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）资助的。无线传感器网络（WSNs）在民用和军事领域都变得越来越普遍。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，开发了一个全面的面向电池充电的无线传感器网络能源管理框架。其关键的新奇是它的会计独特的非线性电池特性，包括被动充电，负载曲线的依赖性，和容量衰减。这样的特性对可用电池容量具有显著影响，并且因此对网络寿命具有显著影响。新颖的，物理合理的电池充电/放电的分析模型，利用设计自适应控制策略，在无线传感器网络中的数据处理和通信，以最大限度地提高网络寿命的目的。这些策略用于操作单个节点以及节点的分层网络。电池感知的自适应CPU电压/频率，射频发射功率，传输速率/调制方案，睡眠/唤醒调度，簇头分配，覆盖选择等模型和算法在这个项目中开发的验证和其可行性，通过仿真和实验证明。该活动包括一个涉及本科生和研究生的教育部分，以及一个强大的技术转让计划。该项目有望为传感器网络带来新颖的设计和控制策略，从而显著延长运行寿命和高效的能源管理。

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.