A Novel Task Offloading Framework to Support Wireless Body Area Networks with MEC

The emerging fifth-generation wireless networks aim at ensuring that various low latency wireless services can be timely and satisfactorily served in any time, any location, and any way. However, even with well-developed infrastructure-based wireless networks, it is still challenged to realize the goal of communicating in low latency for emerging medical services or entertainments such as virtual reality and augmented reality. In this paper, an enhanced computing architecture is proposed to handle the wireless big data applications by integrating the wireless body area networks (WBANs) and mobile edge computing (MEC). Specifically, access points in WBANs are located next to the remote radio heads, which are designed to handle the delay sensitive computation tasks, while MEC is assigned to execute tasks with high computation. UEs are divided into local execution and reschedule sets, according to the energy consumption, delay and channel condition requirements. Moreover, a task offloading request algorithm is proposed, tasks are offloaded to AP or MEC based on the latency requirements. Consider the limited computation resources of AP, a task arrangement algorithm is given, tasks with lower priority will be offloaded to MEC to execute. The results demonstrate that UEs with higher computation capacity achieve lower average service time and higher number of successfully executed tasks at the cost of the network lifetime.