Based on a previous project "Renewable-energy-aware-video-download-cellular-networks", we expand our approach to heterogeneous networks consisting of RE-powered small cells and mobile edge computing (MEC) devices. The objective is to demonstrate the feasibility of small cells powered only by renewable energy, maximizing the longevity of such RE-powered small cells while ensuring satisfaction of application QoS and minimize service outage of edge-computing based services performed by the MECs associated with such small cells. We decided to focus on a demanding use case for small cells with MEC: vehicular edge computing for future assisted and autonomous vehicles.
In this emerging use case, solar-powered road side units (SRSUs), consisting of small cells with MEC, is to be used to provide ultra-low latency services to passing vehicles. The objective of our research is to develop techniques to minimize the QoS loss in terms of service outage for the vehicular edge computing applications. Our proposed approach is to mitigate the temporal and spatial mismatch of the solar power generation and power consumption of SRSUs through SRSUs’ battery charging/discharging management and vehicle association strategies. The proposed approach 1) schedules charging/discharging of the batteries of SRSUs based on prediction of the solar generation and the power consumption profile of SRSUs, 2) offloads users (vehicles) associated with SRSUs which are under power deficiency to neighboring SRSUs, and 3) minimizes the power consumption of each SRSU by communication and computation resource allocation given offloaded workloads. While this work can be extended to any vehicular MEC services, we consider an emerging critical assisted/autonomous vehicle service, MEC-enabled object detection, including video uploading, object detection processing and computation result downloading between SRSU and vehicles, as our MEC service.
This project is sponsored by: