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    FEATURE TOPIC: SECURITY ISSUES IN EMERGING EDGE COMPUTING
  • FEATURE TOPIC: SECURITY ISSUES IN EMERGING EDGE COMPUTING
    An Overview of Privacy Preserving Schemes for Industrial Internet of Things
    Yan Huo, Chun Meng, Ruinian Li, Tao Jing
    2020, 17(10): 1-18.
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    The concept of Internet of Everything is like a revolutionary storm, bringing the whole society closer together. Internet of Things (IoT) has played a vital role in the process. With the rise of the concept of Industry 4.0, intelligent transformation is taking place in the industrial field. As a new concept, an industrial IoT system has also attracted the attention of industry and academia. In an actual industrial scenario, a large number of devices will generate numerous industrial datasets. The computing efficiency of an industrial IoT system is greatly improved with the help of using either cloud computing or edge computing. However, privacy issues may seriously harmed interests of users. In this article, we summarize privacy issues in a cloud- or an edge-based industrial IoT system. The privacy analysis includes data privacy, location privacy, query and identity privacy. In addition, we also review privacy solutions when applying software defined network and blockchain under the above two systems. Next, we analyze the computational complexity and privacy protection performance of these solutions. Finally, we discuss open issues to facilitate further studies.
  • FEATURE TOPIC: SECURITY ISSUES IN EMERGING EDGE COMPUTING
    Online Optimization of Physical-Layer Secure Computation Offloading in Dynamic Environments
    Chenshan Ren, Wei Song*, Lizhi Zhao, Xiaobing Zhao
    2020, 17(10): 19-30.
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    Mobile edge computing can provide powerful computation services around the end-users. However, given the broadcast nature of wireless transmissions, offloading the computation tasks via the uplink channels would raise serious security concerns. This paper proposes an online approach to jointly optimize local processing, transmit power, and task offloading decisions without the a-priori knowledge of the dynamic environments. The proposed approach can guarantee the secure offloading and asymptotically minimize the time-average energy consumption of devices while maintaining the stability of the ergodic secrecy queues and task queues. By exploiting the Lyapunov optimization, the local processing, transmit power, and task offloading variables can be decoupled between time slots. The subproblems on local processing and computation offloading can be solved separately. Convex optimization and graph matching can be used to solve the computation offloading subproblem. Simulations show that the performances of the proposed approach are superior to other popular approaches.
  • FEATURE TOPIC: SECURITY ISSUES IN EMERGING EDGE COMPUTING
    Mobility-Aware Partial Computation Offloading in Vehicular Networks: A Deep Reinforcement Learning Based Scheme
    Jianfei Wang, Tiejun Lv, Pingmu Huang, P. Takis Mathiopoulos
    2020, 17(10): 31-49.
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    Encouraged by next-generation networks and autonomous vehicle systems, vehicular networks must employ advanced technologies to guarantee personal safety, reduce traffic accidents and ease traffic jams. By leveraging the computing ability at the network edge, multi-access edge computing (MEC) is a promising technique to tackle such challenges. Compared to traditional full offloading, partial offloading offers more flexibility in the perspective of application as well as deployment of such systems. Hence, in this paper, we investigate the application of partial computing offloading in-vehicle networks. In particular, by analyzing the structure of many emerging applications, e.g., AR and online games, we convert the application structure into a sequential multi-component model. Focusing on shortening the application execution delay, we extend the optimization problem from the single-vehicle computing offloading (SVCOP) scenario to the multi-vehicle computing offloading (MVCOP) by taking multiple constraints into account. A deep reinforcement learning (DRL) based algorithm is proposed as a solution to this problem. Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.
  • FEATURE TOPIC: SECURITY ISSUES IN EMERGING EDGE COMPUTING
    When Edge Computing Meets IoT Systems: Analysis of Case Studies
    Fabio Arena, Giovanni Pau*
    2020, 17(10): 50-63.
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    On a macroscopic level, an edge computing architecture looks like a distributed and decentralized IT (Information Technology) architecture. More in detail, it could be defined as a mesh network of micro data centers capable of processing and storing critical data locally, and to transmit all data received and/or processed to a central data center or a cloud storage repository. This network topology, also taking advantage of the availability on the market of cost-effective small form factor (SFF) electronic components and systems decreasing, brings the essential components of processing, storage, and networking closer to the sources that generate the data. The typical use case is that of Internet of Things (IoT) devices and implementations, which often face latency problems, lack of bandwidth, reliability, which cannot be addressed through the conventional cloud model. In this context, the edge computing architecture can reduce the size of data to be sent to the cloud, processing critical data, sensitive to latency, at the point of origin, via a smart device, or sending it to an intermediate server, located nearby. The aim of this paper is to report some of the main aspects and significant features of edge computing and analyzing several popular case studies.
