A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks

Abstract

Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors’ residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH’s number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime.

Enhancing Smart Agriculture Monitoring via Connectivity Management Scheme and Dynamic Clustering Strategy

“…The residual energy had the highest weight, whereas the sink distance had the lowest weight. In [41], a PSO-based method was adopted to find the optimal cluster head by combining residual energy and sink distance to minimize the message overhead. In [42], nodes were clustered before using energy-based paths to connect clusters.…”

Efficient cluster-based routing protocol for wireless sensor networks by using collaborative-inspired Harris Hawk optimization and fuzzy logic

“…Fig 5 portrays triangular and two trapezoidal membership functions, representing varying ranges of neighboring nodes: less, normal, and more. The computation of a node's number of neighboring nodes is detailed in Eq 5 [15].…”

“…Given their reduced energy consumption during data transmission, these nodes can evenly distribute the energy load, preventing localized energy depletion and effectively averting hot spot issues. The computation of a node's distance to BS is detailed in Eq 6 [11,15].…”

“…This paper utilizes Min-Max Scaling to linearly map energy, distance, and the number of nodes to a range between 0 and 1. The normalization calculation process is as follows [15]:…”

“…However, this approach can rapidly deplete the energy of CHs, cause uneven energy distribution among network nodes, and overlook adequate load balancing, potentially leading to network overload in specific areas and affecting overall performance. In response, scholars have proposed various enhanced CH selection and routing path determination algorithms, often employing intelligent computing methods such as fuzzy logic [11], genetic algorithms [12], particle swarm optimization [13], ant colony optimization [14], and combinations like fuzzy logic with particle swarm optimization [15], fuzzy logic with ant colony optimization [16], and fuzzy logic with weed optimization [17]. Particularly, fuzzy logic stands out for its ability to handle uncertainties inherent in clustering and routing compared to other algorithms [18,19].…”