Residual Energy Maximization-Based Resource Allocation in Wireless-Powered Edge Computing Industrial IoT

Industrial Internet of Things (IIoT) is a new stage for traditional industry to achieve intelligent development. However, the problems of limited resource, communication congestion, and capacity-constrained batteries have emerged due to massive wireless sensing devices (WSDs). With the recent advent...

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Vydáno v:IEEE internet of things journal Ročník 8; číslo 24; s. 17678 - 17690
Hlavní autoři: Xu, Haitao, Li, Qianqian, Gao, Hongjie, Xu, Xiaobin, Han, Zhu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 15.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Edge computing
Industrial applications
Industrial development
Industrial Internet of Things
Industrial Internet of Things (IIoT)
Internet of Things
Maximization
Mixed integer
Optimization
Power management
Residual energy
Resource allocation
Resource management
Sensors
Task analysis
whale optimization algorithm (WOA)
Wireless communication
wireless power transfer (WPT)
Wireless power transmission
Wireless sensor networks
ISSN:2327-4662, 2327-4662
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Popis
Shrnutí:Industrial Internet of Things (IIoT) is a new stage for traditional industry to achieve intelligent development. However, the problems of limited resource, communication congestion, and capacity-constrained batteries have emerged due to massive wireless sensing devices (WSDs). With the recent advent of the wireless power transfer (WPT) technique and the development of edge computing technology, the IIoT pays much attention to the interconnected, instant, and high-intelligent system. In this article, we first consider a three-layer architecture to describe the IIoT environment and we propose a resource allocation strategy aiming at maximizing the residual energy of WSDs. The optimization of residual energy is formulated as a mixed-integer nonconvex programming NP hard problem, by constraining the offloading decision, power allocation, computing resource allocation, and time allocation. In addition, an improved hybrid whale optimization algorithm (IHWOA) is proposed to search for the approximate optimal solution. Rapid convergent and stable efficient solution is obtained through simulation experiments. Finally, numerical results prove that the proposed solution achieves good performance.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2021.3082161