As the trends towards decentralization, miniaturization, and longevity of deployment continue in many domains, power management has become increasingly important. In this work, we develop power-aware control strategies for wireless sensor networks to improve the lifetime of the network and to ensure that the desired performance is guaranteed. For the case of static networks (networks of agents with no mobility), we identify the problem of the effects of power variations on the performance of an individual sensing device and on the entire network. To address this problem in a randomly deployed sensor network comprising of sensing devices whose sensing range is a function of transmitted power, we propose power-aware controllers to compensate for the variations in available power and maintain desired performance. We also propose a novel energy-efficient sleep-scheduling scheme that is random in nature and allows limited coordination among neighboring sensors for making switching decisions. This scheme is based on the concept of a hard-core point process from stochastic geometry, in which neighboring points are allowed to interact with each other through some predefined interaction laws. For the case of mobile networks (networks of agents with mobility), we propose a solid framework for distributed power-aware mobility strategies that can achieve any desired global objective while minimizing total energy consumption. This goal is achieved by first exploring fundamental trade-offs among various modes of operations of mobile devices and then exploiting these trade-offs for minimizing energy consumption. Through this framework, a whole class of decentralized power-aware controllers emerge for solving canonical problems in multi-agent systems like connectivity maintenance, rendezvous, and coverage control.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/50390 |
| Date | 13 January 2014 |
| Creators | Jaleel, Hassan |
| Contributors | Egerstedt, Magnus B. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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