2006
DOI: 10.1007/11687818_22
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Adaptive Scheduling in Wireless Sensor Networks

Abstract: As the number of Wireless Sensor Networks (WSNs) applications is anticipated to grow substantially in coming years, new and radical strategies for effectively managing such networks will be needed. One possibility involves endowing the network with an autonomic capability to dynamically adapt itself to the prevailing network operating conditions, even while communications sessions are active. This may involve the network adapting itself either partially or completely. The approach suggested in this paper propo… Show more

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Cited by 9 publications
(6 citation statements)
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References 13 publications
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“…1) The sensor networks are used in a variety of applications which require constant monitoring and detection of specific events. The military applications include battle field surveillance and monitoring, guidance systems of intelligent missiles and detection of attack by weapons of mass destruction, such as chemical, biological or nuclear [14].…”
Section: Applicationsmentioning
confidence: 99%
“…1) The sensor networks are used in a variety of applications which require constant monitoring and detection of specific events. The military applications include battle field surveillance and monitoring, guidance systems of intelligent missiles and detection of attack by weapons of mass destruction, such as chemical, biological or nuclear [14].…”
Section: Applicationsmentioning
confidence: 99%
“…A large number of scheduling algorithms have been proposed to reduce the energy consumption at all levels of the wireless sensor networks. In 2004, Barbara Hohlt et al, [10] proposed a distributed on-demand power-management protocol for collecting data in sensor networks. In the paper, they presented a dynamic distributed time division scheduling protocol that facilitated power management by enabling nodes to turn off their radios during idle slots.…”
Section: Related Workmentioning
confidence: 99%
“…In a loosely connected network, remote sensor nodes have to make local decisions, rather than pushing raw data to a remote BS. For example, in a large-scale fire monitoring network, the WSN nodes should be capable of expediting sampling when the threat from a potential fire is identified, as well as adopting other strategies that would increase responsiveness, for example, adaptive scheduling (Ruzzelli et al 2005). Nodes should also be capable of proactively restoring original operating parameters when conditions return to normal.…”
Section: Belief Generation Through Agent Theoretical Reasoningmentioning
confidence: 99%