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

2021 ◽  
Author(s):  
Senthil G A ◽  
Arun Raaza ◽  
N Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Internet Of Things ◽  
Optimal Path ◽  
Particle Swarm ◽  
Base Station ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

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 energy-constrained 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.

scholarly journals A New Hybrid Particle Swarm Optimization and Greedy for 0-1 Knapsack Problem

2016 ◽  
Vol 1 (3) ◽  
pp. 411 ◽  
Author(s):  
Phuong Hoai Nguyen ◽  
Dong Wang ◽  
Tung Khac Truong
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Function ◽  
Knapsack Problem ◽  
Binary Code ◽  
Particle Swarm ◽  
Superior Performance ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Greedy Strategy ◽  
Hybrid Particle Swarm Optimization

This paper proposes a new binary particle swarm optimization with a greedy strategy to solve 0-1 knapsack problem. Two constraint handling techniques are consider to cooperation with binary particle swarm optimization that are penalty function and greedy. The sigmoid transfer function is used to convert real code to binary code. The experimental results have proven the superior performance of the proposed algorithm.

scholarly journals Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Shanhe Jiang ◽  
Chaolong Zhang ◽  
Shijun Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Random Coefficients ◽  
Superior Performance ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Gravitational Search

Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.

An Energy Efficient Trust Based Routing Scheme Using Hybrid Particle Swarm Optimization for Wireless Sensor Based Healthcare Networks

2021 ◽  
Vol 11 (12) ◽  
pp. 3096-3102
Author(s):  
S. Gnana Selvan ◽  
I. Muthu Lakshmi
Keyword(s):  
Particle Swarm Optimization ◽  
Network Performance ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Base Stations ◽  
Wireless Sensor ◽  
Second Phase ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

Healthcare networks are so sensitive and requires faster yet reliable data transmission. The problem based on congestion degrades the resources that lead to the failure of sensor nodes and faulty node misbehavior. In addition to this, increased energy computation, network performance minimizes the network lifetime. So to overcome such drawbacks, this paper proposes trust-based congestion aware using Hybrid Particle Swarm Optimization (HPSO) in Wireless Sensor based Healthcare Networks (WSHN). The proposed approach comprises two significant phases. The initial phase involves the calculation of congestion state among various nodes and the of trust values. Thus an optimal congestion metric is obtained. In the second phase, two diverse metrics namely distance and trust congestion metrics are executed using HPSO algorithm for optimal data packet routing from the base stations to the source node. This article presents a novel HPSO algorithm that utilises two distinct operators, namely the emigration and immigration processes, as well as the mutation process of the Bio-geographical based Optimization (BBO) algorithm, for presenting the optimal data routing protocol. The experimental outcomes and comparison analysis demonstrate that the proposed strategy outperforms several alternative approaches.

scholarly journals Hybrid Wireless Sensor Network Coverage Holes Restoring Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Liu Zhouzhou ◽  
Yanhong She
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

Aiming at the perception hole caused by the necessary movement or failure of nodes in the wireless sensor actuator network, this paper proposed a kind of coverage restoring scheme based on hybrid particle swarm optimization algorithm. The scheme first introduced network coverage based on grids, transformed the coverage restoring problem into unconstrained optimization problem taking the network coverage as the optimization target, and then solved the optimization problem in the use of the hybrid particle swarm optimization algorithm with the idea of simulated annealing. Simulation results show that the probabilistic jumping property of simulated annealing algorithm could make up for the defect that particle swarm optimization algorithm is easy to fall into premature convergence, and the hybrid algorithm can effectively solve the coverage restoring problem.

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