4.6 Article

Resource Allocation in Wireless Powered Virtualized Sensor Networks

Journal

IEEE ACCESS
Volume 8, Issue -, Pages 40327-40336

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2976476

Keywords

Wireless sensor networks; Resource management; Energy consumption; Wireless communication; Optimization; Sensors; Task analysis; Wireless sensor network; virtualization; wireless energy transfer; resource allocation

Funding

  1. National Natural Science Foundation of China [61901078, 61771082, 61871062]
  2. Science and Technology Research Program of Chongqing Municipal Education Commission [KJQN201900609]
  3. Doctoral Initial Funding of Chongqing University of Posts and Telecommunications [A2019-18]
  4. Program for Innovation Team Building at the Institutions of Higher Education in Chongqing [CXTDX201601020]

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As an important technology for Internet of Things, wireless sensor networks have been receiving widespread attention in recent years. Traditional wireless sensor network deploys resources for specific applications, which causes the problem of low resource utilization. Additionally, the energy consumption brought by the diversification of application requirements has also raised the burden on sensor nodes. Therefore, based on the introduction of wireless energy transfer technology, this paper proposes a resource allocation strategy for virtualized wireless sensor networks. Specifically, physical resources are pooled by the the wireless sensor network service provider. Then, virtual sensor networks are built through network slicing technology to provide one-to-one services based on the application requirements and the current status of the physical sensor nodes. Furthermore, in order to minimize the overall network energy consumption, a system-friendly resource allocation strategy is proposed to optimize the jointly configurations of sensing frequency, time slot and transmission power. Simulation results validate that the proposed strategy can not only effectively save the network energy, but also meet the diverse personalized needs of applications.

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