scholarly journals Parallel energy-efficient coverage optimization using WSN with Image Compression

2013 ◽  
pp. 12-17
Author(s):  
Jibanananda Mishra ◽  
Ranjan Kumar Jena ◽  
Rabinarayana Parida ◽  
Abinash Panda
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Process Model ◽  
Sensor Node ◽  
Radial Basis Function Network ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Parallel Particle Swarm Optimization

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrices are calculated by grid exclusion algorithm and Dijkstra’s algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combing the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection.

scholarly journals Improved GbLN-PSO Algorithm for Indoor Localization in Wireless Sensor Network

2021 ◽  
pp. 242-249
Author(s):  
M.Shahkhir Mozamir ◽  
◽  
Rohani Binti Abu Bakar ◽  
Wan Isni Soffiah Wan Din ◽  
Zalili Binti Musa
Keyword(s):  
Particle Swarm Optimization ◽  
Indoor Localization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Computational Time ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Local Neighborhood

Localization is one of the important matters for Wireless Sensor Networks (WSN) because various applications are depending on exact sensor nodes position. The problem in localization is the gained low accuracy in estimation process. Thus, this research is intended to increase the accuracy by overcome the problem in the Global best Local Neighborhood Particle Swarm Optimization (GbLN-PSO) to gain high accuracy. To compass this problem, an Improved Global best Local Neighborhood Particle Swarm Optimization (IGbLN-PSO) algorithm has been proposed. In IGbLN-PSO algorithm, there are consists of two phases: Exploration phase and Exploitation phase. The neighbor particles population that scattered around the main particles, help in the searching process to estimate the node location more accurately and gained lesser computational time. Simulation results demonstrated that the proposed algorithm have competence result compared to PSO, GbLN-PSO and TLBO algorithms in terms of localization accuracy at 0.02%, 0.01% and 59.16%. Computational time result shows the proposed algorithm less computational time at 80.07%, 17.73% and 0.3% compared others.

scholarly journals Deployment of Wireless Sensor Networks for Oilfield Monitoring by Multiobjective Discrete Binary Particle Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Zhen-Lun Yang ◽  
Angus Wu ◽  
Hua-Qing Min
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Real Time ◽  
Monitoring System ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization

The deployment problem of wireless sensor networks for real time oilfield monitoring is studied. As a characteristic of oilfield monitoring system, all sensor nodes have to be installed on designated spots. For the energy efficiency, some relay nodes and sink nodes are deployed as a delivery subsystem. The major concern of the construction of the monitoring system is the optimum placement of data delivery subsystem to ensure the full connectivity of the sensor nodes while keeping the construction cost as low as possible, with least construction and maintenance complexity. Due to the complicated landform of oilfields, in general, it is rather difficult to satisfy these requirements simultaneously. The deployment problem is formulated as a constrained multiobjective optimization problem and solved through a novel scheme based on multiobjective discrete binary particle swarm optimization to produce optimal solutions from the minimum financial cost to the minimum complexity of construction and maintenance. Simulation results validated that comparing to the three existing state-of-the-art algorithms, that is, NSGA-II, JGGA, and SPEA2, the proposed scheme is superior in locating the Pareto-optimal front and maintaining the diversity of the solutions, thus providing superior candidate solutions for the design of real time monitoring systems in oilfields.

Using Multi-Objective Particle Swarm Optimization for Energy-Efficient Clustering in Wireless Sensor Networks

2012 ◽  
pp. 291-304
Author(s):  
Hamid Ali ◽  
Waseem Shahzad ◽  
Farrukh Aslam Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Efficient ◽  
Efficient Solution ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Battery Power ◽  
Energy Efficient Clustering

In this chapter, the authors propose a multi-objective solution to the problem by using multi-objective particle swarm optimization (MOPSO) algorithm to optimize the number of clusters in a sensor network in order to provide an energy-efficient solution. The proposed algorithm considers the ideal degree of nodes and battery power consumption of the sensor nodes. The main advantage of the proposed method is that it provides a set of solutions at a time. The results of the proposed approach were compared with two other well-known clustering techniques: WCA and CLPSO-based clustering. Extensive simulations were performed to show that the proposed approach is an effective approach for clustering in WSN environments and performs better than the other two approaches.

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.

Node Deployment Using Virtual Force with Particle Swarm Optimization in WSN

2018 ◽  
Vol 24 (8) ◽  
pp. 6017-6019 ◽  
Author(s):  
K. S Umadevi ◽  
Virti Shah ◽  
Unnati Desai
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Node Deployment ◽  
Virtual Force

Sensor nodes are always considered in wireless sensor networks. So deployments of these sensor nodes are considerable, but proper deployment can decrease the complication of problems in wireless sensor networks. During such communication, data routing must be done efficiently in order to reduce the complexity. In addition, it minimizes energy consumption and thus extends the lifetime of Network. An attempt is made using Virtual Force and Particle Swarm Optimization for effective node deployment. First step, Virtual Force Algorithm is used for placement of nodes. Secondly, the result is provided to Particle Swarm Optimization to optimize the best fit between the neighbor nodes. The result depicts the proper deployment of nodes done in wireless sensor networks and improves the efficiency by minimal energy consumption.

scholarly journals Performance evaluation of power optimization in wireless sensor networks using particle swarm optimization

2018 ◽  
Vol 7 (2.7) ◽  
pp. 404
Author(s):  
Chandaluru Mohan Venkata Siva Prasad ◽  
Dr K. RaghavaRao ◽  
D Satish Kumar ◽  
A V. Prabhu
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Power Consumption ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization

Wireless sensor networks are the sensors which are acclimated to sense the environmental condition like temperature, pressure, sultriness, moisture etc, sensing the environment parameters and sending them to the gateway and retrieving the aggregated data from the gateway to the end user. Power is the major constraint in wireless sensor networks. One must need to reduce the power consumption. Wireless sensor networks have sensor nodes in which each node has a processor, antenna and a battery. The batteries consume power so that we require increasing the lifetime of the battery for that some optimization techniques are required to reduce the power consumption. There are some techniques which are inspired from the lifestyle of animals. In this paper proposing an optimization technique which is inspired by the animal demeanor which reduces the power consumption of the sensor nodes which is particle swarm optimization (PSO) technique. PSO is inspired by the convivial demeanor of birds or schooling of fish. By utilizing this bio-inspired technique we can reduce the power consumed by the sensor nodes and at the same time lifetime of the batteries present in the sensor nodes are increased. 

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