Distributed Slice Selection-Based Computation Offloading for Intelligent Vehicular Networks

Distributed artificial intelligence (AI) is becoming an efficient approach to fulfill the high and diverse requirements for future vehicular networks. However, distributed intelligence tasks generated by vehicles often require diverse resources. A customized resource provision scheme is required to improve the utilization of multi-dimensional resources. In this work, a slice selection-based online offloading (SSOO) algorithm is proposed for distributed intelligence in future vehicular networks. First, the response time and energy consumption are reduced for processing tasks locally on the vehicles. Then, the offloading overheads, including latency and energy consumption, are calculated by considering the available resource amount, wireless channel states and vehicle conditions. The slice selection results is obtained by the deep reinforcement learning (DRL)-based method. Based on the selection solution, resource allocation results are achieved by KKT conditions and bisection method. Finally, the experimental results depict that the proposed SSOO algorithm outperforms other comparing algorithms in terms of energy consumption and task completion rate.

Automated summary

The paper proposes a slice selection-based online offloading (SSOO) algorithm for distributed intelligence in future vehicular networks, which improves response time and energy consumption by processing tasks locally on vehicles and calculating offloading overheads based on available resources, wireless channel states, and vehicle conditions. Slice selection results are obtained through deep reinforcement learning (DRL) and resource allocation results are achieved using KKT conditions and bisection method. Experimental results show that the SSOO algorithm outperforms other comparing algorithms in terms of energy consumption and task completion rate.