Multi-objective cooperative computation offloading for MEC in UAVs hybrid networks via integrated optimization framework

With the continuous expansion of the application field of the Internet of Things (IoT), mobile edge computing (MEC) is regarded as a promising technique to reduce the time-delay and energy-consumption of application. However, the conventional MEC infrastructure is lack of flexibility, and failed to meet the different requires of mobile device. UAVs have the distinct features of high scalability and mobility for communications, which can act as the complement of conventional MEC infrastructure. This paper investigates the issues of multi -objective cooperative computation offloading for MEC in UAVs hybrid Networks. The proposed UAVs hybrid MEC system enables edge-cloud and UAVs cooperation to address the flexible limitations of conventional MEC infrastructure and the efficient computation offloading of computation task. To support good-quality services in a cost-effective manner, we model the computation offloading problem as a multi-objective optimization process, and propose an intelligent computation offloading algorithms based on integrated optimization framework, including mixed integer transformation solving framework, improved multi-adaptive MOEA/D-DE(MOEA/D-MSDE) and Grey Relational Projection (GRP). Evaluation results show that the proposed algorithms outperform in solving multi-objective cooperative computation offloading problem in terms of service time-delay, energy-consumption and server-cost.