基于云边协同和用户偏好的无线体域网通信与计算资源分配研究

The popularity of Wireless Body Area Networks (WBANs) is restricted by the limited network resources and the storage and computing capability of intelligent devices. Cloud computing and edge computing are urgently needed to meet the needs of mass health data with low-latency, low-power consumption, high-reliability and user personalized service requirements. The proposal integrates network communication and computing resources, studies the cloud-edge collaborative WBAN resource scheduling problem, aiming to reduce network overhead, improve resource utilization and improve user satisfaction. The research is carried out from the following three aspects: (1) Design a multi-attribute decision making based computation offloading strategy, facilitating load balancing and resource allocation; (2) Propose a communication and computing collaborative resource allocation scheme, and further perform multi-objective optimization to minimize delay and energy consumption; (3) Establish a resource demand prediction model based on mobile users' temporal behavior, and design a cross-layer handoff management pattern to enhance service continuity and user experience. The research results can not only promote the reliability, availability and flexibility of WBAN, but also provide strong theoretical support and technical support for the development of intelligent healthcare.

无线体域网（Wireless Body Area Network, WBAN）的普及应用受到有限的网络资源和智能设备存储计算能力制约，迫切需要云计算与边缘计算技术支撑来满足海量健康数据低时延、低功耗、高可靠性需求和用户多样化服务需求。本课题融合网络中通信与计算资源，研究云边协同WBAN资源调度问题，以降低网络开销、提升资源利用率和提高用户满意度为目标，从以下三个方面展开研究，具体包含：（1）建立多属性决策的计算卸载策略，均衡网络负载，为资源分配提供依据;（2）设计通信与计算协同资源调度方案并进行多目标优化，最小化时延和能耗;（3）建立基于移动用户时序行为的资源需求预测模型，跨层切换管理保证服务连续性，提升用户体验。本课题的研究结果对于增强WBAN网络可靠性、可用性和灵活性具有强力推动作用，也为促进智能医疗的发展提供有力的理论支撑与技术保障。

针对无线体域网（Wireless Body Area Network, WBAN）中迫切需要云计算与边缘计算技术支撑来满足海量健康数据低时延、低功耗、高可靠性需求和用户多样化服务需求。本项目主要融合网络中通信与计算资源，研究云边协同WBAN资源调度问题，以降低网络开销、提升资源利用率和提高用户满意度为目标，从以下三个方面展开研究，具体包含：（1）建立多属性决策的计算卸载策略，均衡网络负载，为资源分配提供依据;（2）设计通信与计算协同资源调度方案并进行多目标优化，最小化时延和能耗;（3）建立基于移动用户时序行为的资源需求预测模型，跨层切换管理保证服务连续性，提升用户体验。本课题的研究结果保证了具有不同用户优先级的海量健康数据在较低的延迟和能耗下可以得到处理。提出的算法能够提高用户数据特征的挖掘效用，从而分析出数据特征与疾病发病程度的相关性。对于增强WBAN网络可靠性、可用性和灵活性具有强力推动作用，也为促进智能医疗的发展提供有力的理论支撑与技术保障。