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Collaborative edge intelligence, a distributed computing paradigm, refers to a system where multiple edge devices work together to process data and perform distributed machine learning (DML) tasks locally. Decentralized Internet of Things (IoT) devices share knowledge and resources to improve the quality of service (QoS) of the system with reduced reliance on centralized cloud infrastructure. However, the paradigm is vulnerable to free-riding attacks, where some devices benefit from the collective intelligence without contributing their fair share, potentially disincentivizing collaboration and undermining the system’s effectiveness. Moreover, data collected from heterogeneous IoT devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a novel incentive mechanism that relies on time-dependent blockchain records and multi-access edge computing (MEC). We formulate the QoS problem as an unbounded multiple knapsack problem at the network edge. Furthermore, a decentralized valuation protocol is introduced atop blockchain to incentivize contributors and disincentivize free-riders. To improve model prediction accuracy within latency requirements, a data scheduling algorithm is given based on a curriculum learning framework. Based on our computer simulations using heterogeneous datasets, we identify two critical factors for enhancing the QoS in collaborative edge intelligence systems: (1) mitigating the impact of information loss and free-riders via decentralized data valuation and (2) optimizing the marginal utility of individual data samples by adaptive data scheduling.

Performance of wireless powered wireless systems is analyzed. The wireless devices in such systems scavenge energy from sources in downlinks and use the energy to communicate in uplinks. We introduce two new models for these energy harvesters to consider their nonlinear circuitry and their functioning over multiple line-of-sight and non-line-of-sight channels. The newly proposed Beaulieu-Xie fading model is used to characterize this manifold of channels. Performance analyses of average harvested energy and transmission outage probability show good fit between the proposed models and measured data.

Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided.

With its rapid development recently, edge computing with processing, storage, and networking capabilities has become an important solution to break through the bottleneck of emerging technology development by virtue of its advantages in reducing data transmission, decreasing service latency, and easing cloud computing pressure. Among several application scenarios such as network optimization, intelligent manufacturing, and real-time video analytics, edge computing can work with artificial intelligence (AI) synergistically. Therefore, many researchers are investigating edge computing with AI from two perspectives. One is that the emergence of AI solves the optimization problem of edge computing. For example, when network devices need to process some complex and fuzzy information, the powerful learning and reasoning ability of AI can help to extract valuable information from the massive data and realize intelligent management. Another is how edge computing supports AI in a networking environment. For example, AI training and inference can be efficiently enabled by a multitude of computing resources from edge computing. Therefore, edge computing and AI are mutually beneficial in networking.

In the wireless Internet of Things (IoT) networks with resource-constrained devices, fog computing has been introduced to deal with the computation-intensive applications at the edges of the networks. While fog computing decreases the computation delay and fronthaul traffic data, it also brings the severe challenge on complex resource allocation of the available computation and communication resources under the stringent quality of service (QoS) requirements. In this paper, we investigate the problem of tasks scheduling and heterogeneous resource allocation for multiple devices in the wireless IoT networks. The IoT devices that collect a massive amount of data need to make proper offloading decision to transfer the data to the fog computing nodes (FNs). Moreover, to support a massive number of device connections and transfer a huge amount of data with low latency and limited resource, we consider the deployment of non-orthogonal multiple access (NOMA) in IoT networks, which enables multiple IoT devices to simultaneously transmit data to the same FN at the same time, frequency, and code domain. We jointly optimize the allocation of resource blocks and transmit power of multiple IoT devices, subject to the respective QoS requirements. Furthermore, the optimization problem is formulated as a mixed-integer nonlinear programming problem to minimize the system energy consumption. Since it is an NP-hard problem, we introduce an improved genetic algorithm (IGA) to solve it. The simulation results show that the proposed scheme achieves good performance in throughput, delay, outage probability, and energy consumption.

Remote monitoring of electrical cable conditions is an essential characteristic of the next-generation smart grid, which features the ability to consistently surveil and control the grid infrastructure. In this paper, we propose a technique that harnesses power line modems (PLMs) for monitoring cable health. We envisage that all or most of these PLMs have already been deployed for data communication purposes and focus on the distribution grid or neighborhood area networks in the smart grid. For such a setting, we propose a machine learning (ML)-based framework for automatic cable diagnostics by continuously monitoring the cable status to identify, assess, and locate possible degradations. As part of our technique, we also synthesize the state-of-the-art reflectometry methods within the PLMs to extract beneficial features for the effective performance of our proposed ML solution. The simulation results demonstrate the effectiveness of our solution under different aging conditions and varying load configurations. Finally, we reflect on our proposed diagnostics method by evaluating its robustness and comparing it with existing alternatives.

With the developing of the smart mobile terminal and wideband application in the mobile Internet, the requirement of large capacity, low latency, and high scalability in the future network is becoming more and more stringent. Edge computing is attracting great attention to decrease time delay and improve the quality of networks' service. By integrating fog computing with the radio access network (RAN) and taking full advantage of the edge computing, fog RAN (F-RAN) is expected as a promising example for 5G/6G systems. The idea of NOMA is to distinguish users whose information is multiplexed into the same orthogonal resource by different power factors, and successive interference cancellation (SIC) is used to detect information at the fog access nodes. In this paper, we focus on dynamic power allocation of wireless sub-channel in NOMA and extend it to F-RAN. A three-user multiplexing model of a NOMA multiuser system model is developed at the transmitting side and the corresponding SIC receiver is designed. Moreover, a group of dynamic power adjusting factors is defined according to users' channel conditions, and an improved fractional transmit power allocation (I-FTPA) algorithm is proposed for NOMA and applied to the three-user model. The experimental results demonstrate that the I-FTPA performs better than the previous FPA and FTPA, more meaningful is that more than one wireless channel power allocation schemes can be found performing better than OFDMA under the same conditions for F-RAN by using the I-FTPA, which provides some significance for deploying NOMA into F-RAN in the future application.

