Near Optimal Power and Rate Control of Multi-Hop Sensor Networks With Energy Replenishment: Basic Limitations With Finite Energy and Data Storage

• Published in: IEEE transactions on automatic control Vol. 57; no. 4; pp. 815 - 829
• Main Authors: Zhoujia Mao, Koksal, C. E., Shroff, N. B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2012; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Batteries; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Control theory. Systems; Discharges; Equations; Exact sciences and technology; Memory and file management (including protection and security); Memory organisation. Data processing; Optimal control; Power control; Quality of service; rate; renewable energy; Resource management; Sensors; Software; Spread spectrum communication; Liveness; Measurement sensor; Data storage; Secondary cell; Coverage; Multirate system; Buffer system; Multi hop network; Energy source; Renewable energy; Power control; rate; Optimal control; Distributed algorithm; Battery; Sensor array; Optimal control (mathematics); Queue; Energy storage
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2011.2166310

Renewable energy sources can be attached to sensor nodes to provide energy replenishment for extending the battery life and prolonging the overall lifetime of sensor networks. For networks with replenishment, conservative energy expenditure may lead to missed recharging opportunities due to battery capacity limitations, while aggressive usage of energy may result in reduced coverage or connectivity for certain time periods. Thus, new power allocation schemes need to be designed to balance these seemingly contradictory goals, in order to maximize sensor network performance. In this paper, we study the problem of how to jointly control the data queue and battery buffer to maximize the long-term average sensing rate of a wireless sensor network with replenishment under certain QoS constraints for the data and battery queues. We propose a unified algorithm structure and analyze the performance of the algorithm for all combinations of finite and infinite data and battery buffer sizes. We also provide a distributed version of the algorithm with provably efficient performance.