Abstract: Mobile edge computing（MEC） has become one of the popular methods to solve the lack of computing resources in wireless body area networks（WBAN） in recent years. However, in the existing research work, the computing resources around the patients have not been fully utilized, which may easily cause network congestion. Under this circumstance, an efficient task offloading scheme is proposesd, combining cellular, WiFi network and device to device（D2D） communication, which makes full use of various computing resources in WBAN application scenarios, effectively reduces the load of cellular network and improves the reliability of the system. A low-complexity genetic algorithm is designed to obtain the minimum total unloading cost of the system under the condition of considering the patient’s delay, energy consumption and economical expenses at the same time. The experimental simulation results show that compared with random offloading, cellular offloading, offloading without WiFi, and offloading without D2D, the proposed scheme can more effectively reduce the total system cost and provide patients with higher service quality.

Key words: wireless body area network（WBAN）, mobile edge computing（MEC）, task offloading, genetic algorithm

摘要： 移动边缘计算（mobile edge computing，MEC）近年来成为解决无线体域网（wireless body area network，WBAN）计算资源匮乏的热门方法之一，但在现有的研究工作中，并没有将患者身边的计算资源充分利用起来，容易造成网络的拥堵。针对这种情况，提出了一种联合蜂窝、WiFi网络与设备到设备（device to device，D2D）通信的高效任务卸载方案，充分利用了WBAN应用场景中的多种计算资源，有效减少了蜂窝网络的负载，提高了系统的可靠性。设计了一种低复杂度的遗传算法，在同时考虑患者时延、能耗以及经济开销条件下，得到系统的最小卸载总成本。实验仿真结果表明，相比于随机卸载、蜂窝卸载、无WiFi卸载、无D2D卸载，该方案可以更有效降低系统总成本，为患者提供更高的服务质量。

关键词: 无线体域网（WBAN）, 移动边缘计算（MEC）, 任务卸载, 遗传算法