  • FEATURE TOPIC: SECURITY ISSUES IN EMERGING EDGE COMPUTING
    Secrecy Capacity Maximization for a UAV-Assisted MEC System
    Dongsheng Han*, Tianhao Shi
    2020, 17(10): 64-81.
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    Unmanned aerial vehicle (UAV) communication has attracted wide attentions in the mobile edge computing (MEC) system owing to its high-flexibility and simple operation auxiliary communication mode. Users can offload computing tasks to UAVs, which serves as edge nodes. Meanwhile, UAVs forward the tasks onto a cloud center or base station for processing, thereby shortening the implementation time of tasks. Nevertheless, the offloading links of an UAV-assisted MEC system adopt a radio broadcasting mode. Several eavesdroppers might be present in the environment to eavesdrop the data sent by users and UAVs, thereby causing significant effects on the secrecy performance. An optimized iterative algorithm is proposed in this paper to realize the maximum secrecy capacity of the MEC system and further improve the secrecy performance of an UAV-assisted MEC system and assure secrecy transmit. By doing so, the secrecy transmit problems of the two-staged offloading model of the UAV-assisted MEC system are solved. The maximum secrecy capacity of the system is obtained through joint optimization of the UAV positions, transmit power of the UAV, task offloading ratio, and allocation of offloading users considering the limited time and energy of an UAV. Simulation results demonstrate that the proposed iterative algorithm can effectively improve the secrecy capacity of the system.
  • FEATURE TOPIC: SECURITY ISSUES IN EMERGING EDGE COMPUTING
    Deeply Understanding Graph-Based Sybil Detection Techniques via Empirical Analysis on Graph Processing
    Jian Mao, Xiang Li, Qixiao Lin, Zhenyu Guan
    2020, 17(10): 82-96.
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    Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices, which expose serious threat to edge computing based distributed systems. Graph-based Sybil detection approaches extract social structures from target distributed systems, refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure. Graph preprocessing is a critical component in such Sybil detection methods, and intuitively, the processing methods will affect the detection performance. Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches. In this paper, we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments. The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.
    • FEATURE TOPIC: AI-EMPOWERED MILLIMETER WAVE COMMUNICATION AND NETWORKING
  • FEATURE TOPIC: AI-EMPOWERED MILLIMETER WAVE COMMUNICATION AND NETWORKING
    GUEST EDITORIAL
    2020, 17(10): 97-99.
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  • FEATURE TOPIC: AI-EMPOWERED MILLIMETER WAVE COMMUNICATION AND NETWORKING
    Machine Learning Empowered Beam Management for Intelligent Reflecting Surface Assisted MmWave Networks
    Chenglu Jia, Hui Gao, Na Chen, Yuan He
    2020, 17(10): 100-114.
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    Recently, intelligent reflecting surface (IRS) assisted mmWave networks are emerging, which bear the potential to address the blockage issue of the millimeter wave (mmWave) communication in a more cost-effective way. In particular, IRS is built by passive and programmable electromagnetic elements that can manipulate the mmWave propagation channel into a more favorable condition that is free of blockage via judicious joint base station (BS)-IRS transmission design. However, the coexistence of IRSs and mmWave BSs complicates the network architecture, and thus poses great challenges for efficient beam management (BM) that is one critical prerequisite for high performance mmWave networks. In this paper, we systematically evaluate the key issues and challenges of BM for IRS-assisted mmWave networks to bring insights into the future network design. Specifically, we carefully classify and discuss the extensibility and limitations of the existing BM of conventional mmWave towards the IRS-assisted new paradigm. Moreover, we propose a novel machine learning empowered BM framework for IRS-assisted networks with representative showcases, which processes environmental and mobility awareness to achieve highly efficient BM with significantly reduced system overhead. Finally, some interesting future directions are also suggested to inspire further researches.
