Tasks Offloading for Connected Autonomous Vehicles in Edge Computing

Internet of vehicles (IoV) is gradually combined with connected autonomous vehicles (CAV), which accelerates the development of CAV. In order to meet the service requirements of CAV, mobile edge computing (MEC) provides IoV with a novel paradigm which provides services by fast processing vehicle tasks at the road side units distributed near target vehicles. In this way, vehicle tasks can be offloaded to edge servers deployed in road side units (RSU). A vehicle tasks offloading problem requires load balance of edge servers to be maintained with minimum total time cost. Thus, we proposed a vehicle tasks offloading method (VTO) in which the vehicle tasks offloading problem is formulated as a multi-objective optimization problem. Hence, we design a multi-objective optimization evolutionary algorithm basing on improving the strength pare to evolutionary algorithm (SPEA2) and technique for order preference by similarity to ideal solution (TOPSIS) and multiple criteria decision making (MCDM). Through theoretical analysis and experimental evaluation, the results shows that the performance of VTO is effective and efficient.

Automated summary

Internet of vehicles (IoV) is combined with connected autonomous vehicles (CAV) to accelerate CAV development, and mobile edge computing (MEC) provides a novel paradigm for IoV services. The vehicle tasks offloading problem requires maintaining load balance of edge servers, and our proposed VTO method optimizes this issue.