Conventional collaborative beamforming (CB) with virtual node antenna array often results in high maximum sidelobe level (SLL) due to the unexpected positions of the nodes. In this paper, a sidelobe suppression approach (JSSA) to suppress the maximum SLL by jointly optimizing the locations and amplitude weights of the nodes is proposed. JSSA organizes the node positions according to the concentric circular antenna array for location optimization. Then, a novel algorithm called variation particle chicken swarm optimization (VPCSO) is proposed to further optimize the amplitude weights of the selected CB nodes. The proposed VPCSO introduces the variation mechanism and location learning mechanism to enhance the performance of the conventional chicken swarm optimization algorithm. Simulations are conducted and the results show that the proposed location optimization approach is effective, and the maximum SLLs of beam patterns obtained by VPCSO are lower than that of other algorithms. Moreover, the energy consumption can be saved by VPCSO. In addition, electromagnetic (EM) simulations are conducted to verify the performance of the proposed JSSA in EM environments.

Urban water distribution networks (WDNs) are usually threatened by leakage, reflux, infiltration and internal pollution. To ensure the safety of water supply, it is essential to properly monitor the WDNs. However, it is not easy to design a fast, fine-grained and comprehensive monitoring solution under limited cost, due to the large scale and high complexity of WDNs. In recent years, mobile sensor networks have been widely utilized to monitor WDNs with data uploaded by static nodes or receiver nodes. However, existing solutions haven't addressed the impact of duplicated uploading caused by the topology of WDNs. Hence, the coverage rate is unsatisfactory. In addition, the mobile sensor nodes could be potentially far away from the receiver nodes, which might introduce long data acquisition delay. In this work, we propose a low delay and high coverage WDNs monitoring solution, with the constraint of limited number of mobile and receiver nodes. The proposed solution can find the optimal deployment strategy of mobile and receiver nodes by constructing a probability dependent model between mobile and receiver nodes, and coordinating the trade-off between low-delay and high-coverage under multi-objective optimization model. We also analyzed the theoretical lower bound of the algorithm, by utilizing the submodular function characteristics of the objective. Extensive simulation proved that the proposed algorithm can obtain higher coverage with lower delay.

Ram Adapa (Fellow, IEEE) received the B.S. degree from Jawaharlal Nehru Technological University, India, the M.S. degree from IIT Kanpur, India, and the Ph.D. degree from the University of Waterloo, ON, Canada, all in electrical engineering.

Recently, due to the increasing popularity of enjoying various multimedia services on mobile devices (e.g., smartphones, ipads, and electronic tablets), the generated mobile data traffic has been explosively growing and has become a serve burden on mobile network operators. To address such a serious challenge in mobile networks, an effective approach is to manage data traffic by using complementary technologies (e.g., small cell network, WiFi network, and so on) to achieve mobile data offloading. In this paper, we discuss the recent advances in the techniques of mobile data offloading. Particularly, based on the initiator diversity of data offloading, we classify the existing mobile data offloading technologies into four categories, i.e., data offloading through small cell networks, data offloading through WiFi networks, data offloading through opportunistic mobile networks, and data offloading through heterogeneous networks. Besides, we show a detailed taxonomy of the related mobile data offloading technologies by discussing the pros and cons for various offloading technologies for different problems in mobile networks. Finally, we outline some opening research issues and challenges, which can provide guidelines for future research work.

The growing popularity of smart mobile devices has not only triggered an exponential increase in mobile data traffic, but also boosted the computation demands and requirements to an unprecedented level. Meanwhile, the next generation wireless networks, i.e., 5G networks, are envisioned to provide richer user experience and diversified mobile applications, which calls for more intensive computation at mobile devices. Therefore, the concept of Mobile Edge Computing (MEC) has been recently introduced as a means to bring cloud computing capabilities at the mobile network edge.

Intrusion detection system (IDS) provides an important basis for the network defense. Due to the development of the cloud computing and social network, massive amounts of data are generated, which inevitably brings much pressure to IDS. And therefore, it becomes crucial to efficiently divide the data into different classes over big data according to data features. Moreover, we can further determine whether one is normal behavior or not based on the classes information. Although the clustering approach based on K-means for IDS has been well studied, unfortunately directly using it in big data environment may suffer from inappropriateness. On the one hand, the efficiency of data clustering needs to be improved. On the other hand, differ from the classification, there is no unified evaluation indicator for clustering issue, and thus, it is necessary to study which indicator is more suitable for evaluating the clustering results of IDS. In this paper, we propose a clustering method for IDS based on Mini Batch K-means combined with principal component analysis. First, a preprocessing method is proposed to digitize the strings and then the data set is normalized so as to improve the clustering efficiency. Second, the principal component analysis method is used to reduce the dimension of the processed data set aiming to further improve the clustering efficiency, and then mini batch K-means method is used for data clustering. More specifically, we use K-means++ to initialize the centers of cluster in order to avoid the algorithm getting into the local optimum, in addition, we choose the Calsski Harabasz indicator so that the clustering result is more easily determined. Compared with the other methods, the experimental results and the time complexity analysis show that our proposed method is effective and efficient. Above all, our proposed clustering method can be used for IDS over big data environment.

