LAUSR

Beam-based wireless power transfer and Fog/edge computing are promising dual technologies for realizing wireless powered Fog computing networks to support the upcoming B5G/6G IoT applications, which require latency-aware and intensive computing, with a limited energy supply. In such systems, IoT devices can either offload their computing tasks to the proximal Fog nodes or execute local computing with replenishing energy from the dedicated beamforming. However, effective integration of these techniques is still challenging, where two new issues arise: energy-aware task offloading and signal interferences from spillovers of wireless beamforming. In this paper, we observe that the beam-ripple phenomenon, which takes advantage of beamformer defects to transfer energy to IoT devices, is the key to jointly addressing these two issues. Different from traditional SWIPT technology, as in our approach the stream is not separately divided into data/energy streams, but target IoT devices can potentially harvest the whole stream. Inspired by this phenomenon, we treat the collaborative energy beamforming and edge computing design as a strongly NP-hard optimization problem. The proposed solution is an iterative algorithm to cascadingly integrate a polynomial-time (1 - 1/e)- approximation algorithm, which achieves the theoretical upper bound in approximation ratio unless P = NP, and an optimal dynamic programming algorithm. The numerical results show that the energy minimization goal among IoT devices can achieve, and the developed harvest-when-interfered protocol is practical in the wireless powered Fog computing networks.