SWIPT-based Real-Time Mobile Computing Systems: A Stochastic Geometry Perspective

TL;DR

This paper explores the use of SWIPT in real-time mobile computing systems to enable energy-efficient, battery-free operation of low-power IoT devices, using stochastic geometry for system-level analysis.

Contribution

It introduces a stochastic geometry framework to analyze the rate-energy trade-off in SWIPT-enabled real-time mobile computing networks, linking network parameters to CPU energy consumption.

Findings

Network densification improves CPU performance.

Propagation environment significantly affects energy harvesting.

Optimal trade-offs depend on device density and channel conditions.

Abstract

Driven by the Internet of Things vision, recent years have seen the rise of new horizons for the wireless ecosystem in which a very large number of mobile low power devices interact to run sophisticated applications. The main hindrance to the massive deployment of low power nodes is most probably the prohibitive maintenance cost of battery replacement and the ecotoxicity of the battery production/end-of-life. An emerging research direction to avoid battery replacement is the combination of radio frequency energy harvesting and mobile computing (MC). In this paper, we propose the use of simultaneous information and power transfer (SWIPT) to control the distributed computation process while delivering power to perform the computation tasks requested. A real-time MC system is considered, meaning that the trade-off between the information rate and the energy harvested must be carefully chosen to guarantee that the CPU may perform tasks of given complexity before receiving a new control signal. In order to provide a system-level perspective on the performance of SWIPT-MC networks, we propose a mathematical framework based on stochastic geometry to characterise the rate-energy trade-off of the system. The resulting achievable performance region is then put in relation with the CPU energy consumption to investigate the operating conditions of real-time computing systems. Finally, numerical results illustrate the joint effect of the network densification and the propagation environment on the optimisation of the CPU usage.