Machine learning is one of the most prevailing techniques in computer science, and it has been widely applied in image processing, natural language processing, pattern recognition, cybersecurity, and other fields. Regardless of successful applications of machine learning algorithms in many scenarios, e.g., facial recognition, malware detection, automatic driving, and intrusion detection, these algorithms and corresponding training data are vulnerable to a variety of security threats, inducing a significant performance decrease. Hence, it is vital to call for further attention regarding security threats and corresponding defensive techniques of machine learning, which motivates a comprehensive survey in this paper. Until now, researchers from academia and industry have found out many security threats against a variety of learning algorithms, including naive Bayes, logistic regression, decision tree, support vector machine (SVM), principle component analysis, clustering, and prevailing deep neural networks. Thus, we revisit existing security threats and give a systematic survey on them from two aspects, the training phase and the testing/inferring phase. After that, we categorize current defensive techniques of machine learning into four groups: security assessment mechanisms, countermeasures in the training phase, those in the testing or inferring phase, data security, and privacy. Finally, we provide five notable trends in the research on security threats and defensive techniques of machine learning, which are worth doing in-depth studies in future.

With the proliferation of smart embedded devices, cyber physical social computing and networking systems (CPSCN) are emerging as a next generation of social networks. Unlike traditional social networks that run on cloud-based infrastructure, CPSCN systems usually depend on a large number of distributed, heterogeneous devices, such as mobile phones, smart vehicles, or network access points. These computing resources, which are often referred to as fog computing systems, provide a gateway to the physical world, and thus offer new possibilities for social applications. Unfortunately, building CPSCN systems that leverage fog computing infrastructure is not straightforward. Significant challenges arise from the large scale distribution of computing resources over a wide area, and the dynamic nature of multiple, possibly mobile, hosts. In this paper, we extend our previous work on a distributed dataflow programming model and propose an application platform for realizing CPSCN systems. A key aspect of our work is the development of an exogenous coordination model, which exhibits a separation of concern between computation and communication activities, and helps resolve some of the challenges brought about by the dynamic and large scale nature of CPSCN systems.

Interference management and energy management are two important issues in ultra-dense heterogeneous cellular networks (HetNets). However, load balancing investigated for system capacity on interference coordination is not efficient for energy saving in HetNets. Meanwhile, the maximum energy efficiency configuration leads to the serious unfair problem for users associated with larger power node for the ultra-dense scenario. Thus, in this paper, we investigate energy consumption jointly together with interference coordination for ultra-dense HetNets, and formulate max-min energy-efficient enhanced intercell interference coordination configuration problem. Due to the non-smooth and mixed programming nature of this formulation, we propose a novel iterative and distributed algorithm to solve the problem by using fractional programming and Lagrangian dual theory. The simulation results verified the effectiveness of our proposed algorithm and fairness achieved for energy efficiency of users, it especially identified a new energy efficiency tradeoff between macro user and small cell user with interference coordination in ultradense HetNets.

The explosive advancements in mobile Internet and Internet of Things challenge the network capacity and architecture. The ossification of wireless networks hinders the further evolution toward the fifth generation of mobile communication systems. Ultra-dense small cell networks are considered as a feasible way to meet high-capacity demands. Meanwhile, ultra-dense small cell network virtualization also exploits an insightful perspective for the evolution because of its superiority, such as diversity, flexibility, low cost, and scalability. In this paper, we specify the necessity of resource management in virtualized ultra-dense small cell networks through a mapping and management architecture and consider the problem of user-oriented virtual resource management. Then, we model the virtual resource management problem as a hierarchical game and obtain the closed-form solutions for spectrum, power, and price, respectively. Furthermore, we propose a customer-first (CF) algorithm that characterizes the user-oriented service of virtualization, and analyze its convergence. Simulation results present the effectiveness of the proposed CF algorithm.

How to measure the centrality of nodes is a significant problem in mobile social networks (MSNs). Current studies in MSNs mainly focus on measuring the centrality of nodes in a certain time interval based on the static graph that do not change over time. However, the network topology of MSNs is changing very rapidly, which is the main characteristic of MSNs. Therefore, it will not be accurate to measure the centrality of nodes in a certain time interval by using the static graph. To solve this problem, this paper first introduces a new centrality metric named cumulative neighboring relationship (CNR) for MSNs. Then, a time-ordered aggregation model is proposed to reduce a dynamic network to a series of time-ordered networks. Based on the time-ordered aggregation model, this paper proposes three particular time-ordered aggregation methods and combines with the proposed centrality metric CNR to measure the importance of nodes in a certain time interval. Finally, extensive trace-driven simulations are conducted to evaluate the performance of our proposed time-ordered aggregation model-based centrality metric time-ordered cumulative neighboring relationship (TCNR). The results show that the exponential time-ordered aggregation method can measure TCNR centrality in a certain time interval more accurately than other aggregation methods, and our proposed time-ordered aggregation model-based centrality metric TCNR outperforms other existing temporal centrality metrics.

