# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 1 Introduction  
Recent technological developments have enabled various emerging biomedical and clinical applications such as transplanted organ monitoring and wireless capsule endoscopy image transmission that demand high a data rate and low-latency quality of service (QoS) requirements [1]. However, there exist numerous research challenges in this area. One of the most critical of these is how to handle the intensive computation tasks from implanted sensors promptly. Moreover, due to the technical constraint of the battery design, another research challenge is how to improve battery-powered relays' computation capacity when executing intensive computing tasks [2; 3; 4; 5]. Mobile edge computing (MEC) has been proposed as a promising candidate to replace cloud computing services when considering hospital/home-based healthcare scenarios [6; 7]. This is achieved by extending conventional monitoring to ambulatory monitoring, offering real-time feedback to patients by taking full advantage of the Internet of Things (IoT) relays in combination with an edge computing server. In contrast to cloud-like computing providers that transmit the task to a remote server that is possibly several thousand kilometers away from the patients, the MEC-based scheme is capable of executing intensive computing tasks locally or in a proximal edge server. However, the majority of current MEC research is focused on mobile communications and very little has been reported on healthcare-related scenarios. Direct transmission between the implanted sensor and the local coordinator suffers significant energy attenuation and thus decreases the network lifetime [8]. Moreover, since every piece of in-body information is critical when considering emergency intra-body data transmission, one should be aware that it is more efficient if the relays can execute the task from the implanted sensors when designing the system architecture. As a result, the patient can be notified of life-critical emergency medical information via the relay, which has user-friendly interaction [9; 10]. Furthermore, due to the limited computation resources of relays, they cannot execute all computing tasks from implanted sensors. As reported in Reference [11], a cloud or cloudlet with a substantial amount of computing resources that can accept offloaded tasks from user equipment (UE) such as smartphones can decrease UE computing energy consumption. However, this is not applicable when considering emergency medical scenarios where the computing server should be closed to the patient, so as to offer computing resources under abnormal medical situations. Moreover, the network should provide multiple available physiological signal monitoring services. To achieve the proposed targets, inspired by the MEC technique and IoT relays, the architecture of a wireless relay-enabled task offloading framework is proposed that can significantly improve the quality of the hospitalized patient monitoring services. Moreover, relay-based task offloading schemes are considered to address the research challenges related to computation intensive tasks and resource management. Furthermore, this work is motivated by the significant tradeoffs involving relays, considering different task offloading based schemes and various QoS metrics. Since the task offloading process consumes time regarding offloading decisions and transmission, a single-hop technique is adopted and the task offloads to the relay with a minimal cost function value if the relay cannot execute the task locally. Finally, if the emergency intra-body task is too large to be performed by relays locally, it will be offloaded to the C-MEC, which has higher computation capacity and power resources compared with on-body relays. In this paper, a novel framework of the hospitalized relay-enabled offload strategy for wireless biomedical implant systems is proposed. It is assumed that the computation resource of relays is limited and that the computation tasks can be executed by either the relays or the C-MEC.