Combination of improved Harris’s hawk optimization with fuzzy to improve clustering in wireless sensor network

Abstract

A Wireless Sensor Network (WSN) is divided into groups of sensor nodes for efficient transmission of data from the point of measuring to sink. By performing clustering, the network remains energy-efficient and stable. An intelligent mechanism is needed to cluster the sensors and find an organizer node, the cluster head. The organizer node assembles data from its constituent nodes called member nodes, finds an optimal route to the sink of the network, and transfers the same. The nomination of cluster head is crucial since energy utilization is a major challenge of sensor nodes deployed over a hostile environment. In this paper, a fuzzy-based Improved Harris’s Hawk Optimization Algorithm (IHHO) is proposed to select an able cluster head for data communication. The fuzzy inference model ponders balance energy, distance from self to sink node, and vicinity of nodes from cluster head as input factors and decides if a candidate node is eligible for becoming a cluster head. The IHHO tunes the logic into an energy-efficient network with less complexity and more ease. The novelty of the paper lies in applying the hawk-pack technique based on fuzzy rules. Simulations show that the combination of Fuzzy based IHHO reduces the death of nodes through which network lifetime is enhanced.

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

“…In order to strictly verify the performance of the proposed CHHFO protocol, we conducted a comparative analysis. The analysis involves the comparison of the protocol EFCR [31] using fuzzy systems, the intelligent algorithm HHO-UCRA [33] and the protocol IHHO-F [36] combining intelligent algorithms with fuzzy systems. These comparisons are carried out in the same simulation environment.…”

“…To improve the network lifetime. [36] proposed a fuzzy based Improved Harris Hawk Optimization Algorithm (IHHO-F). The algorithm aims to determine the optimal set of CHs through two phases: exploration and exploitation.…”

Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

“…To demonstrate the adaptability of proposed unequal clustering in shape varying networks, the network lifetime of proposed method is also compared against stateof-the-art techniques for square shape networks. The results in Figure 19 show network lifetime of proposed method against Harris Harks Optimization Clustering with Fuzzy Routing (HHOCFR) [79], Harris Hawk Optimization based Unequal Clustering Routing Algorithm (HHO-UCRA) [80], Improved Harris Hawk Algorithm with Fuzzy (IHHO-F) [81], Distributed clustering routing protocol combined Affinity Propagation with Fuzzy Logic (DAPFL) [82], and Improved Balanced Residual Energy LEACH, (IBRE-LEACH) [83]. The results in Figure 19, confirm that the proposed method demonstrate 101%, 61% 186%, 59% and 24% increase in network lifetime on FND scale and 45%, 29%, 13%, 41% and 23% increase on LND scale as compared to IBRE-LEACH, DAPFL, IHHO-F, HHO-UCRA, and HHOCFR respectively.…”

“…In this section a comparison of overall energy consumption in proposed method has been developed with IBRE-LEACH [83], DAPFL [82], IHHO-F [81], HHO-UCRA [80], and HHOCFR [79], Fuzzy Logic based unequal clustering [77], IUCR [78], and ECUC [27]. Results demonstrate that after 1200 rounds of operation there is around 12.8%, 16.2%, 39.6%, 48.9%, 53.1%, 56.7%, 58.3%, and 61.4% decrease in overall energy consumption by using the proposed method as compared to FL based clustering, IUCR, ECUC, HHOCFR, HHO-UCRA, IHHO-F, DAPFL, and IBRE-LEACH respectively.…”

Fusion of Harris Hawk’s and Particle Swarm Optimization to Improve Performance in Wireless Network

“…Fuzzy-based Improved In order to choose a capable GH for data transfer, the HHO Algorithm (IHHO) is offered as a solution [15]. The fuzzy inference model considers balancing energy, distance from self to sink node, and proximity of nodes from GH as input inputs.…”