Predictive power control in wireless sensor networks

Michele Chincoli, Aly Aamer Syed, Decebal Constantin Mocanu, Antonio Liotta

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)

Abstract

Communications in Wireless Sensor Networks (WSNs) are affected by dynamic environments, variable signal fluctuations and interference. Thus, prompt actions are necessary to achieve dependable communications and meet quality of service requirements. To this end, the reactive algorithms used in literature and standards, both centralized and distributed ones, are too slow and prone to cascading failures, instability and sub-optimality. We explore the predictive power of machine learning to better exploit the local information available in the WSN nodes and make sense of global trends. We aim at predicting the configuration values that lead to network stability. In this work, we adopt the Q-learning algorithm to train WSNs to proactively start adapting in face of changing network conditions, acting on the available transmission power levels. Our aim is to prove that smart nodes lead to better network performance with the aid of simple machine learning.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 1st International Conference on Internet-of-Things Design and Implementation, IoTDI 2016
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages309-312
Number of pages4
ISBN (Electronic)978-1-4673-9948-7
DOIs
Publication statusPublished - 17 May 2016
Externally publishedYes
Event1st IEEE International Conference on Internet-of-Things Design and Implementation, IoTDI 2016 - Berlin, Germany
Duration: 4 Apr 20168 Apr 2016
Conference number: 1

Conference

Conference1st IEEE International Conference on Internet-of-Things Design and Implementation, IoTDI 2016
Abbreviated titleIoTDI
CountryGermany
CityBerlin
Period4/04/168/04/16

Keywords

  • Q-learning
  • Software architecture
  • Transmission power control (TPC)
  • Wireless sensor network (WSN)

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