Energy-Efficient Resource Scheduling and Computation Offloading Strategy for Solar-Powered Agriculture WSN
IoT-based smart agriculture plays a significant role in building a high-yield, sustainable, and intelligent modern agriculture. However, limited battery capacity and low-power processors of sensors cannot accommodate the exponential expansion of data from smart agriculture sensing terminals. To over...
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| Veröffentlicht in: | Journal of sensors 2023-04, Vol.2023 (1) |
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| creator | Gao, Juan Wu, Runze Hao, Jianhong Xu, Chen Guo, Haobo Wang, Haonan |
| description | IoT-based smart agriculture plays a significant role in building a high-yield, sustainable, and intelligent modern agriculture. However, limited battery capacity and low-power processors of sensors cannot accommodate the exponential expansion of data from smart agriculture sensing terminals. To overcome the challenges, we introduced solar harvesting and multiaccess edge computing (MEC) to investigate sustainable monitoring of smart agriculture in solar-powered MEC-enabled WSNs. Considering the cyclical and day-night fluctuations of solar energy, we formulate a joint optimization problem for resource scheduling and computation offloading strategy to maximize the minimum weighted computation capacity across the time slots under solar energy constraints. To solve the mixed-integer nonlinear program (MINLP), we propose a multiply-iterated decoupling optimization algorithm by jointly optimizing a computation offloading strategy, energy provision of the solar-powered hybrid access point (HAP), and local CPU frequency as well as time scheduling. Simulation results show that the proposed algorithm can efficiently use solar energy to balance network calculations, improve network energy efficiency, and realize unmanned and sustainable agricultural WSN. |
| doi_str_mv | 10.1155/2023/7020104 |
| format | Article |
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However, limited battery capacity and low-power processors of sensors cannot accommodate the exponential expansion of data from smart agriculture sensing terminals. To overcome the challenges, we introduced solar harvesting and multiaccess edge computing (MEC) to investigate sustainable monitoring of smart agriculture in solar-powered MEC-enabled WSNs. Considering the cyclical and day-night fluctuations of solar energy, we formulate a joint optimization problem for resource scheduling and computation offloading strategy to maximize the minimum weighted computation capacity across the time slots under solar energy constraints. To solve the mixed-integer nonlinear program (MINLP), we propose a multiply-iterated decoupling optimization algorithm by jointly optimizing a computation offloading strategy, energy provision of the solar-powered hybrid access point (HAP), and local CPU frequency as well as time scheduling. Simulation results show that the proposed algorithm can efficiently use solar energy to balance network calculations, improve network energy efficiency, and realize unmanned and sustainable agricultural WSN.</description><identifier>ISSN: 1687-725X</identifier><identifier>EISSN: 1687-7268</identifier><identifier>DOI: 10.1155/2023/7020104</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Agriculture ; Algorithms ; Alternative energy sources ; Cloud computing ; Communication ; Computation offloading ; Decoupling ; Design ; Edge computing ; Energy consumption ; Energy efficiency ; Harvesting ; Information returns ; Maintenance costs ; Mixed integer ; Mobile computing ; Optimization ; Random variables ; Renewable resources ; Resource scheduling ; Scheduling ; Sensors ; Solar energy ; Wireless sensor networks</subject><ispartof>Journal of sensors, 2023-04, Vol.2023 (1)</ispartof><rights>Copyright © 2023 Juan Gao et al.</rights><rights>Copyright © 2023 Juan Gao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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However, limited battery capacity and low-power processors of sensors cannot accommodate the exponential expansion of data from smart agriculture sensing terminals. To overcome the challenges, we introduced solar harvesting and multiaccess edge computing (MEC) to investigate sustainable monitoring of smart agriculture in solar-powered MEC-enabled WSNs. Considering the cyclical and day-night fluctuations of solar energy, we formulate a joint optimization problem for resource scheduling and computation offloading strategy to maximize the minimum weighted computation capacity across the time slots under solar energy constraints. To solve the mixed-integer nonlinear program (MINLP), we propose a multiply-iterated decoupling optimization algorithm by jointly optimizing a computation offloading strategy, energy provision of the solar-powered hybrid access point (HAP), and local CPU frequency as well as time scheduling. 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| subjects | Agriculture Algorithms Alternative energy sources Cloud computing Communication Computation offloading Decoupling Design Edge computing Energy consumption Energy efficiency Harvesting Information returns Maintenance costs Mixed integer Mobile computing Optimization Random variables Renewable resources Resource scheduling Scheduling Sensors Solar energy Wireless sensor networks |
| title | Energy-Efficient Resource Scheduling and Computation Offloading Strategy for Solar-Powered Agriculture WSN |
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