WSN wtih Artificial Intelligence Techniques based Optimization using PSO - A Survey

Harish Patil, Tejaswini Patil


The WSN is the network of sensor in which each node can collect  and  forward  data  to  his  neighbor  node  or  to sink node say base station. The genetic algorithm formally used for creating and re-initiating the cluster based WSN in which it optimizes the network while network establishment. Optimization done in such way that the networks itself find his cluster head and leaf node of network by using fitness function. The dormant parameters, connectivity parameters and energy parameters play precise role to form fitness function to validate WSN. The optimization stages of WSN are node placement, network coverage, clustering, data aggregation and routing can be optimized by using artificial intelligence techniques such as genetic algorithm and particle swarm optimization. In advance to the genetic algorithm for forming energy-efficient network particle swarm optimization computational method came into picture. Particle swarm optimization internally uses candidate solution to find optimize solution to get strengthen particle out  of all swarm particle say node to establish WSN. Particle swarm optimization is the most efficient way of optimizing the stages of WSN.

Full Text:



N. M. Abdul Latiff, C. C. Tsimenidis, B. S. Sharif, Energy- Aware Clustering For Wireless Sensor Networks Using Particle Swarm Optimization, The 18th Annual Ieee International Sym- posium On Personal, Indoor And Mobile Radio Communica- tions (Pimrc07).

Ali Norouzi and A. Halim Zaim, Genetic Algorithm Application in Optimization of Wireless Sensor Networks, Hindawi Publish- ing Corporation, the Scientific World Journal Volume 2014.

A.H. Mohamed, K.H. Marzouk, Optimizing the Energy Con- sumption of Wireless Sensor Networks, International Journal of Applied Information Systems (IJAIS) Volume 10 No.2, Decem- ber 2015.

Mohammed Abo-Zahhad, Sabah M. Ahmed, Nabil Sabor and Shigenobu Sasaki, A New Energy Efficient Adaptive Clustering Protocol Based on Genetic Algorithm for Improving the Life- time and the Stable Period of Wireless Sensor Networks, In- ternational Journal of Energy, Information and Communications Vol.5, Issue 3 (2014).

Zhen-Lun Yang, AngusWu, and Hua-QingMin, Deployment of Wireless Sensor Networks for Oilfield Monitoring by Multiob- jective Discrete Binary Particle Swarm Optimization, Hindawi Publishing Corporation Journal of Sensors, Volume 2016.

Devi Manickavelu, And Rhymend Uthariaraj Vaidyanathan, Particle Swarm Optimization (PSO) Based Node And Link Lifetime Prediction Algorithm For Route Recovery In MANET, Manickavelu And Vaidyanathan EURASIP Journal On Wireless Communications And Networking 2014.

Mohammad Reza Sabeti Baygi, Mostafa Razavi Ghods, Gelareh Veisi, Sensor Clustering and Base Station Mobilizing in Wireless Sensor Networks Using Genetic Algorithms, November 2015.

Kanika Goel, Suveg Moudgil, An Energy Efficient Routing Algorithm For WSN Using PSO Approach, International Journal For Advance Research In Engineering And Technology, Volume 3, Issue V, May 2015.

Yulai Suen, Genetic-Algorithm Based Mobile Sensor Network Deployment Algorithm, EE382C: Embedded Software Systems, 2005.

Deepak R Dandekar, Dr. P.R.Deshmukh, Relay Node Placement for Multi-Path Connectivity in Heterogeneous Wireless Sensor Networks, ScienceDirect, Procedia Technology 4 (2012) 732 736.

Ali Norouzi, Faezeh Sadat Babamir, Abdul Halim Zaim, A New Clustering Protocol for Wireless Sensor Networks Using Genetic Algorithm Approach, Wireless Sensor Network, 2011.

Raghavendra V. Kulkarni, Senior Member, IEEE, and Ganesh Kumar Venayagamoorthy, Senior Member, IEEE, Particle Swarm Optimization in Wireless Sensor Networks: A Brief Survey, IEEE April 2011.

Amol P. Bhondekar, Member, IAENG, Renu Vig, Madan Lal Singla, C Ghanshyam, Pawan Kapur, Genetic Algorithm Based Node Placement Methodology For Wireless Sensor Networks, Proceedings of the International MultiConference of Engineers

and Computer Scientists 2009 Vol I IMECS 2009, March 18-20, 2009.

Frederico Paiva, Fabiola Guerra Nakamura and Ger- aldo Robson Mateus, Hybrid Approach to solve the Coverage and Connectivity Problem inWireless Sensor Networks, 2008.

Neeraj Jaggi and Alhussein A. Abouzeid, Energy-Efficient Connected Coverage in Wireless Sensor Networks, 2007.


  • There are currently no refbacks.

Copyright © IJETT, International Journal on Emerging Trends in Technology