Efficient communications in wireless sensor networks based on biological robustness

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

Researchers

Research units

  • University of Texas at Arlington
  • Missouri University of Science and Technology
  • Virginia Commonwealth University

Abstract

Robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their network topology and on how devices can react to disruptions, including node and link failures. This article presents a novel solution to obtain robust WSNs by exploiting principles of biological robustness at nanoscale. Specifically, we consider Gene Regulatory Networks (GRNs) as a model for the interaction between genes in living organisms. GRNs have evolved over millions of years to provide robustness against adverse factors in cells and their environment. Based on this observation, we apply a method to build robust WSNs, called bio-inspired WSNs, by establishing a correspondence between the topology of GRNs and that of alreadydeployed WSNs. Through simulation in realistic conditions, we demonstrate that bio-inspired WSNs are more reliable than existing solutions for the design of robust WSNs. We also show that communications in bio-inspired WSNs have lower latency as well as lower energy consumption than the state of the art.

Details

Original languageEnglish
Title of host publicationProceedings - 12th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2016
Publication statusPublished - 8 Aug 2016
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Distributed Computing in Sensor Systems - Washington, United States
Duration: 26 May 201628 May 2016
Conference number: 12

Publication series

Name International Conference on Distributed Computing in Sensor Systems and workshops
PublisherIEEE
ISSN (Print)2325-2936
ISSN (Electronic)2325-2944

Conference

ConferenceInternational Conference on Distributed Computing in Sensor Systems
Abbreviated titleDCOSS
CountryUnited States
CityWashington
Period26/05/201628/05/2016

    Research areas

  • Bio-inspired, Efficient communications, Gene regulatory networks, Nanoscale properties, Performance evaluation, Robustness, Wireless sensor networks

ID: 7390855