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DCLA: A duty-cycle learning algorithm for IEEE 802.15.4 beacon-enabled WSNs

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

The current specification for IEEE 802.15.4 beacon-enabled networks does not define how active and sleep schedules should be configured in order to achieve the optimal network performance in all traffic conditions. Several algorithms exist in the literature that dynamically vary these schedules based on traffic load estimations. But it is still uncertain how these adaptive schemes perform with regard to each other as their performance has only been compared with the standard beacon mode. In this paper, we compare the current state-of-the-art schemes, and with the objective of overcoming the performance deficiencies shown by previous approaches, we introduce DCLA, an adaptive duty-cycle scheme for IEEE 802.15.4 beacon-enabled Wireless Sensor Networks (WSN) that employs a reinforcement learning technique. Simulation results show that the proposed scheme achieves the best overall network performance for a wide range of traffic conditions and performance parameters when compared with existing IEEE 802.15.4 duty-cycle adaptation schemes.

Original languageEnglish
Title of host publicationAd Hoc Networks - Second International Conference, ADHOCNETS 2010, Revised Selected Papers
Pages217-232
Number of pages16
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2nd International Conference on Ad Hoc Networks, ADHOCNETS 2010 - Victoria, BC, Canada
Duration: 18 Aug 201020 Aug 2010

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
Volume49 LNICST
ISSN (Print)1867-8211

Conference

Conference2nd International Conference on Ad Hoc Networks, ADHOCNETS 2010
Country/TerritoryCanada
CityVictoria, BC
Period18/08/1020/08/10

Keywords

  • Duty-cycle
  • Energy efficiency
  • IEEE 802.15.4
  • Machine learning
  • Reinforcement learning
  • Wireless Sensor Networks (WSNs)

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