposed adaptive hybrid communication protocols including a novel position-prediction-based directional MAC protocol (PPMAC) and a self-learning routing protocol based on reinforcement learning (RLSRP). The performance results show that the proposed PPMAC overcomes the directional deafness problem with directional antennas, and RLSRP provides an automatically evolving and more effective routing scheme. Our proposed hybrid adaptive communication protocols have the potential to provide an intelligent and highly autonomous communication solution for FANETs, and indicate the main research orientation of FANET protocols.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Over the last decade, a significant amount of effort has been invested on architecting agile and adaptive management solutions in support of autonomic, self-managing networks. Autonomic networking calls for automated decisions for management actions. This can be realized through a set of pre-defined network management policies engineered from human expert knowledge. However, engineering sufficiently accurate knowledge considering the high complexity of today's networking environment is a difficult task. This has been a particularly limiting factor in the practical deployment of autonomic systems. ML is a powerful technique for extracting knowledge from data. However, there has been little evidence of its application in realizing practical management solutions for autonomic networks. Recent advances in network softwarization and programmability through SDN and NFV, the proliferation of new sources of data, and the availability of lowcost and seemingly infinite storage and compute resource from the cloud are paving the way for the adoption of ML to realize cognitive network management in support of autonomic networking. This article is intended to stimulate thought and foster discussion on how to defeat the bottlenecks that are limiting the wide deployment of autonomic systems, and the role that ML can play in this regard.

Description: Predicting failures on networks and systems is critical in order to maintain high uptime rates. Online failure prediction (OFP) techniques use machine learning and predictive analytics to generate failure models that can be applied to computer network data. These techniques can be provisioned on state-of-the-art stream processing systems, such as Spark Streaming, in order to cope with the scalability challenges from the base data. A big challenge with OFP is selecting the right information to process, as well as the appropriate features in order to achieve high accuracy in predicting failures on complex, interconnected systems. In this article we describe an OFP system built over Apache Spark that takes a repository of network management events, trains a Random Forest model, and uses this model to predict the appearance of future events in near real time. We show through our experiments the usefulness of network management events for accurate predictions, and the advantages of the proposed system in terms of predictive quality, cost, and ease of deployment.

Description: The growing demands of ubiquitous computing are leading to the densification of wireless access networks. The challenges of high density deployments can be addressed by network-wide centralized control. To this end, we propose CADWAN, a Control Architecture for efficient management of Dense WiFi Access Networks. Its main advantages are: flexibility (it supports software-defined wireless networking), scalability (it uses a three-tier optimization framework), and extendibility (it exploits a unified control interface with support for heterogeneous devices). Furthermore, CADWAN is complementary to ongoing developments in IEEE 802.11, especially 802.11ax.

Description: While radio spectrum allocation is well regulated, there is little knowledge about its actual utilization over time and space. This limitation hinders taking effective actions in various applications including cognitive radios, electrosmog monitoring, and law enforcement. We introduce Electrosense, an initiative that seeks a more efficient, safe and reliable monitoring of the electromagnetic space by improving the accessibility of spectrum data for the general public. A collaborative spectrum monitoring network is designed that monitors the spectrum at large scale with low-cost spectrum sensing nodes. The large set of data is stored and processed in a big data architecture and provided back to the community with an open spectrum data as a service model, that allows users to build diverse and novel applications with different requirements. We illustrate useful usage scenarios of the Electrosense data.

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Description: Millimeter-wave MIMO systems are one of the candidate schemes for 5G wireless standardization efforts. In this context, the main contributions of this article are three-fold. First, we describe parallel sets of measurements at identical transmit-receive location pairs with 2.9, 29 and 61 GHz carrier frequencies in indoor office, shopping mall, and outdoor settings. These measurements provide insights on propagation, blockage and material penetration losses, and the key elements necessary in system design to make mm-Wave systems viable in practice. Second, one of these elements is hybrid beamforming necessary for better link margins by reaping the array gain with large antenna dimensions. From the class of fully-flexible hybrid beamformers, we describe a robust class of directional beamformers toward meeting the high data-rate requirements of mm-Wave systems. Third, leveraging these design insights, we then describe an experimental prototype system at 28 GHz that realizes high data rates on both the downlink and uplink and robustly maintains these rates in outdoor and indoor mobility scenarios. In addition to maintaining large signal constellation sizes in spite of radio frequency challenges, this prototype leverages the directional nature of the mm-Wave channel to perform seamless beam switching and handover across mm-Wave base stations, thereby overcoming the path losses in non-line-of-sight links and blockages encountered at mm-Wave frequencies.

Description: Availability of widely harmonized mobile spectrum is crucial for the success of 5G mobile communications, fulfilling the visions and requirements and delivering the full range of potential capabilities for 5G. This article introduces the global situation of spectrum for 5G, both below and above 6 GHz, in both the regulatory status and technical aspects. In particular, the technical challenges of supporting 5G in millimeter-wave spectrum, such as coverage limitation and implementation aspects, are discussed.

