A lightweight dynamic optimization methodology for wireless sensor networks

Arslan Munir, Ann Gordon-Ross, Susan Lysecky, Roman Lysecky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Technological advancements in embedded systems due to Moore's law have lead to the proliferation of wireless sensor networks (WSNs) in different application domains (e.g. defense, health care, surveillance systems) with different application requirements (e.g. lifetime, reliability). Many commercial-off-the-shelf (COTS) sensor nodes can be specialized to meet these requirements using tunable parameters (e.g. voltage, frequency) to specialize the operating state. Since a sensor node's performance depends greatly on environmental stimuli, dynamic optimizations enable sensor nodes to automatically determine their operating state in-situ. However, dynamic optimization methodology development given a large design space and resource constraints (memory and computational) is a very challenging task. In this paper, we propose a lightweight dynamic optimization methodology that intelligently selects initial tunable parameter values to produce a high-quality initial operating state in one-shot for time-critical or highly constrained applications. Further operating state improvements are made using an efficient greedy exploration algorithm, achieving optimal or near-optimal operating states while exploring only 0.04% of the design space on average.

Original languageEnglish (US)
Title of host publication2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob'2010
PublisherIEEE Computer Society
Pages133-136
Number of pages4
ISBN (Print)9781424477425
DOIs
StatePublished - 2010

Publication series

Name2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob'2010

Keywords

  • Dynamic optimization
  • Optimization algorithms
  • Wireless sensor networks

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Communication

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