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Energy-efficient and fault-tolerant evolution models for large-scale wireless sensor networks: a complex networks-based approach

  • Haixia Peng
  • , Shuaizong Si
  • , Mohamad Khattar Awad
  • , Nan Cheng
  • , Haibo Zhou
  • , Xuemin Shen
  • , Hai Zhao
  • Northeastern University China
  • University of Waterloo
  • Kuwait University

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

Abstract

In this paper, we present three network evolution models for generating fault-tolerant and energy- efficient large-scale peer-to-peer wireless sensor networks (WSNs) based on complex networks theory. Being scale-free is one of the intrinsic features of complex networks-based evolution models that generates fault- tolerant topologies. In this work, we argue that fault- tolerant topologies are not necessarily energy efficient. The three proposed energy-aware evolution models are energy-aware common neighbors (ECN), energy- aware large degree promoted (ELDP) and energy-aware large degree demoted (ELDD). ECN considers neighborhood overlap, whereas ELDP and ELDD consider topological overlap for node attachment. The ELDP model promotes the establishment of links to nodes with a large degree, whereas the ELDD model demotes this strategy. Performance evaluations demonstrate that the proposed models outperform a candidate clustering-based model, thereby providing greater energy savings and fault- tolerance. Among the proposed models, ECN is the winner in-terms of energy efficiency, ELDD performs best in- terms of fault-tolerance, and ELDP conveniently provides balance between the two.

Original languageEnglish
Article number7417534
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
StatePublished - 2015
Externally publishedYes
Event58th IEEE Global Communications Conference, GLOBECOM 2015 - San Diego, United States
Duration: 6 Dec 201510 Dec 2015

Keywords

  • Complex network
  • Energy-efficient networks
  • Fault-tolerant networks
  • Large-scale wireless sensor networks

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