Description: Non-orthogonal multiple access (NOMA) as an efficient method of radio resource sharing has its roots in network information theory. For generations of wireless communication systems design, orthogonal multiple access schemes in the time, frequency, or code domain have been the main choices due to the limited processing capability in the transceiver hardware, as well as the modest traffic demands in both latency and connectivity. However, for the next generation radio systems, given its vision to connect everything and the much evolved hardware capability, NOMA has been identified as a promising technology to help achieve all the targets in system capacity, user connectivity, and service latency. This article provides a systematic overview of the state-of-theart design of the NOMA transmission based on a unified transceiver design framework, the related standardization progress, and some promising use cases in future cellular networks, based on which interested researchers can get a quick start in this area.

Description: The 3GPP is in the process of developing the next generation radio access technology, named New Radio (NR), which will be proposed as a candidate technology for IMT-2020. This article outlines the wide bandwidth operation of NR, among other new features being considered, based on the up-to-date discussions and decisions made in 3GPP standardization meetings. The much wider channel bandwidth of NR, compared to LTE, enables more efficient use of resources than the existing carrier aggregation framework with lower control overhead. The support of multiple sub-carrier spacing options allows NR to operate in a wide range of carrier frequency from sub-6 GHz band to mmWave band with appropriate handling of multi-path delay spread and phase noise depending on the carrier frequency. In addition, the introduction of the new bandwidth part concept allows flexible and dynamic configuration of UE's operating bandwidth, which will make NR an energy-efficient solution despite the support of wide bandwidth. Other NR wideband operation related issues, such as the support of UEs with limited RF capability and frequency domain resource indexing, are also explained in this article.

Description: 5G radio access networks are expected to provide very high capacity, ultra-reliability and low latency, seamless mobility, and ubiquitous end-user experience anywhere and anytime. Driven by such stringent service requirements coupled with the expected dense deployments and diverse use case scenarios, the architecture of 5G New Radio (NR) wireless access has further evolved from the traditionally cell-centric radio access to a more flexible beam-based user-centric radio access. This article provides an overview of the NR system multi-beam operation in terms of initial access procedures and mechanisms associated with synchronization, system information, and random access. We further discuss inter-cell mobility handling in NR and its reliance on new downlink-based measurements to compensate for a lack of always-on reference signals in NR. Furthermore, we describe some of the user-centric coordinated transmission mechanisms envisioned in NR in order to realize seamless intra/inter-cell handover between physical transmission and reception points and reduce the interference levels across the network.

Description: This article provides an overview of key features pertaining to CSI reporting and beam management for the 5G New Radio (NR) currently being standardized in 3GPP. For CSI reporting, the modular design framework and high-resolution spatial information feedback offer not only flexibility in a host of use cases and deployment scenarios, but also improved average user throughput over state-of-the-art 4G LTE. To accommodate cellular communications in the milimeter-wave regime where a combination of analog and digital beamforming is typically used at both a base station and user equipment, beam management procedures such as measurement, reporting, and recovery are introduced. The utility and joint usage of these two features are demonstrated along with some potential upgrades for the next phase of 5G NR. Introduction

Description: By placing active antennas in a 2D grid at a BS, 3D MIMO is considered as a promising and practical technique for 5G New Radio (NR). So far, 3D MIMO studies reported are mostly done with antenna elements from 32 up to 128 in the limited scenario at one frequency. To gain further insights into the 3D massive MIMO channel and performance, field measurements from 32 to 256 antenna elements at the transmitter and 16 antenna elements at the receiver are performed in three typical deployment scenarios, including outdoor to indoor, urban microcell, and urban macrocell at both 3.5 and 6 GHz frequencies with 200 MHz bandwidth. Based on the extracted channel information from measured data, power angle spectrum, root mean square angle spread, channel capacity, and eigenvalue spread have been studied. Several observations, including 3D MIMO channel spatial dispersive properties and multi-user performance varying with antenna number, scenario, and frequency are given. These findings can provide valuable experimental insights for efficient utilization of 3D MIMO with massive antenna elements.

Description: Future 5G systems will include a point-to-multipoint (P2MP) transmission mode to achieve high capacity and high spectrum efficiency for multiple use cases, such as IoT, lifeline communications, and broadcast-type services. Layered-division-multiplexing (LDM) is a novel non-orthogonal multiplexing technology recently adopted by the next generation digital TV broadcast system, ATSC 3.0, which is capable of providing significant capacity improvement when delivering multiple broadcast services simultaneously. This article explores the application of LDM as an enabling technology for 5G to achieve high-efficiency P2MP transmission and to deliver more diversified broadcast-type services using the mobile broadband infrastructure. The potential advantages that can be offered by LDM are demonstrated by capacity analysis and computer simulations. Coverage studies show that a 5G P2MP subsystem with LDM can deliver high-quality broadcast services using the broadband infrastructure. Finally, some general guidelines on the receiver implementation are presented to minimize the hardware complexity of consumer devices.

