Table of Content

    25 June 2021, Volume 19 Issue 2
    Special Topic
    Editorial: Special Topic onEdge Intelligence for Internet of Things
    2021, 19(2):  1-1.  doi:10.12142/ZTECOM.202102001
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    RecCac: Recommendation-Empowered Cooperative Edge Caching for Internet of Things
    HAN Suning, LI Xiuhua, SUN Chuan, WANG Xiaofei, LEUNG Victor C. M.
    2021, 19(2):  2-10.  doi:10.12142/ZTECOM.202102002
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    Edge caching is an emerging technology for supporting massive content access in mobile edge networks to address rapidly growing Internet of Things (IoT) services and content applications. However, the edge server is limited with the computation/storage capacity, which causes a low cache hit. Cooperative edge caching jointing neighbor edge servers is regarded as a promising technique to improve cache hit and reduce congestion of the networks. Further, recommender systems can provide personalized content services to meet user’s requirements in the entertainment-oriented mobile networks. Therefore, we investigate the issue of joint cooperative edge caching and recommender systems to achieve additional cache gains by the soft caching framework. To measure the cache profits, the optimization problem is formulated as a 0–1 Integer Linear Programming (ILP), which is NP-hard. Specifically, the method of processing content requests is defined as server actions, we determine the server actions to maximize the quality of experience (QoE). We propose a cache-friendly heuristic algorithm to solve it. Simulation results demonstrate that the proposed framework has superior performance in improving the QoE.

    Cost-Effective Task Scheduling for Collaborative Cross-Edge Analytics
    ZHAO Kongyange, GAO Bin, ZHOU Zhi
    2021, 19(2):  11-19.  doi:10.12142/ZTECOM.202102003
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    Collaborative cross-edge analytics is a new computing paradigm in which Internet of Things (IoT) data analytics is performed across multiple geographically dispersed edge clouds. Existing work on collaborative cross-edge analytics mostly focuses on reducing either analytics response time or wide-area network (WAN) traffic volume. In this work, we empirically demonstrate that reducing either analytics response time or network traffic volume does not necessarily minimize the WAN traffic cost, due to the price heterogeneity of WAN links. To explicitly leverage the price heterogeneity for WAN cost minimization, we propose to schedule analytic tasks based on both price and bandwidth heterogeneities. Unfortunately, the problem of WAN cost minimization underperformance constraint is shown non-deterministic polynomial (NP)-hard and thus computationally intractable for large inputs. To address this challenge, we propose price- and performance-aware geo-distributed analytics (PPGA) , an efficient task scheduling heuristic that improves the cost-efficiency of IoT data analytic jobs across edge datacenters. We implement PPGA based on Apache Spark and conduct extensive experiments on Amazon EC2 to verify the efficacy of PPGA.

    BPPF: Bilateral Privacy-Preserving Framework for Mobile Crowdsensing
    LIU Junyu, YANG Yongjian, WANG En
    2021, 19(2):  20-28.  doi:10.12142/ZTECOM.202102004
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    With the emergence of mobile crowdsensing (MCS), merchants can use their mobile devices to collect data that customers are interested in. Now there are many mobile crowdsensing platforms in the market, such as Gigwalk, Uber and Checkpoint, which publish and select the right workers to complete the task of some specific locations (for example, taking photos to collect the price of goods in a shopping mall). In mobile crowdsensing, in order to select the right workers, the platform needs the actual location information of workers and tasks, which poses a risk to the location privacy of workers and tasks. In this paper, we study privacy protection in MCS. The main challenge is to assign the most suitable worker to a task without knowing the task and the actual location of the worker. We propose a bilateral privacy protection framework based on matrix multiplication, which can protect the location privacy between the task and the worker, and keep their relative distance unchanged.

    Maximum-Profit Advertising Strategy Using Crowdsensing Trajectory Data
    LOU Kaihao, YANG Yongjian, YANG Funing, ZHANG Xingliang
    2021, 19(2):  29-43.  doi:10.12142/ZTECOM.202102005
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    Out-door billboard advertising plays an important role in attracting potential customers. However, whether a customer can be attracted is influenced by many factors, such as the probability that he/she sees the billboard, the degree of his/her interest, and the detour distance for buying the product. Taking the above factors into account, we propose advertising strategies for selecting an effective set of billboards under the advertising budget to maximize commercial profit. By using the data collected by Mobile Crowdsensing (MCS), we extract potential customers’ implicit information, such as their trajectories and preferences. We then study the billboard selection problem under two situations, where the advertiser may have only one or multiple products. When only one kind of product needs advertising, the billboard selection problem is formulated as the probabilistic set coverage problem. We propose two heuristic advertising strategies to greedily select advertising billboards, which achieves the expected maximum commercial profit with the lowest cost. When the advertiser has multiple products, we formulate the problem as searching for an optimal solution and adopt the simulated annealing algorithm to search for global optimum instead of local optimum. Extensive experiments based on three real-world data sets verify that our proposed advertising strategies can achieve the superior commercial profit compared with the state-of-the-art strategies.

