CA-PSO: A Combinatorial Auction and Improved Particle Swarm Optimization based Computation Offloading Approach for E-Healthcare

As one of the enabling technologies for E-Health, Internet of Medical Things (IoMT) interconnects various medical devices to collect and exchange healthcare information. To enable low-delay healthcare information processing, Mobile edge computing (MEC) can be incorporated in IoMT which can process various data in proximity of the medical devices. In this paper, we propose a Combinatorial Auction and Improved Particle Swarm Optimization based Computation Offloading Approach (CA-PSO) for e-healthcare to meet the Quality of Service (QoS) requirements of low delay and low energy consumption in healthcare monitoring. Firstly, we formulate a joint optimization problem to minimize the system cost consisting of delay and energy consumption, and transform this problem into a potential game. Secondly, we use combinatorial auction algorithm to analyze the offloading situation for different channels and servers, and combine channels and servers to simplify the original problem. Then we combine the offloading combination with improved Particle Swarm Optimization (PSO) to solve the optimal offloading strategy and server resource allocation. The simulation results show that compared with the comparison algorithm, the CA-PSO algorithm has achieved better performance in terms of average processing cost, delay, and energy consumption.