  • FEATURE TOPIC: AI-EMPOWERED MILLIMETER WAVE COMMUNICATION AND NETWORKING
    Enhanced Reconfigurable Intelligent Surface Assisted mmWave Communication: A Federated Learning Approach
    Lixin Li, Donghui Ma, Huan Ren, Dawei Wang, Xiao Tang, Wei Liang, Tong Bai
    2020, 17(10): 115-128.
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    Reconfigurable intelligent surface (RIS) has been proposed as a potential solution to improve the coverage and spectrum efficiency for future wireless communication. However, the privacy of users’ data is often ignored in previous works, such as the user’s location information during channel estimation. In this paper, we propose a privacy-preserving design paradigm combining federated learning (FL) with RIS in the mmWave communication system. Based on FL, the local models are trained and encrypted using the private data managed on each local device. Following this, a global model is generated by aggregating them at the central server. The optimal model is trained for establishing the mapping function between channel state information (CSI) and RIS’ configuration matrix in order to maximize the achievable rate of the received signal. Simulation results demonstrate that the proposed scheme can effectively approach to the theoretical value generated by centralized machine learning (ML), while protecting user’ privacy.
  • FEATURE TOPIC: AI-EMPOWERED MILLIMETER WAVE COMMUNICATION AND NETWORKING
    Energy-Efficient UAV Trajectory Design for Backscatter Communication: A Deep Reinforcement Learning Approach
    Yiwen Nie, Junhui Zhao, Jun Liu, Jing Jiang, Ruijin Ding
    2020, 17(10): 129-141.
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    Recently, backscatter communication (BC) has been introduced as a green paradigm for Internet of Things (IoT). Meanwhile, unmanned aerial vehicles (UAVs) can serve as aerial base stations (BSs) to enhance the performance of BC system thanks to their high mobility and flexibility. In this paper, we investigate the problem of energy efficiency (EE) for an energy-limited backscatter communication (BC) network, where backscatter devices (BDs) on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor. Specifically, we first reformulate the EE optimization problem as a Markov decision process (MDP) and then propose a deep reinforcement learning (DRL) algorithm to design the UAV trajectory with the constraints of the BD scheduling, the power reflection coefficients, the transmission power, and the fairness among BDs. Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.
  • FEATURE TOPIC: AI-EMPOWERED MILLIMETER WAVE COMMUNICATION AND NETWORKING
    Throughput Maximization for Multi-UAV Enabled Millimeter Wave WPCN: Joint Time and Power Allocation
    Jiansong Miao, Pengjie Wang, Qian Zhang, Yue Wang
    2020, 17(10): 142-156.
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    In this paper, we investigate the effective deployment of millimeter wave (mmWave) in unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In particular, a novel framework for optimizing the performance of such UAV-enabled WPCN in terms of system throughput is proposed. In the considered model, multiple UAVs monitor in the air along the scheduled flight trajectory and transmit monitoring data to micro base stations (mBSs) with the harvested energy via mmWave. In this case, we propose an algorithm for jointly optimizing transmit power and energy transfer time. To solve the non-convex optimization problem with tightly coupled variables, we decouple the problem into more tractable subproblems. By leveraging successive convex approximation (SCA) and block coordinate descent techniques, the optimal solution is obtained by designing a two-stage joint iteration optimization algorithm. Simulation results show that the proposed algorithm with joint transmit power and energy transfer time optimization achieves significant performance gains over Q-learning method and other benchmark schemes.
    • COVER PAPER
  • COVER PAPER
    Generative Adversarial Network-Based Electromagnetic Signal Classification: A Semi-Supervised Learning Framework
    Huaji Zhou, Licheng Jiao, Shilian Zheng, Lifeng Yang, Weiguo Shen, Xiaoniu Yang
    2020, 17(10): 157-169.
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    Generative adversarial network (GAN) has achieved great success in many fields such as computer vision, speech processing, and natural language processing, because of its powerful capabilities for generating realistic samples. In this paper, we introduce GAN into the field of electromagnetic signal classification (ESC). ESC plays an important role in both military and civilian domains. However, in many specific scenarios, we can’t obtain enough labeled data, which cause failure of deep learning methods because they are easy to fall into over-fitting. Fortunately, semi-supervised learning (SSL) can leverage the large amount of unlabeled data to enhance the classification performance of classifiers, especially in scenarios with limited amount of labeled data. We present an SSL framework by incorporating GAN, which can directly process the raw in-phase and quadrature (IQ) signal data. According to the characteristics of the electromagnetic signal, we propose a weighted loss function, leading to an effective classifier to realize the end-to-end classification of the electromagnetic signal. We validate the proposed method on both public RML2016.04c dataset and real-world Aircraft Communications Addressing and Reporting System (ACARS) signal dataset. Extensive experimental results show that the proposed framework obtains a significant increase in classification accuracy compared with the state-of-the-art studies.
