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

Wireless Body Area Networks (WBANs) have attracted considerable attentions as one of the key technologies in IoT, which can accelerate the implementation of in-home health-care monitoring. 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. Therefore, we devote this paper to developing a human mobility-driven computation data offloading and resource allocation scheme based on Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) for WBANs. Technically, we consider a typical three-tier system setup including one Remote Cloud Server (RCS), multiple Mobile Edge Servers (MESs) and various WBAN users. Then, an optimization problem with the objective to minimize the total cost in terms of the data processing time and energy consumption is formulated. To investigate the impact of human mobility, the transmission time between the WBANs and MESs is solved initially 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 performs best in terms of the total cost and load balancing.