Allocating Multiple Base Stations under General Power Consumption by the Particle Swarm Optimization

2007 ◽  
Author(s):  
Tzung-Pei Hong ◽  
Guo-Neng Shiu
Keyword(s):  
Particle Swarm Optimization ◽  
Power Consumption ◽  
Particle Swarm ◽  
Base Stations ◽  
Swarm Optimization ◽  
General Power

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 Improved 3-D Indoor Positioning Based on Particle Swarm Optimization and the Chan Method

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 208
Author(s):  
Shanshan Chen ◽  
Zhicai Shi ◽  
Fei Wu ◽  
Changzhi Wang ◽  
Jin Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Complexity ◽  
Large Scale ◽  
Particle Swarm ◽  
Base Station ◽  
Base Stations ◽  
Time Of Arrival ◽  
Swarm Optimization ◽  
Positioning Accuracy ◽  
Initial Location

Time of arrival (TOA) measurement is a promising method for target positioning based on a set of nodes with known positions, with high accuracy and low computational complexity. However, most positioning methods based on TOA (such as least squares estimation, maximum-likelihood, and Chan, etc.) cannot provide desirable accuracy while maintaining high computational efficiency in the case of a non-line of sight (NLOS) path between base stations and user terminals. Therefore, in this paper, we proposed a creative 3-D positioning system based on particle swarm optimization (PSO) and an improved Chan algorithm to greatly improve the positioning accuracy while decreasing the computation time. In the system, PSO is used to estimate the initial location of the target, which can effectively eliminate the NLOS error. Based on the initial location, the improved Chan algorithm performs iterative computations quickly to obtain the final exact location of the target. In addition, the proposed methods will have computational benefits in dealing with the large-scale base station positioning problems while has highly positioning accuracy and lower computational complexity. The experimental results demonstrated that our algorithm has the best time efficiency and good practicability among stat-of-the-art algorithms.

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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