Blockchain technology has attracted tremendous attention in both academia and capital market. However, overwhelming speculations on thousands of available cryptocurrencies and numerous initial coin offering scams have also brought notorious debates on this emerging technology. This paper traces the development of blockchain systems to reveal the importance of decentralized applications (dApps) and the future value of blockchain. We survey the state-of-the-art dApps and discuss the direction of blockchain development to fulfill the desirable characteristics of dApps. The readers will gain an overview of dApp research and get familiar with recent developments in the blockchain.

In this paper, we investigate the performance of simultaneous wireless information and power transfer cooperative amplify-and-forward communication systems with binary differential phase-shift keying modulation. The piece-wise linear energy harvesting (EH) model that captures the nonlinear input-output characteristic of the EH circuit is considered. We derive a novel and closed-form expression of the probability density function (PDF) for the harvested power of the nonlinear EH receiver. Based on this, we further derive the expressions for the PDF of the received signal-to-noise ratio of the cooperative link and the average biterror-rate of the systems. Numerical results confirm the validity of the proposed analytical results. It is shown that the system performance is significantly overestimated when the ideal linear EH model is used in the place of the practical nonlinear EH model. An opposite result to the linear EH model that the system performance is monotonically improved with the increase of the distance between the location of the relay node and the source node is also demonstrated.

In this paper, we propose a weighted Dempster-Shafer (DS) evidence theory-based fusion algorithm to take advantages of state-of-the-art salient object detection methods. First, we define the mass function value for each saliency detection method to be fused at the pixel level, based on which we further calculate the similarity coefficient and similarity matrix. The credibility of each saliency detection method will be computed by considering to what degree it is supported by other saliency detection methods. Second, using the credibility of each image saliency detection method as the weight, we compute the weighted mass function value of each method and get a saliency map. Third, we use the synthetic rules of DS evidence theory to fuse the weighted mass function values and get the other saliency map. The final saliency map will be obtained by fusing the aforementioned two saliency maps. Extensive experiments on three publicly available benchmark datasets demonstrate the superiority of the proposed weighted DS evidence theory-based fusion model against each individual saliency detection algorithm in terms of three evaluation metrics of precision-recall rate, F-measure, and average absolute error. The saliency map after fusion utilizing weighted DS evidence theory is closer to the ground-truth map.

A unified framework for bridging the power allocation (PA) strategies of single carrier (SC) and orthogonal frequency division multiplexing (OFDM) systems is proposed through the hybrid carrier (HC) scheme based on weighted-type fractional Fourier transform (WFRFT). The performance analyses of multiple indicators are easily achieved in the proposed framework, and the expected performances could be obtained according to the flexible WFRFT order. The closed BER expressions of arbitrary PA strategies in the HC scheme are derived and verified by the simulations, where the equal gain PA and minimum mean square error (mmse) PA are given as the examples. Compared with the OFDM and SC systems, the tradeoff between BER, achievable rate, and peak-to-average power ratio is achieved in the HC system. In addition, an approximate minimum bit error rate method through joining the PA and subcarrier allocation is investigated in the HC scheme as a generalization of the proposed framework. Considering the novel structure, the dynamic threshold could be established in the HC scheme, and thus the tradeoff between BER and complexity is obtained in comparison with the OFDM and SC systems. In order to illustrate the anti-interference characteristics of the proposed HC framework with PA, the BER performance with insufficient cyclic prefix is investigated. The better BER performance is captured in the HC system with the proposed mmse PA strategy.

In this paper, we study the potential of non-orthogonal multiple access (NOMA) for the purpose of interference mitigation in downlink small cell networks (SCNs). Different from prior works, we focus on opportunistically multiplexing different users on the same subchannel to avoid the severe inter-cell interference brought in by ultradense networking. Aiming to maximize the network throughput, we formulate a distributed subchannel assignment problem with local information exchange. This problem is analyzed through a locally cooperative game model, and the existence of Nash equilibrium (NE) is confirmed by proving that the formulated game is an exact potential game. To solve the problem, we design two concurrent distributed algorithms based on best response (BR) and spatial adaptive play (SAP), respectively. The BR-based algorithm guarantees rapid convergence to an NE, which may not be globally optimal. On the contrary, the SAP-based algorithm can find the global optimum with an arbitrary large probability, although the learning process requires more iterations to converge. Simulation results reveal that the aggregate interference can be more efficiently suppressed in NOMA enhanced networks, which can lead to higher network throughputs. Besides, the superiority of NOMA over orthogonal multiple access is more obvious when the network grows denser.

With the rapid development of 5G communications, enhanced mobile broadband, massive machine type communications and ultra-reliable low latency communications are widely supported. However, a 5G communication system is still based on Shannon’s information theory, while the meaning and value of information itself are not taken into account in the process of transmission. Therefore, it is difficult to meet the requirements of intelligence, customization, and value transmission of 6G networks. In order to solve the above challenges, we propose a 6G mailbox theory, namely a cognitive information carrier to enable distributed algorithm embedding for intelligence networking. Based on Mailbox, a 6G network will form an intelligent agent with self-organization, self-learning, self-adaptation, and continuous evolution capabilities. With the intelligent agent, redundant transmission of data can be reduced while the value transmission of information can be improved. Then, the features of mailbox principle are introduced, including polarity, traceability, dynamics, convergence, figurability, and dependence. Furthermore, key technologies with which value transmission of information can be realized are introduced, including knowledge graph, distributed learning, and blockchain. Finally, we establish a cognitive communication system assisted by deep learning. The experimental results show that, compared with a traditional communication system, our communication system performs less data transmission quantity and error.

