Fog-Assisted Multi-User SWIPT Networks: Local Computing or Offloading

TL;DR

This paper explores a fog-assisted multi-user SWIPT network where sensors choose between local computing and fog offloading to optimize energy use while meeting data and processing requirements.

Contribution

It formulates and solves a novel mixed integer programming problem for joint scheduling, power, and ratio optimization in SWIPT networks with fog computing.

Findings

Fog offloading is preferable when sensors are near the HAP or fog server.

Local computing is better when sensors are farther from the HAP and fog server.

Energy harvesting and battery storage reduce total sensor energy requirements.

Abstract

This paper investigates a fog computing-assisted multi-user simultaneous wireless information and power transfer (SWIPT) network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multi-user scheduling, the time assignment, the sensors' transmit powers and the PS ratios. Since the problem is a mixed integer programming (MIP) problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions and theoretical analysis. For a scheduled sensor, the closed-form and semi-closedform solutions to achieve its minimal required energy are derived, and then an efficient multi-user scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server (FS), the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.