Joint Device Charging and Fresh Data Retrieval With Mobile Edge Device in Wireless-Powered IoT Systems

被引:0
|
作者
Qiu, Xiaoxing [1 ]
Fu, Chenchen [1 ]
Wu, Weiwei [1 ]
Zhou, Zining [1 ]
Sun, Sujunjie [1 ]
Song, Yuanyuan [1 ]
Han, Song [2 ]
机构
[1] Southeast Univ, Dept Comp Sci & Engn, Nanjing 210096, Peoples R China
[2] Univ Connecticut, Dept Comp Sci & Engn, Storrs, CT 06029 USA
基金
中国国家自然科学基金;
关键词
Task analysis; Real-time systems; Trajectory; Data integrity; Performance evaluation; Consumer electronics; Wireless communication; Real-time data retrieval; RF energy harvesting; speed-adjustable mobile edge device; CYBER-PHYSICAL SYSTEMS; TIME DATA-RETRIEVAL; FRAMEWORK;
D O I
10.1109/TCE.2024.3419128
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Introducing mobile edge devices in IoT systems for real-time data retrieval can reduce energy consumption and human interaction, and thus has attracted significant research attentions in recent years. In the meanwhile, leveraging mobile edge device(s) to charge the sensor devices in proximity through RF-based Wireless Energy Transfer (WET) technologies can provide controllable and stable energy supply, and is being increasingly deployed in the field. Based on these two recent trends, this work aims to formulate and solve the joint device charging and fresh data retrieval problem where a speed-adjustable mobile edge device judiciously powers a set of sensor devices in emergent energy shortage through WET while retrieving designated data items through multi-hop transmissions. To this end, an efficient 3-phase method for Joint Charging and Retrieving (3JCR) is proposed. 3JCR first identifies the sensor devices to be charged and develops the moving trajectory of the mobile edge device. It then applies a max-flow min-cost based clustering scheme to determine the routing path of the required data items to be transmitted to the mobile edge device, and finally constructs the data retrieval schedule and adjusts the speed of the mobile edge device in the run time. Our extensive experimental results show that 3JCR outperforms all state-of-the-art methods in terms of the number of feasibly retrieved data items, while achieving similar node survival rate.
引用
收藏
页码:7385 / 7397
页数:13
相关论文
共 50 条
  • [21] Computation Efficiency in A Wireless-Powered Mobile Edge Computing Network with NOMA
    Zhou, Fuhui
    Wu, Yongpeng
    Hu, Rose Qingyang
    Qian, Yi
    ICC 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2019,
  • [22] Mobile Charging in Wireless-Powered Sensor Networks: Optimal Scheduling and Experimental Implementation
    Sangare, Fahira
    Xiao, Yong
    Niyato, Dusit
    Han, Zhu
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2017, 66 (08) : 7400 - 7410
  • [23] Intelligent Time Allocation for Wireless Power Transfer in Wireless-Powered Mobile Edge Computing
    Dong, Xiaogang
    Wan, Zheng
    Deng, Changshou
    Wen, Wenying
    Luo, Yuxuan
    Wireless Communications and Mobile Computing, 2022, 2022
  • [24] Intelligent Time Allocation for Wireless Power Transfer in Wireless-Powered Mobile Edge Computing
    Dong, Xiaogang
    Wan, Zheng
    Deng, Changshou
    Wen, Wenying
    Luo, Yuxuan
    WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022, 2022
  • [25] Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems
    Zhou, Fuhui
    Wu, Yongpeng
    Hu, Rose Qingyang
    Qian, Yi
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2018, 36 (09) : 1927 - 1941
  • [26] Collaborative Transmission and Resource Management in IRS-Aided Wireless-Powered Mobile Edge Computing Systems
    Cao, Xueyan
    Sun, Kai
    Wang, Shubin
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (23): : 37693 - 37707
  • [27] A Mobile Edge Computing Device to Support Data Collecting and Processing from IoT
    Lee, Youngjae
    Kim, Wonjong
    Moon, Kiyoung
    Lim, Kiltaek
    2019 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC), 2019, : 423 - 425
  • [28] Intelligent Online Computation Offloading for Wireless-Powered Mobile-Edge Computing
    Wang, Yanting
    Qian, Zhuo
    He, Lijun
    Yin, Rui
    Wu, Celimuge
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (17): : 28960 - 28974
  • [29] Covertness and Timeliness of Data Collection in UAV-Aided Wireless-Powered IoT
    Lu, Xingbo
    Yang, Weiwei
    Yan, Shihao
    Li, Zan
    Ng, Derrick Wing Kwan
    IEEE INTERNET OF THINGS JOURNAL, 2021, 9 (14): : 12573 - 12587
  • [30] Joint Data Transmission and Trajectory Optimization in UAV-Enabled Wireless Powered Mobile Edge Learning Systems
    Liu, Jianxin
    Xu, Zhiguo
    Wen, Zhigang
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2023, 72 (09) : 11617 - 11630