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.