    • COMMUNICATIONS THEORIES & SYSTEMS
  • COMMUNICATIONS THEORIES & SYSTEMS
    Interpolation Method of Head-Related Transfer Functions Based on Common-Pole/Zero Modeling
    Wei Chen, Xiaochen Wang, Ruimin Hu, Gang Li, Weiping Tu
    2020, 17(10): 170-182.
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    The head-related transfer function (HRTF) involves the cues for human auditory localization, which turns it into an essential item of virtual auditory display technology. In practice, the interpolation of HRTF is necessary for the virtual auditory display systems to achieve high spatial resolution. Traditional geometric-based interpolation methods are generally restrained by the spatial distribution of reference on HRTF. When the spatial distribution is sparse, the accuracy of interpolation decreases significantly. Therefore, an interpolation method using the common-pole/zero model and the fitting neural network is proposed. First, we propose a common-pole/zero model to represent HRTFs across multiple subjects, in which the low-dimensional features of the measured HRTFs are extracted. Then, for a new spatial direction, we predict the corresponding low-dimensional HRTF with a fitting neural network. Finally, we reconstruct the high-dimensional HRTF from the predicted low-dimensional HRTF. The simulation results suggest that the proposed method outperforms other interpolation methods such as Linear_AMBC, Bilinear_AMBC, and the Combination method.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Detecting DNS Covert Channels Using Stacking Model
    Peng Yang, Ye Li, Yunze Zang
    2020, 17(10): 183-194.
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    A covert channel is an information channel that is used by the computer process to exfiltrate data through bypassing security policies. The DNS protocol is one of the important ways to implement a covert channel. DNS covert channels are easily used by attackers for malicious purposes. Therefore, an effective detection approach of the DNS covert channels is significant for computer systems and network securities. Aiming at the difficulty of the DNS covert channel identification, we propose a DNS covert channel detection method based on a stacking model. The stacking model is evaluated on a campus network and the experimental results show that the detection based on the stacking model can detect the DNS covert channels effectively. Besides, it can identify unknown covert channel traffic. The area under the curve (AUC) of the proposed method reaches 0.9901, which outperforms existing detection methods.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Ensemble of High Performance Structured Binary Convolutional LDPC Codes with Moderate Rates
    Liwei Mu*
    2020, 17(10): 195-205.
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    An algebraic construction methodology is proposed to design binary time-invariant convolutional low-density parity-check (LDPC) codes. Assisted by a proposed partial search algorithm, the polynomialform parity-check matrix of the time-invariant convolutional LDPC code is derived by combining some special codewords of an (n, 2, n - 1) code. The achieved convolutional LDPC codes possess the characteristics of comparatively large girth and given syndrome former memory. The objective of our design is to enable the time-invariant convolutional LDPC codes the advantages of excellent error performance and fast encoding. In particular, the error performance of the proposed convolutional LDPC code with small constraint length is superior to most existing convolutional LDPC codes.
  • COMMUNICATIONS THEORIES & SYSTEMS
    Wireless Network Architecture for Evacuated Tube Transportation System
    Li Han, Hao Wu, Xia Chen
    2020, 17(10): 206-217.
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    Evacuated Tube Transportation (ETT) systems have been claimed to have considerable strengths, including ultra-high-speed, safety, and environmentally-friendly. However, the frequent handover caused by the high-speed brings a challenge for ETT mobile wireless communication to preserve steady link performance. Moreover, in such a special scenario, the wireless link between the base station and the passengers on the train needs to experience fading from both metal pipe and train, thus the free-wave coverage with antennas in traditional high-speed rail wireless communication systems is not suitable for ETT. Based on the characteristics of ETT, an improved architecture of wireless communication network is proposed, using distributed base stations with remote radio units (RRUs) and baseband units (BBUs) and leaky waveguides to form stable coverage. And a redundant BBUs or RRUs structure is designed for coverage enhancement. Based on this redundant architecture, a fast handover scheme is proposed to resolve the handover problem. The analytical and simulation results show that the proposed scheme is capable of reducing communication outage probability and handover failure probability remarkably.
