Wireless Big Data Meets WBANs: An Attempt for Cooperative Task Process Assisted with MEC

With the rapid developments of wireless body area networks (WBANs), the main purpose of this emerging technology has been transformed from remote healthcare monitoring to resource-hungry interactive entertainment services such as AR/VR applications, which makes both WBANs and user equipment (UE) struggle to process the computation intensive and latency sensitive applications in real-time. This paper proposes a cooperative computation architecture that can handle the wireless big data applications by taking the advantages of WBANs and mobile edge computing (MEC). The access point (AP) in WBANs is integrated with remote radio head (RRH), which is capable of executing the high latency and low computation tasks, while MEC server can handle the low latency computation-intensive tasks. After introducing the proposed model, task classification, task offloading priority and AP offloading decision algorithms are given. Numerical results show that the proposed solution has significantly improved the network lifetime and the total number of successfully executed tasks, as compared with the existing relay-enabled task offloading scheme. Moreover, UEs with higher computation capacity promises a lower network average service latency.