NeTS:Medium:Collaborative Research: Exploiting Battery-Supply Nonlinearities in Optimal Resource Management and Protocol Design for Wireless Sensor Networks

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。无线传感器网络(WSNs)在民用和军用领域都得到了广泛的应用。这些网络的低成本和易于部署，需要使用电池作为主要电源。因此，电池容量已成为最大化网络运行寿命的关键设计参数。在这个项目中，我们开发了一个全面的面向电池充电的无线传感器网络能量管理框架。它的主要创新之处在于它考虑了独特的非线性电池特性，包括无源充电、负载分布依赖和容量衰减。这些特性对电池的可用容量有很大影响，从而影响网络的寿命。在无线传感器网络的数据处理和通信的自适应控制策略设计中，采用了新颖的、物理上合理的电池充放电分析模型，以最大化网络寿命为目标。这些策略用于操作单个节点以及节点的分层网络。对CPU电压/频率、射频发射功率、传输速率/调制方式、休眠/唤醒调度、簇头分配、覆盖选择等进行了电池感知自适应，并通过仿真和实验验证了所提出的模型和算法的可行性。该活动包括一个涉及本科生和研究生的教育部分，以及一个强有力的技术转让计划。该项目有望为传感器网络带来新的设计和控制策略，具有显著更长的运行寿命和高效的能量管理。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Wireless sensor networks (WSNs) are becoming pervasive in both civilian and military domains. Low cost and ease of deployment of these networks necessitate the use of batteries as the primary source of power. As a result, battery capacity has emerged as a critical design parameter for maximizing the operational lifetime of the network. In this project, a comprehensive battery-charge-oriented framework for energy management in WSNs is developed. Its key novelty is its accounting of unique nonlinear battery characteristics, including passive recharge, load-profile dependence, and capacity fading. Such characteristics have significant impact on the usable battery capacity, and consequently on the network lifetime. Novel, physically justified analytical models for battery charge/discharge are exploited in designing adaptive control strategies for data processing and communications in a WSN, with the aim of maximizing the network lifetime. These strategies are used to operate individual nodes as well as a hierarchical network of nodes. Battery-aware adaptivity is performed on CPU voltage/frequency, RF transmission power, transmission rate/modulation scheme, sleep/wakeup scheduling, cluster-head assignment, cover selection, etc. Models and algorithms developed in this project are validated and their feasibility demonstrated through simulations and experimentation. The activity includes an education component involving undergraduate and graduate students, and a strong technology transfer plan. The project is expected to lead to novel designs and control strategies for sensor networks, with significantly longer operational lifetime and highly efficient energy management.