A Two-Tier Distributed Fuzzy Logic Based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks

Seyyit Alper Sert, Abdullah Alchihabi, Adnan Yazici

Research output: Contribution to journalArticlepeer-review

103 Citations (Scopus)

Abstract

This study proposes a two-Tier distributed fuzzy logic based protocol (TTDFP) to improve the efficiency of data aggregation operations in multihop wireless sensor networks (WSNs). Clustering is utilized for efficient aggregation requirements in terms of consumed energy. In a clustered network, member (leaf) nodes transmit obtained data to cluster-heads (CHs) and CHs relay received packets to the base station. In multihop wireless networks, this CH-generated transmission occurs over other CHs. Due to the adoption of a multihop topology, hotspots and/or energy-hole problems may arise. This article proposes a TTDFP to extend the lifespan of multihop WSNs by taking the efficiency of clustering and routing phases jointly into account. TTDFP is a distribution-Adaptive protocol that runs and scales sensor network applications efficiently. Additionally, along with the two-Tier fuzzy logic based protocol, we utilize an optimization framework to tune the parameters used in the fuzzy clustering tier in order to optimize the performance of a given WSN. This paper also includes performance comparisons and experimental evaluations with the selected state-of-The-Art algorithms. The experimental results reveal that TTDFP performs better than any other protocols under the same network setup considering metrics used for comparing energy-efficiency and network lifespan of the protocols.

Original languageEnglish
Article number8368142
Pages (from-to)3615-3629
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Volume26
Issue number6
DOIs
Publication statusPublished - Dec 1 2018

Keywords

  • Fuzzy clustering
  • fuzzy routing
  • parameter optimization
  • wireless sensor networks (WSNs)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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