Description: Turbo codes, prevalent in most modern cellular devices, are set to be replaced by LDPC codes as the code for forward error correction. This transition was ushered in mainly because of the high throughput demands for 5G New Radio (NR). The new channel coding solution also needs to support incremental-redundancy hybrid ARQ, and a wide range of blocklengths and coding rates, with stringent performance guarantees and minimal description complexity. In this article, we first briefly review the requirements of the new channel code for 5G NR. We then describe the LDPC code design philosophy and how the broad requirements of 5G NR channel coding led to the introduction of novel structural features in the code design, culminating in an LDPC code that satisfies all the demands of 5G NR.

Description: With the proliferation and diversification of mobile communication services, the current mobile broadband networks face severe challenges. The progressive video service that will make up the majority of future network traffic has more demands for transmission bandwidth, network capacity, and management flexibility. Moreover, denser connectivity, higher reliability, lower latency, and better interaction impose some great challenges to existing networks. In order to meet these growing demands, a promising approach is to integrate a broadcast network with a cellular network. In this article, we briefly set forth the latest progress on the cellular network and the broadcast network, respectively. Then the logic architecture of the integrated network is given to clarify the functional partitioning. In addition, the physical architecture of the integrated network is presented to enable coordination in different aspects. Some feasible strategies are discussed and several promising technologies are presented.

Description: This article concentrates on the multicast service in massive MIMO systems, and develops a hierarchical security model-based opportunistic NOMA scheme for 3D massive MIMO channel. In the considered scenario, the base station communicates with different security level users, where eavesdroppers are randomly deployed. Different security level users are classified into NOMA users and non-NOMA users, where NOMA users employ successive interference cancellation to cancel the low security multicast interference and separate their own data streams. The proposed scheme employs multicast beamforming in the multicast group, and applies null-space-based interference cancellation to eliminate the signal leakage generated by other groups. Numerical results revealed that hierarchical security model-based opportunistic NOMA schemes can significantly improve the network throughput and secrecy capacity in typical massive MIMO scenarios.

Description: Massive MIMO is one of the key enabling technologies in 5G wireless communications. A large number of antenna elements bring extra degrees of freedom for increasing the throughput and considerable beamforming gains for improving the coverage. In practice, a large number of antenna elements can be assembled into multiple antenna panels for the purpose of cost reduction and power saving. Multi-panel MIMO is expected to become promising for millimeter-wave massive MIMO systems. This article gives an overview of multi-panel MIMO systems, including downlink, uplink, and non-coherent transmissions, and discuss research challenges and future directions of multi-panel MIMO. We also propose new multi-panel codebook design and corresponding CSI feedback design for 5G systems as well as non-coherent MIMO transmission.

Description: Drones (or unmanned aerial vehicles) are expected to be an important component of 5G/ beyond 5G (B5G) cellular architectures that can potentially facilitate wireless broadcast or point-to-multipoint transmissions. The distinct features of various drones such as the maximum operational altitude, communication, coverage, computation, and endurance impel the use of a multi-tier architecture for future dronecell networks. In this context, this article focuses on investigating the feasibility of multi-tier drone network architecture over traditional single-tier drone networks and identifying the scenarios in which drone networks can potentially complement the traditional RF-based terrestrial networks. We first identify the challenges associated with multi-tier drone networks as well as drone-assisted cellular networks. We then review the existing state-of-the-art innovations in drone networks and drone-assisted cellular networks. We then investigate the performance of a multi-tier drone network in terms of spectral efficiency of downlink transmission while illustrating the optimal intensity and altitude of drones in different tiers numerically. Our results demonstrate the specific network load conditions (i.e., ratio of user intensity and base station intensity) where deployment of drones can be beneficial (in terms of spectral efficiency of downlink transmission) for conventional terrestrial cellular networks.

Description: As the traffic of local content sharing grows rapidly, device-to-device multicast (D2MD) is introduced into 5G cellular networks, enabling traffic offloading from base stations to device direct transmissions and improved energy and spectrum efficiency. In the content sharing context, the social attributes of mobile users have special concerns to generate effective D2MD groups and D2MD links, and due to reuse of licensed cellular spectrum, the interference between D2MD groups and cellular users should be carefully mitigated. This article presents a D2MD scheme for content sharing in cellular networks by taking into account social and physical attributes in D2MD cluster formation, and jointly optimizing power and channel allocation among D2MD clusters. Simulation results validate the significant throughput gain for D2MD-based content sharing in 5G cellular networks.

Description: Based on spatial and temporal big data, location- based services (LBSs) obtain considerable attention due to their potential to make cities smarter. However, LBS provides the functionality by extracting personal data from users, which may incur privacy leakage risk, such as intrusion, theft, and unauthorized sale of sensitive information. Hence, the mass market share of LBS depends on how well the privacy of citizens can be protected. In this article, we specifically study the privacy issues in LBSs. In detail, we start with classifying different types of LBSs and analyzing the privacy leakage presented in each type. After summarizing the state-of-the-art work on location privacy and query privacy, we propose a query content preservation approach with the aim of providing accurate LBS answer with zero server knowledge on query content. Finally, we present some open problems that may foster future research on LBS privacy preservation.