Distributed Edge Intelligence Enabled Resource Control in IoV With Use Case in Emergency Healthcare Support
Xiaohong Lyu, Ashu Taneja, Shalli Rani, Yanhong Feng · IEEE Internet of Things Journal · 2024
Modern vehicles involve large number of sensors, cameras and communication systems for real-time traffic management, collision avoidance and vehicle health monitoring. As the Internet of Vehicles (IoV) ecosystem evolve with more number of connected vehicles, handling of the enormous data is a challenge. This is overcome with the promising distributed edge intelligence (DEI) approach in which the computational tasks are distributed among the intelligent road side units (RSUs) at the network edge. The edge servers cooperate among themselves so as not to overload the central cloud server. This article presents a cooperative vehicular communication network which exploits the existing 5G infrastructure in roadside building as the edge/relay nodes. To overcome the communication and energy overhead, network resource management is enabled through proposed edge node selection algorithm. Further, a joint edge node and antenna selection algorithm is proposed for enhanced energy efficiency (EE) and reduced outage. The closed-form expression for the outage probability of the proposed cooperative communication scheme is derived. Our analysis shows that the proposed selection approach achieves improved outage probability and energy-efficiency. In particular, the proposed edge node selection approach improves the EE by 10.46% at total transmit power to noise power ratio of 16 dB. Moreover, the overall system performance is further enhanced by proposing a joint selection scheme. Specifically, the analysis shows that the energy-efficiency improves by 27.87% with the joint selection scheme. In the end, a use case scenario of DEI empowered IoVs in emergency healthcare support is discussed along with the future research directions.