# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 2 Related Work  
Currently, the majority of patients' physiological signal monitoring services are based on note-taking, which is time-consuming and subject to inaccuracy. In addition, this method is unable to offer real-time data sensing and computing of the collected data to support the clinical diagnosis and decision-making process. One promising solution to handle the mentioned research challenges is to employ the WBAN techniques along with the utility computing methods [2; 6]. In this way, the collected data of the implanted devices can be transmitted to interconnected on-body UE or computing servers to be processed. The processed data are then distributed to the relevant medical professionals. Practically, the computation and wireless communication resources of relays are limited. Thus, the handling of intensive computation tasks is one of the critical research challenges in WBANs. The authors of References [12; 13] reported a cloud-based data stream execution technique in mobile cloud computing and extended the access method of cloud services to mobile devices such as smart wearable devices. The results show that the proposed technology enables mobile devices to execute mobile applications efficiently by employing cloud resources. However, this technique is not applicable in medical applications because data are uploaded to a remote cloud at a distance of many kilometers and thus suffer significant transmission delay. Numerous commercially available cloud platforms have been proposed, such as ThinkAir and Amazon elastic compute cloud, which are able to migrate the computational tasks from the UE to the cloud [14]. However, when the UE requires a cloud computation resource, one has to send the instructions and the tasks all the way via the Internet. As a result, the long-distance data transmission cannot meet the healthcare monitoring requirements regarding communication reliability, flexibility, and latency. [PERSON] et al. proposed a list of transmission algorithms regarding the uplink-based task offloading framework that can reduce the signaling between UE and the cloud [15]. However, the results proved that some UE might increase their transmission power to maintain the high data rate and this action may significantly increase other UE's packet loss and degrade the link quality. Another research challenge is that the traditional relay cannot offer sufficient computation or communication resources. [PERSON] et al. [16] demonstrated that wearable devices can perform as IoT relays because of their computation and communication capabilities. After the physiological data of the human body have been sensed, the task can be executed locally and the results can be promptly noticed by patients. As reported by [PERSON] et al. [17], data relaying processes require extra power and battery-operated IoT relays may exhaust quickly. These authors also pointed out that the network lifetime can be significantly increased by employing relay collaboration and efficient transmission scheduling techniques. However, this paper does not consider the relay selection scheme or the tradeoff between the system complexity and performance. Moreover, to overcome severe interference and inefficient use of the limited wireless communication resources of UE, small cell networks were recommended in Reference [18]. However, with less computational capacity compared with macro networks and local area networks, traditional small cell networks are likely unable to meet the low latency QoS requirement when a large number of relays offload the tasks simultaneously. Moreover, to increase the overall lifetime of all relays, relays with higher residual energy can accept as many as possible offloading requests from other relays by managing the communication and computation resource in the networks. However, there is no appropriate control mechanism or optimization techniques for this method when considering hospital/home-based medical monitoring scenarios.