Book chapter
Temporal Partition in Sensor Networks
Stabilization, Safety, and Security of Distributed Systems, pp.325-339
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2007
DOI: 10.1007/978-3-540-76627-8_25
Abstract
Sensor networks are composed of nodes embedded in physical environments where applications may be tasked to run for years without human maintenance and without continuous external power supply. Strategies for power conservation are thus important in sensor network protocols and system architecture. One such strategy is to arrange node sleeping schedules so that radios are powered off until communication is necessary. Nodes cannot receive messages during periods when the radio is turned off. In this setting, there can arise situations where groups of network nodes have somehow become temporally partitioned: due to having different sleeping schedules, groups of nodes could be unaware of each other. The paper presents several self-stabilizing protocols to solve the problem of temporal partition; starting from an arbitrary temporally partitioned state, these protocols lead the network to a state in which all nodes have a perfectly aligned sleep schedule. Our techniques include using randomly chosen relatively prime sleep periods and occasional, and possibly random, probing of extra time slots. Our protocols aim for fast convergence while imposing only a small energy consumption overhead.
Details
- Title: Subtitle
- Temporal Partition in Sensor Networks
- Creators
- Ted Herman - University of IowaSriram Pemmaraju - University of IowaLaurence Pilard - University of IowaMorten Mjelde - University of Bergen
- Resource Type
- Book chapter
- Publication Details
- Stabilization, Safety, and Security of Distributed Systems, pp.325-339
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-540-76627-8_25
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2007
- Academic Unit
- Computer Science; Internal Medicine
- Record Identifier
- 9984259424802771
Metrics
6 Record Views