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The increasing amount of user equipment (UE) and the rapid advances in wireless body area networks bring revolutionary changes in healthcare systems. However, due to the strict requirements on size, reliability and battery lifetime of UE devices, it is difficult for them to execute latency sensitive or computation intensive tasks effectively. In this paper, we aim to enhance the UE computation capacity by utilizing small size coordinator-based mobile edge computing (C-MEC) servers. In this way, the system complexity, computation resources, and energy consumption are considerably transferred from the UE to the C-MEC, which is a practical approach since C-MEC is power charged, in contrast to the UE. First, the system architecture and the mobility model are presented. Second, several transmission mechanisms are analyzed along with the proposed mobility-aware cooperative task offloading scheme. Numerous selected performance metrics are investigated regarding the number of executed tasks, the percentage of failed tasks, average service time, and the energy consumption of each MEC. The results validate the advantage of task offloading schemes compared with the traditional relay-based technique regarding the number of executed tasks. Moreover, one can obtain that the proposed scheme archives noteworthy benefits, such as low latency and efficiently balance the energy consumption of C-MECs.

Inspired by the recent developments of the Internet of Things (IoT) relay and mobile edge computing (MEC), a hospital/home-based medical monitoring framework is proposed, in which the intensive computing tasks from the implanted sensors can be efficiently executed by on-body wearable devices or a coordinator-based MEC (C-MEC). In this paper, we first propose a wireless relay-enabled task offloading mechanism that consists of a network model and a computation model. Moreover, to manage the computation resources among all relays, a task offloading decision model and the best task offloading recipient selection function is given. The performance evaluation considers different computation schemes under the predetermined link quality condition regarding the selected vital quality of service (QoS) metrics. After demonstrating the channel characterization and network topology, the performance evaluation is implemented under different scenarios regarding the network lifetime of all relays, network residual energy status, total number of locally executed packets, path loss (PL), and service delay. The results show that data transmission without the offloading scheme outperforms the offload-based technique regarding network lifetime. Moreover, the high computation capacity scenario achieves better performance regarding PL and the total number of locally executed packets.

Network lifetime maximization of wireless biomedical implant systems is one of the major research challenges of wireless body area networks (WBANs). In this paper, a mutual information (MI)-based incremental relaying communication protocol is presented where several on-body relay nodes and one coordinator are attached to the clothes of a patient. Firstly, a comprehensive analysis of a system model is investigated in terms of channel path loss, energy consumption, and the outage probability from the network perspective. Secondly, only when the MI value becomes smaller than the predetermined threshold is data transmission allowed. The communication path selection can be either from the implanted sensor to the on-body relay then forwards to the coordinator or from the implanted sensor to the coordinator directly, depending on the communication distance. Moreover, mathematical models of quality of service (QoS) metrics are derived along with the related subjective functions. The results show that the MI-based incremental relaying technique achieves better performance in comparison to our previous proposed protocol techniques regarding several selected performance metrics. The outcome of this paper can be applied to intra-body continuous physiological signal monitoring, artificial biofeedback-oriented WBANs, and telemedicine system design.

The rapid growth in remote healthcare services and biomedical demands has seen novel developments in wireless body area networks (WBANs). The WBAN can be seen as an integration of intelligent networks, which permits devices and sensors to work together to obtain a series of critical physiological parameters, such as blood flow velocity and heartbeat frequency. Analysis of WBAN radio frequency communication systems is the key factor and the critical research challenge that determines system performance, such as achievable transmission distance, data rate and so forth. The human head is an area of particular potential in WBAN design that is worthy of attracting more attention than its limited literature to date. This paper is primarily focused on the one of the most detailed comprehensive multi-modal imaging-based anatomical human head models. This is a multimodal imaging-based detailed anatomical model, denoted by the acronym MIDA, this features 153 structures at a high resolution of up to 500 µm, including numerous distinct muscles, bones and skull layers in the license-free 2.4 GHz industrial, scientific, and medical (ISM) band. It presents and compares a set of advanced simulation methods and then proposes a path loss simulation flat phantom, semiempirical path loss models for typical homogeneous tissues and the anatomical human head MIDA model. The bit error rate (BER) performances of the MIDA model fading channel using binary phase shift keying (BPSK) and pulse-amplitude modulation (PAM) are obtained. Furthermore, achievable transmission distances for several data rates for predetermined acceptable BERs are accomplished. The results show that PAM promises longer transmission distances than BPSK when using both high and low data rates. The proposed communication systems can be applied to optimize implantation communication system scenarios and biotelemetry applications. [ABSTRACT FROM AUTHOR]