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 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.