Human Mobility-Driven Offloading and Resource Allocation in MEC-Enabled WBANs

Wireless body area networks (WBANs) have attracted significant attention as a key enabling technology within Internet of things, facilitating the realization of in-home health-care monitoring systems. However, due to the characteristics of limited energy resource and computing capability, it is difficult for WBANs to execute all computation tasks timely and effectively at home. In this paper, we devote to developing a human mobility-driven computational data offloading and resource allocation scheme based on mobile cloud computing and mobile edge computing (MEC) for WBANs. Technically, we consider a typical three-tier system architecture comprising one remote cloud server, multiple mobile edge servers (MESs), and various WBAN users. Subsequently, an optimization problem is formulated with the objective of minimizing the total cost in terms of data processing time and energy consumption. To investigate the impact of human mobility, the transmission time between the WBANs and MESs is first solved by a bisection method. After that, we investigate a joint data offloading and resource allocation algorithm based on differential evolution and Lagrange multiplier algorithm, called DE-LM, to minimize the data processing time, reduce the energy consumption of WBANs, and balance the workload of MESs. Extensive simulation results demonstrate that our proposed DE-LM scheme achieves superior performance in terms of the total cost and load balancing.