In-network hebbian plasticity for wireless sensor networks

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

Abstract

In typical Wireless Sensor Networks (WSNs), all sensor data is routed to a more powerful computing entity. In the case of environmental monitoring, this enables data prediction and event detection. When the size of the network increases, processing all the input data outside the network will create a bottleneck at the gateway device. This creates delays and increases the energy consumption of the network. To solve this issue, we propose using Hebbian learning to pre-process the data in the wireless network. This method allows to reduce the dimension of the sensor data, without loosing spatial and temporal correlation. Furthermore, bottlenecks are avoided. By using a recurrent neural network to predict sensor data, we show that pre-processing the data in the network with Hebbian units reduces the computation time and increases the energy efficiency of the network without compromising learning.

Original languageEnglish
Title of host publicationInternet and Distributed Computing Systems 12th International Conference, IDCS 2019, Proceedings
EditorsRaffaele Montella, Angelo Ciaramella, Giancarlo Fortino, Antonio Guerrieri, Antonio Liotta
Place of PublicationBerlin
PublisherSpringer
Pages79-88
Number of pages10
ISBN (Print)9783030349134
DOIs
Publication statusPublished - 1 Jan 2019
Event12th International Conference on Internet and Distributed Computing Systems, IDCS 2019 - Naples, Italy
Duration: 10 Oct 201912 Oct 2019

Publication series

NameLecture Notes in Computer Science
Volume11874
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Internet and Distributed Computing Systems, IDCS 2019
CountryItaly
CityNaples
Period10/10/1912/10/19

Keywords

  • Hebbian plasticity
  • Recurrent neural network
  • Wireless Sensor Networks

Fingerprint Dive into the research topics of 'In-network hebbian plasticity for wireless sensor networks'. Together they form a unique fingerprint.

Cite this