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.

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. 

INERTIA PARTICLE SWARM OPTIMIZATION WITH DYNAMIC VELOCITY BASED CLUSTERING TECHNIQUE IN WIRELESS SENSOR NETWORKS

2021 ◽  
Vol 10 (1) ◽  
pp. 433-442
Author(s):  
R. Sathiya Priya ◽  
K. Arutchelvan ◽  
C. T. Bhuvaneswari
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Fitness Function ◽  
Particle Swarm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Clustering Technique

Wireless Sensor Networks (WSN) comprises a collection of nodes commonly employed to observe the physical environment. Different sensor nodes are linked to an inbuilt power unit to carry out necessary operations and data transmission between nearby nodes. The maximization of network lifetime and minimization of energy dissipation are considered as the major design issue of WSN. Clustering is a familiar energy efficient technique and the choice of optimal cluster heads (CHs) is considered as an NP hard problem. This paper presents an Inertia Particle Swarm Optimization algorithm with dynamic velocities (IPSO-DV) algorithm based clustering technique in WSN. The aim of the IPSO-DV technique is to select the CHs in such a way to maximize network lifetime. The IPSO-DV algorithm derives a fitness function (FF) to select CHs using distance to BS and remaining energy level. The application of dynamic velocities helps to improvise the effectiveness of the conventional PSO algorithm. To assure the performance of the presented IPSO-DV algorithm, a series of experiments were carried out and the results are investigated under several aspects. The experimentation outcome ensured the effective performance of the IPSO-DV algorithm over the compared clustering techniques.

scholarly journals Optimal node placement of industrial wireless sensor networks based on adaptive mutation probability binary particle swarm optimization algorithm

2012 ◽  
Vol 9 (4) ◽  
pp. 1553-1576 ◽  
Author(s):  
Ling Wang ◽  
Wei Ye ◽  
Haikuan Wang ◽  
Xiping Fu ◽  
Minrui Fei ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Adaptive Mutation ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Node Placement

Industrial Wireless Sensor Networks (IWSNs), a novel technique in industry control, can greatly reduce the cost of measurement and control and improve productive efficiency. Different from Wireless Sensor Networks (WSNs) in non-industrial applications, the communication reliability of IWSNs has to be guaranteed as the real-time field data need to be transmitted to the control system through IWSNs. Obviously, the network architecture has a significant influence on the performance of IWSNs, and therefore this paper investigates the optimal node placement problem of IWSNs to ensure the network reliability and reduce the cost. To solve this problem, a node placement model of IWSNs is developed and formulized in which the reliability, the setup cost, the maintenance cost and the scalability of the system are taken into account. Then an improved adaptive mutation probability binary particle swarm optimization algorithm (AMPBPSO) is proposed for searching out the best placement scheme. After the verification of the model and optimization algorithm on the benchmark problem, the presented AMPBPSO and the optimization model are used to solve various large-scale optimal sensor placement problems. The experimental results show that AMPBPSO is effective to tackle IWSNs node placement problems and outperforms discrete binary Particle Swarm Optimization (DBPSO) and standard Genetic Algorithm (GA) in terms of search accuracy and the convergence speed with the guaranteed network reliability.

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