To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications.

Due to the limited battery energy of mobile devices, the issue of energy-efficient resource allocation has drawn significant interest in the mobile cloud computing area. Simultaneous wireless information and power transfer (SWIPT) is an innovative way to provide electrical energy for mobile devices. Extensive research on the resource allocation problem is conducted in SWIPT systems. However, most previous works mainly focus on energy harvesting over a relatively narrow frequency range. Due to small amounts of energy harvested by the users, the practical implementations are usually limited to low power devices. In this paper, an energy-efficient uplink resource allocation problem is investigated in a cloud-based cellular network with ambient radio frequency (RF) energy harvesting. In order to obtain sufficient energy, a broadband rectenna is equipped at the user device to harvest ambient RF energy over six frequency bands at the same time. From the viewpoint of service arrival in the ambient transmitter, a new energy arrival model is presented. The joint problem of sub-carrier and power allocation is formulated as a mixed-integer nonlinear programming problem. The objective is to maximize the energy efficiency while satisfying the energy consumption constraint and the total data rate requirement. In order to reduce the computational complexity, a suboptimal solution to the optimization problem is derived by employing a quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that more energy can be harvested by the user devices compared with narrow band SWIFT systems, and the QPSO method achieves higher energy efficiency than a conventional particle swarm optimization approach.

Interference is one of the fundamental aspects that makes wireless communication challenging, which has attracted great research attention for decades. To solve this interference problem, many interference management (IM) techniques have been developed. Nevertheless, interference can also provide some benefits to wireless networks if it is properly utilized according to the latest research advances. Wireless signal can carry information as well as energy, and thus the redundant resource of interference can be exploited using energy harvesting (EH) to provide the power to support the operation of wireless nodes. In this paper, we provide a comprehensive survey on the research works of exploiting interference for wireless EH. Some fundamental aspects are first reviewed, including the receiver architecture, antenna dimension, network topology, and IM techniques, for wireless EH systems that exploit interference. Then, two IM techniques for wireless EH, beamforming optimization and interference alignment, are discussed in detail. In addition, several research issues are also presented, including the adversarial jamming signal and artificial noise for EH. Finally, some research challenges of exploiting interference for wireless EH are discussed.

In recent years, demands for rich multimedia services over mobile networks have been soaring at a tremendous pace. Traditional dedicated networking equipment may not be able to efficiently support the phenomenal growth of the traffic load and user demand dynamics while consuming an unnecessarily large amount of energy resources. Recently, mobile content caching, whereby popular contents are cached inside the mobile front-haul and back-haul networks so that demands for these contents from users in proximity can be easily accommodated without redundant transmissions from the remote sources, has emerged as an efficient technique for multimedia content delivery. Mobile content caching is particularly suitable for fifth generation (5G) mobile systems that are being designed to incorporate advanced cloud computing technologies and network function virtualization techniques. Therefore, in this paper, we first propose the concept of “Caching-as-a-Service” (CaaS) based on cloud-based radio access networks, and virtualized evolved packet core, which provides the capability to cache anything at anytime, anywhere in the cloud-based 5G mobile systems to satisfy user demands from any service location with high elasticity and adaptivity, and to empower third-party service providers with flexible controllability and programmability. Then, we study the potential techniques related to the virtualization of caching, and discuss the technical details of virtualization and optimization of CaaS in 5G mobile networks. Some novel schemes for CaaS are proposed to target different mobile applications and services. We also explore new opportunities and challenges for further research.

The Internet of Things envisions a multitude of heterogeneous objects and interactions with physical environments. It has provided a promising opportunity to build powerful services and applications by leveraging the growing ubiquity of smart objects connected to the Internet. In this environment, it is challenging to locate desirable services due to the considerable diversity, large scale, dynamic behavior, and geographical distribution of the services provided by physical objects. In this paper, we propose a decentralized semantics-based service discovery framework, which can effectively locate trustworthy services based on requester's quality of service demands and changing context requirements. The proposed service discovery system is built upon fully distributed peer-to-peer architectures and includes two discovery approaches which are scalable, robust, and trust assured. Moreover, an efficient trust propagation mechanism is proposed, which seamlessly integrates with the proposed service discovery system without adding additional infrastructure overhead. Comprehensive simulation studies have demonstrated the effectiveness of the proposed framework.

The Internet of Things (IoT) has emerged as an important field of future network research that focuses on ubiquitous service patterns and universal access for people, devices, processes, etc. Motivated by great market demand and potential profit, future innovations for the application and accommodation for IoT will soon be realized and deployed expeditiously. However, those innovations will inevitably expose IoTs to security and privacy risks. Our society has witnessed tremendous concerns about these issues. Therefore, examining IoT applications and services closely for security and privacy vulnerabilities is of the utmost importance and can be further realized in three dimensions, namely, network architectural principles, application design, and high quality of service. From small atomic elements of the network to entire service packages, fine-grained verification should be performed to reduce security and privacy risks of an individual or the entire network.

Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.

