Online Collaborative Energy-Network Resource Scheduling for WPT-Enabled Green Edge Computing

The operations of IoT devices (IoTD) and 5G base station (BS) contribute to most of carbon emission and on-power energy consumption in wireless edge network. To reduce operational costs and achieve low-carbon computing, this paper investigates a long-term average operational expenditure (OPEX) minimization problem, and proposes an online joint energy-network resource scheduling algorithm, including computation offloading, wireless power transmission, energy sharing, and task migration in wireless edge network powered by renewable energy, energy storage, and power grid. Differing from existing works, ours consider the energy loss of battery, dynamic carbon emission related with on-power energy, and constraint of spatial electric network into our model. Then, we apply the Lyapunov technique to decompose the proposed problem into three real-time sub-problems. Furthermore, a federal gradient descent based full-distributed online algorithm is proposed to obtain solution while protecting the privacy of network operators. We also prove the convergence of proposed algorithm and provide the tradeoff between network stability and optimal OPEX. Simulation results reveal that the proposed algorithm outperforms existing benchmarks in reducing on-grid power dependence and OPEX.