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Life-span extension for sensor networks in the industry

  • Metin Ozturk
  • , Mona Jaber
  • , Muhammad A. Imran
  • University of Glasgow
  • Queen Mary University of London

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

Abstract

This chapter focuses on state-of-the-art life-span extension methods for wireless sensor networks (WSNs) with high-level taxonomy: energy harvesting and energy conservation. WSNs render the industrial processes more dynamic and agile owing to their real-time monitoring capabilities, hence reducing the operational expenditure with minimal installation cost. However, the life-span of WSNs should be as long as possible in order for the industry to capitalize on the offered gain and maximize their cost effectiveness. Energy consumption is the main determinant factor for the life-span of WSNs, since the sensor nodes are typically energy constraint devices due to their limited batteries. The chapter introduces WSNs in general, and focuses on Industrial WSNs by presenting their distinctive characteristics and some selected applications. Lastly, it demonstrates Q learning assisted energy efficient model, and provides the simulation results revealing that it is capable of improving the application specific gains of the WSN while reducing the energy consumption.

Original languageEnglish
Title of host publicationWireless Automation as an Enabler for the Next Industrial Revolution
Publisherwiley
Pages19-45
Number of pages27
ISBN (Electronic)9781119552635
ISBN (Print)9781119552611
DOIs
StatePublished - 27 Dec 2019
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Keywords

  • Energy conservation
  • Energy efficient model
  • Energy harvesting
  • Industrial processes
  • Industrial wireless sensor networks
  • Life-span extension methods
  • Q learning

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