Intelligent Reflecting Surface Aided Wireless Energy Transfer and Mobile Edge Computing for Public Transport Vehicles

TL;DR

This paper proposes an integrated model for future public transport vehicles combining Intelligent Reflecting Surfaces, Wireless Energy Transfer, and Mobile Edge Computing to enhance communication, energy recharging, and computational capabilities for onboard smart devices.

Contribution

It introduces a novel IRS-aided MEC-WET framework tailored for public transport vehicles, addressing challenges and demonstrating its effectiveness through simulation.

Findings

Enhanced signal strength via IRS improves energy and data transfer.

In-vehicle MEC reduces device latency and offloads processing tasks.

Simulation results confirm the model's effectiveness in F-PTV environments.

Abstract

In the forthcoming 6G era, Future Public transport vehicles (F-PTV), such as buses, trains, etc. will be designed to cater to the communication needs of the commuters that will carry numerous smart devices (smartphones, e-bands etc.). These battery-powered devices need frequent recharging. Since recharging facilities are not readily available while commuting, we envision F-PTVs that will provide in-vehicle recharging via Wireless Energy Transfer (WET) through in-vehicle Access Points. F-PTV will also be internally coated with Intelligent Reflecting Surface (IRS) that reflects incident radio waves towards the intended device to improve signal strength at the receiver, for both information and energy transmissions. F-PTVs will also be equipped with Mobile Edge Computing (MEC) servers to also serve multiple purposes, including a reduction in devices' task completion latency and offering in-vehicle Cloud services. MEC-server offloading will also relieve smart devices' processors off intensive tasks to preserve battery power. The challenges associated with the IRS-aided integrated MEC-WET model within F-PTV are also discussed. We present the simulation to show the effectiveness of such a model for F-PTV.