Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network

Abstract

Specialized transducers in Wireless Sensor Networks (WSNs) that offer sensing services to the Internet of Things (IoT) devices have limited storage and energy resources. One of the most vital issues in WSN design is power usage, as it is nearly impossible to recharge or replace sensor nodes' batteries. A prominent role in conserving power for energyconstrained networks is served by the clustering algorithm. It is possible to reduce network energy usage and network lifespan prolongation by proper balancing of the network load with Cluster Head (CH) election. The single-hop inter-cluster routing technique, in which there is a direct transfer from CHs to the Base Station (BS), is done by the Low Energy Adaptive Clustering Hierarchy (LEACH). However, for networks with large-regions, this technique is not viable. An optimized Orphan-LEACH (O-LEACH) has been proposed in this work to facilitate the formation of a novel process of clustering, which can result in minimized usage of energy as well as enhanced network longevity. Sufficient energy is possessed by the orphan node, which will attempt to be cover the network. The proposed work's primary novel contribution is the O-LEACH protocol that supplies the entire network's coverage with the least number of orphaned nodes and has extremely high connectivity rates. A hybrid optimization utilizing Simulated Annealing (SA) with Lightning Search Algorithm (LSA) (SA-LSA), and Particle Swarm Optimization (PSO) with LSA (PSO-LSA) Algorithm is proposed. These proposed techniques effectivelymanage the CH election achieving optimal path routing and minimization in energy usage, resulting in the enhanced lifespan of the WSN. The proposed technique's superior performance, when compared with other techniques, is confirmed from the outcomes of the experimentations.

Coverage hole aware optimal cluster based routing for wireless sensor network assisted IoT using hybrid deep recurrent neural network

“…An optimized Orphan‐LEACH (O‐LEACH) was developed by Senthil et al 22 to facilitate the formation of a novel process of clustering, which resulted in minimized usage of energy as well as enhanced network longevity. A hybrid optimization utilizing simulated annealing with Lightning Search Algorithm (LSA) (SA‐LSA), and particle swarm optimization with LSA (PSO‐LSA) Algorithm was introduced.…”

“…( 4) , − −−−− → q eq. (avg) (22) It is then that exact vocabulary seeks balance between study and exploitation, and finally strives for greater optimization by using the exponential term (e). Because the crowd density changes over time, the next illustration shows a random vector with a range of [0,1].…”

“…A series of simulations validated the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental results of the CBR-ICWSN technique outperformed other methods in terms of network lifetime and energy efficiency.An optimized Orphan-LEACH (O-LEACH) was developed by Senthil et al22 to facilitate the formation of a novel process of clustering, which resulted in minimized usage of energy as well as enhanced network longevity. A hybrid optimization utilizing simulated annealing with Lightning Search Algorithm (LSA) (SA-LSA), and particle swarm optimization with LSA (PSO-LSA) Algorithm was introduced.…”

Outdoor Air Quality Monitoring with Enhanced Lifetime-enhancing Cooperative Data Gathering and Relaying Algorithm (E-LCDGRA) Based Sensor Network

“…In a clustered structure, the sensors are split into cells while a leader is allocated to accumulate readings of their cell and convey them to the remote gateway [17] . Various clustered aggregation schemes can be found in the literature [18][19][20][21][22][23][24] .…”

Network energy optimization and intelligent routing in WSN applicable for IoT using self‐adaptive coyote optimization algorithm

“…The hybridization of the particle swarm optimization (PSO) algorithm and the harmony search algorithm (HSA) is adopted to select the energy‐efficient CHs 14,15 . Moreover, this approach further minimizes data communications as there is no change or little change in the sensed attribute 16,17 . It is utilized to resolve constraint optimization problems, and also, it is applicable for real‐world applications.…”