Massive multiple-input multiple-output (MIMO) has been extensively studied and considered as a key enabling technology for the fifth generation (5G) wireless communication systems, due to its potential to achieve high energy efficiency and spectral efficiency. As the concept of massive MIMO becomes more popular, it is plausible that the eavesdroppers will also employ massive antennas, which may remarkably enhance their ability to intercept the information. In this paper, motivated by the need to protect against the eavesdroppers equipped with powerful large antenna arrays, which has received scarce attention in the literature, a physical layer security approach called original symbol phase rotated (OSPR) secure transmission scheme is proposed to defend against eavesdroppers armed with unlimited antennas. The basic idea of the proposed OSPR scheme is to randomly rotate the phase of original symbols at the base station (BS) before they are transmitted, so that the massive MIMO eavesdropper will be confused by the intercepted signals, which may not represent the true information symbols. However, the legitimate users are able to infer the correct phase rotations and take proper inverse operations to recover the original symbols. We show that when the BS has a large enough, but finite number of antennas, the proposed OSPR scheme can achieve a considerable security performance in that the eavesdropper is unable to recover most of the original symbols, even with unlimited antennas. The process and the security performance of the proposed OSPR scheme are presented in detail. Simulation results are provided to further corroborate that the proposed OSPR scheme is a potential green secure transmission candidate technique for the future wireless networks.

Cloud gaming is a new way to deliver high-quality gaming experience to gamers anywhere and anytime. In cloud gaming, sophisticated game software runs on powerful servers in data centers, rendered game scenes are streamed to gamers over the Internet in real time, and the gamers use light-weight software executed on heterogeneous devices to interact with the games. Due to the proliferation of high-speed networks and cloud computing, cloud gaming has attracted tremendous attentions in both the academia and industry since late 2000's. In this paper, we survey the latest cloud gaming research from different aspects, spanning over cloud gaming platforms, optimization techniques, and commercial cloud gaming services. The readers will gain the overview of cloud gaming research and get familiar with the recent developments in this area.

With the coming of the Internet of Things (IoT) and the fifth generation (5G) wireless communication era, more and more lightweight user equipments (UEs) appear in our life. The private information they gather and transmit on the uplink will likely face security risks, since the lightweight UEs are probably with limited number of antennas, e.g., only one antenna, limited power and low signal processing and data computing capabilities, which may inherently weaken the corresponding secrecy performance. As a consequence, traditional cryptographic techniques and complex physical layer security techniques with favorable secrecy performance may not be suitable for lightweight UEs due to high implementation complexity. Moreover, it is highly plausible that the unauthorized nodes can utilize much more powerful large antenna array, i.e., massive multiple-input multiple-output (MIMO) technology, to intercept the uplink information sent by the lightweight UEs due to the maturity of massive MIMO technology by then. Considering the possibility of facing massive MIMO eavesdropper, we propose to adopt the uplink original symbol phase rotated (UOSPR) scheme to secure the uplink transmission for lightweight single-antenna UEs in this paper. By employing the UOSPR secure transmission scheme, the lightweight UEs will randomly rotate the original information bearing symbols before they are transmitted to the BS on the uplink. This can be viewed as a symbol encryption process. The BS is then assured to be able to infer the accurate phase rotation and recover the original symbols while the massive MIMO eavesdropper can learn little useful information about the randomly rotated phase. The corresponding secrecy analysis of the UOSPR scheme on the uplink transmission is presented in detail. Furthermore, we show that the UOSPR scheme is with low complexity from the perspective of the lightweight UEs, which potentially makes it a candidate uplink secure transmission scheme in IoT and 5G scenarios. Simulation results are provided to further corroborate the effectiveness of the UOSPR secure transmission scheme.

With the accelerated development of Internet-of-Things (IoT), wireless sensor networks (WSNs) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with the Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for WSNs design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards, and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross-layer design of lightweight and cloud-based RESTful Web service.

Recently, mobile networking systems have been designed with more complexity of infrastructure and higher diversity of associated devices and resources, as well as more dynamical formations of networks, due to the fast development of current Internet and mobile communication industry. In such emerging mobile heterogeneous networks (HetNets), there are a large number of technical challenges focusing on the efficient organization, management, maintenance, and optimization, over the complicated system resources. In particular, HetNets have attracted great interest from academia and industry in deploying more effective solutions based on artificial intelligence (AI) techniques, e.g., machine learning, bio-inspired algorithms, fuzzy neural network, and so on, because AI techniques can naturally handle the problems of large-scale complex systems, such as HetNets towards more intelligent and automatic-evolving ones. In this paper, we discuss the state-of-the-art AI-based techniques for evolving the smarter HetNets infrastructure and systems, focusing on the research issues of self-configuration, self-healing, and self-optimization, respectively. A detailed taxonomy of the related AI-based techniques of HetNets is also shown by discussing the pros and cons for various AI-based techniques for different problems in HetNets. Opening research issues and pending challenges are concluded as well, which can provide guidelines for future research work.

Smart world is envisioned as an era in which objects (e.g., watches, mobile phones, computers, cars, buses, and trains) can automatically and intelligently serve people in a collaborative manner. Paving the way for smart world, Internet of Things (IoT) connects everything in the smart world. Motivated by achieving a sustainable smart world, this paper discusses various technologies and issues regarding green IoT, which further reduces the energy consumption of IoT. Particularly, an overview regarding IoT and green IoT is performed first. Then, the hot green information and communications technologies (ICTs) (e.g., green radio-frequency identification, green wireless sensor network, green cloud computing, green machine to machine, and green data center) enabling green IoT are studied, and general green ICT principles are summarized. Furthermore, the latest developments and future vision about sensor cloud, which is a novel paradigm in green IoT, are reviewed and introduced, respectively. Finally, future research directions and open problems about green IoT are presented. Our work targets to be an enlightening and latest guidance for research with respect to green IoT and smart world.