    Speed Estimation Using Commercial Wi-Fi Device in Smart Home
    TIAN Zengshan, YE Chenglin, ZHANG Gongzhui, HE Wei, JIN Yue
    2021, 19(2):  44-52.  doi:10.12142/ZTECOM.202102006
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    With the development of Internet of Things (IoT), the speed estimation technology has attracted significant attention in the field of indoor security, intelligent home and personalized service. Due to the indoor multipath propagation, the speed information is implicit in the motion-induced reflected signal. Thus, the wireless signal can be leveraged to measure the speed of moving target. Among existing speed estimation approaches, users need to either carry a specialized device or walk in a predefined route. Wi-Fi based approaches provide an alternative solution in a device-free way. In this paper, we propose a direction independent indoor speed estimation system in terms of Electromagnetic (EM) wave statistical theory. Based on the statistical characteristics of EM waves, we establish the deterministic relationship between the Autocorrelation Function (ACF) of Channel State Information (CSI) and the speed of a moving target. Extensive experiments show that the system achieves a median error of 0.18 m/s for device-free single target walking speed estimation.

    Analysis of Industrial Internet of Things and Digital Twins
    TAN Jie, SHA Xiubin, DAI Bo, LU Ting
    2021, 19(2):  53-60.  doi:10.12142/ZTECOM.202102007
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    The industrial Internet of Things (IIoT) is an important engine for manufacturing enterprises to provide intelligent products and services. With the development of IIoT, more and more attention has been paid to the application of ultra-reliable and low latency communications (URLLC) in the 5G system. The data analysis model represented by digital twins is the core of IIoT development in the manufacturing industry. In this paper, the efforts of 3GPP are introduced for the development of URLLC in reducing delay and enhancing reliability, as well as the research on little jitter and high transmission efficiency. The enhanced key technologies required in the IIoT are also analyzed. Finally, digital twins are analyzed according to the actual IIoT situation.

    Research Paper
    Higher Speed Passive Optical Networks for Low Latency Services
    ZHANG Weiliang, YUAN Liquan
    2021, 19(2):  61-66.  doi:10.12142/ZTECOM.202102008
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    Latency sensitive services have attracted much attention lately and imposed stringent requirements on the access network design. Passive optical networks (PONs) provide a potential long-term solution for the underlying transport network supporting these services. This paper discusses latency limitations in PON and recent progress in PON standardization to improve latency. Experimental results of a low latency PON system are presented as a proof of concept.

    Differentially Authorized Deduplication System Based on Blockchain
    ZHAO Tian, LI Hui, YANG Xin, WANG Han, ZENG Ming, GUO Haisheng, WANG Dezheng
    2021, 19(2):  67-76.  doi:10.12142/ZTECOM.202102009
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    In architecture of cloud storage, the deduplication technology encrypted with the convergent key is one of the important data compression technologies, which effectively improves the utilization of space and bandwidth. To further refine the usage scenarios for various user permissions and enhance user’s data security, we propose a blockchain-based differential authorized deduplication system. The proposed system optimizes the traditional Proof of Vote (PoV) consensus algorithm and simplifies the existing differential authorization process to realize credible management and dynamic update of authority. Based on the decentralized property of blockchain, we overcome the centralized single point fault problem of traditional differentially authorized deduplication system. Besides, the operations of legitimate users are recorded in blocks to ensure the traceability of behaviors.

    A Novel De-Embedding Technique of Packaged GaN Transistors
    WEI Xinghui, CHEN Xiaofan, CHEN Wenhua, ZHOU Junmin
    2021, 19(2):  77-81.  doi:10.12142/ZTECOM.202102010
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    This paper presents a novel de-embedding technique of packaged high-power transistors. With the proposed technique, the packaged model of the power amplifier (PA) tube can be divided into the frequency independent de-embedded intrinsic device (DID) and the frequency dependent internal parasitic network (IPN), which is of great help in reducing the design complexity of a broadband PA. Different from the conventional technique of parasitic extraction, the proposed technique only requires external measurements. The frequency independent characteristic of DID is verified and the IPN is modeled and calibrated for a 50 W gallium-nitride (GaN) transistor. At last, a broadband Doherty PA is fabricated with the de-embedding technique. According to the measured results, the PA exhibits satisfactory power and efficiency performance.

    Flexible Multiplexing Mechanism for Coexistence of URLLC and EMBB Services in 5G Networks
    XIAO Kai, LIU Xing, HAN Xianghui, HAO Peng, ZHANG Junfeng, ZHOU Dong, WEI Xingguang
    2021, 19(2):  82-90.  doi:10.12142/ZTECOM.202102011
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    5G mobile networks are envisioned to support both evolved mobile broadband (eMBB) and ultra-reliable and low latency communications (URLLC), which may coexist and interfere with each other in the same service cell in many scenarios. In this paper, we propose a dynamic 2-dimension bitmap resource indication to cancel eMBB services with a finer uplink cancellation granularity and a lower probability of false cancellation. Meanwhile, a resource indication based power control method is introduced to dynamically indicate different power control parameters to the user equipment (UE) based on different time-frequency resource groups and the proportion of overlapping resources, by which the reliability of URLLC transmission is guaranteed while the impact on the performance of the eMBB service is minimized. Furthermore, a dynamic selection mechanism is proposed to accommodate the varying cases in different scenarios. Extensive system level simulations are conducted and the results show that about 10.54% more URLLC UE satisfy the requirements, and the perceived throughput of eMBB UE is increased by 23.26%.