    • EMERGING TECHNOLOGIES & APPLICATIONS
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set
    Yiping Duan, Xiaoming Tao, Xijia Liu, Ning Ge
    2020, 17(10): 218-228.
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    In this paper, we build a remote-sensing satellite imagery priori-information data set, and propose an approach to evaluate the robustness of remote-sensing image feature detectors. The building TH Priori-Information (TPI) data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features. The TPI contains 1) raw and calibrated remote-sensing images with high spatial and temporal resolutions (up to 2 m and 7 days, respectively), and 2) a built-in 3-D target area model that supports view position, view angle, lighting, shadowing, and other transformations. Based on TPI, we further present a quantized approach, including the feature recurrence rate, the feature match score, and the weighted feature robustness score, to evaluate the robustness of remote-sensing image feature detectors. The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances. Three remote-sensing image feature detectors, including scale-invariant feature transform (SIFT), speeded up robust features (SURF), and priori information based robust features (PIRF), are evaluated using the proposed approach on the TPI data set. Experimental results show that the robustness of PIRF outperforms others by over 6.2%.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Evolutionary Dynamics Modeling of Symbolic Social Network Structure Equilibrium
    Weijin Jiang, Sijian Lv, Yirong Jiang, Jiahui Chen, Fang Ye, Xiaoliang Liu
    2020, 17(10): 229-240.
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    The use of symbol attributes on the side of symbolic social networks to analyze, understand, and predict the topology, function, and dynamic behaviour of complex networks, and has important theoretical significance for personalized recommendations, attitude prediction, user feature analysis, and clustering and application value. However, due to the huge scale of online social networks, this poses a challenge to traditional symbolic social network analysis methods. Based on the theory of structural equilibrium, this paper studies the evolutionary dynamics of symbolic social networks, proposes the energy function of weak structural equilibrium theory, and uses the evolution of evolutionary algorithms to obtain the weak imbalance of the network. The simulation experiment results show that the calculation method in this paper can get the optimal solution faster. It provides an idea for the study of real and complex social networks.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Dynamic Uplink Transmission Scheduling for Satellite Internet of Things Applications
    Li Wang, Shuaijun Liu, Weidong Wang, Zhiyan Fan
    2020, 17(10): 241-248.
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    Satellite communication systems provide a cost-effective solution for global internet of things (IoT) applications due to its large coverage and easy deployment. This paper mainly focuses on Satellite networks system, in which low earth orbit (LEO) satellites network collect sensing data from the user terminals (UTs) and then forward the data to ground station through geostationary earth orbit (GEO) satellites network. Considering the limited uplink transmission resources, this paper optimizes the uplink transmission scheduling scheme over LEO satellites. A novel transmission scheduling algorithm, which combined Algorithms of Simulated Annealing and Monte Carlo (SA-MC), is proposed to achieve the dynamic optimal scheduling scheme. Simulation results show the effectiveness of the proposed SA-MC algorithm in terms of cost value reduction and fast convergence.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Optimal Energy Efficiency Resource Allocation Strategy for Cognitive Clustering Network under PUEA Attack
    Linna Hu, Ning Cao, Rui Shi, Xue Cai, Minghe Mao, Zhiyu Chen
    2020, 17(10): 249-263.
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    5G has pushed the use of radio spectrum to a new level, and cognitive clustering network can effectively improve the utilization of radio spectrum, which is a feasible way to solve the growing demand for wireless communications. However, cognitive clustering network is vulnerable to PUEA attack, which will lead to the degradation of system detection performance, thereby reducing the energy efficiency. Aiming at these problems, this paper investigates the optimal energy efficiency resource allocation scheme for cognitive clustering network under PUEA attack. A cooperative user selection algorithm based on selection factor is proposed to effectively resist PUEA user attack and improve detection performance. We construct the energy efficiency optimization problem under multi-constraint conditions and transform the nonlinear programming problem into parametric programming problem, which is solved by Lagrangian function and Karush-Kuhn-Tucker condition. Then the sub-gradient iterative algorithm based on optimal energy efficiency under PUEA attack is proposed and its complexity is analyzed. Simulation results indicate that proposed method is effective when subjected to PUEA attacks, and the impact of different parameters on energy efficiency is analyzed.