Abstract This Special Issue originates from the international conference EuCNC2016, which was held in June 2016 in Athens, Greece. Initially, it publishes some key contributions presented at the conference describing different aspects in the most recent 5G (5th generation) activities in the areas of Air Interfaces and Management Technologies. The series continues with further articles in the context of the same area. 5G mobile networks/wireless systems are the next step of mobile telecommunication standards, offering services and speed far beyond what 4G may offer. The most recent research activities focus on the development of 5G communications and networks, aiming to be fully available for the consumers through their devices by 2020. The scope of this Special Issue is to focus on aspects like 5G communications and networks technologies and more specifically Air Interfaces and Management Technologies. In the context of this Special Issue, numerous high-quality papers were received. After rigorous peer review, the following papers have been accepted and included in the Special Issue.

Heterogeneous wireless networks are capable of providing customers with better services while service providers can offer more applications to more customers with lower costs. To provide services, some applications rely on existing servers in the network. In a vehicular ad-hoc network (VANET) some mobile nodes may function as servers. Due to high mobility of nodes and short lifetime of links, server-to-client and server-to-server communications become challenging. In this paper we propose to enhance the performance of server selection by taking link reliability into consideration in the server selection mechanism, thereby avoiding extra client-to-server hand-offs and reducing the need of server-to-server synchronization. As a case study we focus on location management service in a heterogeneous VANET. We provide a routing algorithm for transactions between location servers and mobile nodes. We assume that location servers are vehicles equipped with at least one long- range and one short-range radio interfaces, whereas regular nodes (clients) are only equipped with a short-range radio interface. The primary goal of our design is to minimize hand-offs between location servers while limiting the delays of location updates. Taking advantage of vehicle mobility patterns, we propose a mobility-aware server selection scheme and show that it can reduce the number of hand-offs and yet avoid large delays during location updates. We present simulation results to show that proposed scheme significantly lowers the costs of signaling and rate of server hand-offs by increasing the connection lifetimes between clients and servers.

To meet the requirements of 5G on enabling higher capacity, faster rates, more connectivity, higher reliability, lower latency, greater versatility, and application-domain specific topologies, new concepts and design approaches are in great need. Current standards for 4G may influence the introduction of radio features and network solutions for 5G systems. New network architectures extending beyond heterogeneous networks and exploiting new frequency spectra (e.g., millimeter wave) are emerging from research laboratories around the world. In addition to the network side, advanced terminals and receivers are being developed to optimize network performances. Splitting the control and data planes [currently studied in 3G Partnership Project (3GPP)] is an interesting paradigm for 5G, together with massive multi-input multi-output (MIMO), advanced antenna systems, software-defined networking (SDN), network functions virtualization (NFV), the Internet of Things, and cloud computing. Also, new radio protocols enabling heterogeneous traffics are required.

Around the globe, innovation with integrating information and communication technologies (ICT) with physical infrastructure is a top priority for governments in pursuing smart, green living to improve energy efficiency, protect the environment, improve the quality of life, and bolster economy competitiveness. Cities today faces multifarious challenges, among which energy efficiency of homes and residential dwellings is a key requirement. Achieving it successfully with the help of intelligent sensors and contextual systems would help build smart cities of the future. In a Smart home environment Home Energy Management plays a critical role in finding a suitable and reliable solution to curtail the peak demand and achieve energy conservation. In this paper, a new method named as Home Energy Management as a Service (HEMaaS) is proposed which is based on neural network based Q-learning algorithm. Although several attempts have been made in the past to address similar problems, the models developed do not cater to maximize the user convenience and robustness of the system. In this paper, authors have proposed an advanced Neural Fitted Q-learning method which is self-learning and adaptive. The proposed method provides an agile, flexible and energy efficient decision making system for home energy management. A typical Canadian residential dwelling model has been used in this paper to test the proposed method. Based on analysis, it was found that the proposed method offers a fast and viable solution to reduce the demand and conserve energy during peak period. It also helps reducing the carbon footprint of residential dwellings. Once adopted, city blocks with significant residential dwellings can significantly reduce the total energy consumption by reducing or shifting their energy demand during peak period. This would definitely help local power distribution companies to optimize their resources and keep the tariff low due to curtailment of peak demand.

Device-to-device (D2D) communication is becoming an increasingly important technology in future networks with the climbing demand for local services. For instance, resource sharing in the D2D network features ubiquitous availability, flexibility, low latency and low cost. However, these features also bring along challenges when building a satisfactory resource sharing system in the D2D network. Specifically, user mobility is one of the top concerns for designing a cooperative D2D computational resource sharing system since mutual communication may not be stably available due to user mobility. A previous endeavour has demonstrated and proven how connectivity can be incorporated into cooperative task scheduling among users in the D2D network to effectively lower average task execution time. There are doubts about whether this type of task scheduling scheme, though effective, presents fairness among users. In other words, it can be unfair for users who contribute many computational resources while receiving little when in need. In this paper, we propose a novel blockchain-based credit system that can be incorporated into the connectivity-aware task scheduling scheme to enforce fairness among users in the D2D network. Users’ computational task cooperation will be recorded on the public blockchain ledger in the system as transactions, and each user’s credit balance can be easily accessible from the ledger. A supernode at the base station is responsible for scheduling cooperative computational tasks based on user mobility and user credit balance. We investigated the performance of the credit system, and simulation results showed that with a minor sacrifice of average task execution time, the level of fairness can obtain a major enhancement.

