Open AccessFuzzy Logic-based Efficient Message Route Selection Method to Prolong the Network Lifetime in WSNs
Author(s) -
Jin-Hyun Ahn,
Sanghyeok Lim,
Taeho Cho
Publication year - 2021
Publication title -
international journal of computer networks and communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.159
H-Index - 8
eISSN - 0975-2293
pISSN - 0974-9322
DOI - 10.5121/ijcnc.2021.13605
Subject(s) - computer science , bottleneck , wireless sensor network , computer network , distributed computing , probabilistic logic , energy consumption , cluster analysis , efficient energy use , fuzzy logic , node (physics) , protocol (science) , medicine , ecology , alternative medicine , structural engineering , pathology , artificial intelligence , machine learning , electrical engineering , biology , embedded system , engineering
Recently, sensor networks have been used in a wide range of applications, and interest in sensor node performance has increased. A sensor network is composed of tiny nodes with limited resources. The sensor network communicates between nodes in a configured network through self-organization. An energyefficient security protocol with a hierarchy structure with various advantages has been proposed to prolong the network lifetime of sensor networks. But due to structural problems in traditional protocols, nodes located upstream tend to consume relatively high energy compared to other nodes. A network protocol should be considered to provide minimal security and efficient allocation of energy consumption by nodes to increase the network lifetime. In this paper, we introduce a solution to solve the bottleneck problem through an efficient message route selection method. The proposed method selects an efficient messaging path using GA and fuzzy logic composed of multiple rules. Message route selection plays an important role in controlling the load balancing of nodes. A principal benefit of the proposed scheme is the potential portability of the clustering-based protocol. In addition, the proposed method is updated to find the optimal path through the genetic algorithm to respond to various environments. We demonstrated the effectiveness of the proposed method through an experiment in which the proposed method is applied to a probabilistic voting-based filtering scheme that is one of the cluster-based security schemes.