There is an increasing interest in using video sensor networks (VSNs) as an alternative to existing video monitoring/surveillance applications. Due to the limited amount of energy resources available in VSNs, power consumption efficiency is one of the most important design challenges in VSNs. Video encoding contributes to a significant portion of the overall power consumption at the VSN nodes. In this regard, the encoding parameter settings used at each node determine the coding complexity and bitrate of the video. This, in turn, determines the encoding and transmission power consumption of the node and the VSN overall. Therefore, in order to calculate the nodes' power consumption, we need to be able to estimate the coding complexity and bitrate of the video. In this paper, we modeled the coding complexity and bitrate of the H.264/AVC encoder, based on the encoding parameter settings used. We also propose a method to reduce the model estimation error for videos whose content changes within a specified period of time. We have conducted our experiments using a large video dataset captured from real-life applications in the analysis. Using the proposed model, we show how to estimate the VSN power consumption for a given topology.

Internet-of-Things (IoT) is emerging to play an important role in the continued advancement of information and communication technologies. To accelerate industrial application developments, the use of web services for networking applications is seen as important in IoT communications. In this paper, we present a RESTful web service architecture for energy-constrained wireless sensor networks (WSNs) to enable remote data collection from sensor devices in WSN nodes. Specifically, we consider both IPv6 protocol support in WSN nodes as well as an integrated gateway solution to allow any Internet clients to access these nodes.We describe the implementation of a prototype system, which demonstrates the proposed RESTful approach to collect sensing data from a WSN. A performance evaluation is presented to illustrate the simplicity and efficiency of our proposed scheme.

The Internet of Things (IoT) represents a bright prospect that a variety of common appliances can connect to one another, as well as with the rest of the Internet, to vastly improve our lives. Unique communication and security challenges have been brought out by the limited hardware, low-complexity, and severe energy constraints of IoT devices. In addition, a severe spectrum scarcity problem has also been stimulated by the use of a large number of IoT devices. In this paper, cognitive IoT (CIoT) is considered where an IoT network works as the secondary system using underlay spectrum sharing. A wireless energy harvesting (EH) node is used as a relay to improve the coverage of an IoT device. However, the relay could be a potential eavesdropper to intercept the IoT device’s messages. This paper considers the problem of secure communication between the IoT device (e.g., sensor) and a destination (e.g., controller) via the wireless EH untrusted relay. Since the destination can be equipped with adequate energy supply, secure schemes based on destination-aided jamming are proposed based on power splitting (PS) and time splitting (TS) policies, called intuitive secure schemes based on PS (Int-PS), precoded secure scheme based on PS (Pre-PS), intuitive secure scheme based on TS (Int-TS) and precoded secure scheme based on TS (Pre-TS), respectively. The secure performances of the proposed schemes are evaluated through the metric of probability of successfully secure transmission ( P S S T ), which represents the probability that the interference constraint of the primary user is satisfied and the secrecy rate is positive. P S S T is analyzed for the proposed secure schemes, and the closed form expressions of P S S T for Pre-PS and Pre-TS are derived and validated through simulation results. Numerical results show that the precoded secure schemes have better P S S T than the intuitive secure schemes under similar power consumption. When the secure schemes based on PS and TS polices have similar P S S T , the average transmit power consumption of the secure scheme based on TS is lower. The influences of power splitting and time slitting ratios are also discussed through simulations.

Interworking Universal Mobile Telecommunication System (UMTS) and IEEE 802.11 Wireless Local Area Networks (WLANs) introduce new challenges including the design of secured and fast handover protocols. Handover operations within and between networks must not compromise the security of the networks involved. In addition, handovers must be instantaneous to sustain the quality of service (QoS) of the applications running on the User Equipment (UE). There is a need to design fast and secured handover protocols to operate in UMTS-WLAN interworking architectures. This paper proposes two secured pre-authentication protocols in the UMTS-WLAN interworking architectures. Performance analysis of the proposed protocols show superior results in comparison to existing protocols in terms of authentication signaling cost, authentication delay and load on critical nodes involved in the authentication procedure. Additionally, the security of the proposed protocols was verified by the Automated Validation of Internet Security Protocols and Applications (AVISPA) security analyzer.

The IEEE 802.21 framework for Media Independent Handover (MIH) provides seamless vertical handover support for multimode mobile terminals. MIH messages are exchanged over various wireless media between mobile terminals and access networks to facilitate seamless handover. This calls for the need to secure MIH messages against network security threats in the wireless medium. In this paper, we first analyze IPSec/IKEv2 and DTLS security solution for secure MIH message transport. We show that handover latency can be an impediment to the use of IPSec and DTLS solutions. To overcome the handover overhead and hence minimize authentication time, a new secure MIH message transport solution, referred as MIHSec in this paper, is proposed. Experimental results are obtained for MIH between WLAN and Ethernet networks and the impacts of MIH message security on the handover latency are evaluated for IPSec, DTLS, and MIHSec security solutions. The effectiveness of MIHSec is demonstrated.