IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 8 - Issue 11, November 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Energy Efficient Object Tracking Using Adaptive Node Deployment And Evolutionary Algorithm Based Node Localization

[Full Text]

 

AUTHOR(S)

Dr. P. Prabaharan, Dr. R. Jayavadivel, Dr. L. Malathi and M. Ramesh

 

KEYWORDS

Target Tracking, Node Deployment, Node Localization, Virtual Force Algorithm, Adaptive Micro Genetic Algorithm.

 

ABSTRACT

Target tracking is the predominant application of wireless sensor networks (WSNs) which are used in security and military environments. The initial step in any WSN based application is Node deployment and localization. Random deployment used by many WSN, leads to coverage hole problem which will cause a greater performance drop. Localization is the process by which the location of the sensor nodes is estimated relative to the position of anchor nodes. These anchor nodes are deployed with predetermined positions. Based on various distance measures localization can be considered as unconstrained optimization problem. In this work, a target tracking framework is proposed with hybrid node deployment and evolutionary algorithm based node localization. For node deployment the Adaptive Virtual Force algorithm (AVFA) and for node localization, the adaptive micro-genetic algorithm (AMGA) is proposed. During node deployment, the static nodes are deployed randomly, the area coverage and coverage hole are calculated, and if coverage hole exits then the mobile nodes are deployed for patching the coverage hole using AVFA. Finally the complete target tracking framework is described and the overall system is simulated. The result of each stage is described and it is compared with the existing well known algorithm.

 

REFERENCES

[1] Ahmed.N, S. S Kanhere, and S. Jha, “The Holes Problem in Wireless Sensor Networks: A Survey,” ACM SIGMOBILE Mobile Computing and Communications Review” , vol. 9, no. 2, pp. 4-8, 2005.
[2] Vincent Tam, King-Yip Cheng and King-Shan Lu “Improving Localization in Wireless Sensor Networks with An Evolutionary Algorithm” ,Communications and Networking Conference, 2006. CCNC 2006. 3rd IEEE, Volume: 1.
[3] CullerD D, Estrin, D& Srivastava, M 2004, “Overview of Sensor Networks”, Computer Magazine, IEEE, vol. 37, no. 8, pp. 41-49.
[4] Prabaharan .P and Nelson kennedy Babu.C “Efficient Tracking Using Hybrid Clustering And Low Power Prediction Mechanism”, Australian Journal of Basic and Applied Sciences, 9(7) April 2015, Pages: 794-802 .
[5] Wang, G, Cao, G & La Porta, T 2003, ‘A bidding protocol for deploying mobile sensors’, in Proceedings of the 11th IEEE International Conference on Network Protocol (ICNP ’03), pp. 315-324.
[6] Ganeriwal, S, Kansal, A & Srivastava, M 2004, ‘Self aware actuation for fault repair in sensor networks’, in Proc. IEEE Int. Conf. Robot. Autom., vol. 5, pp. 5244-5249.
[7] Nadeem Ahmed, Salil S Kanhere & Sanjay Jha 2011, ‘A pragmatic approach to area coverage in hybrid wireless sensor networks’, Wireless Communications and Mobile Computing Wireless Commun. Mobile Computing, vol.11, pp.23-45.
[8] Babaie, S & Pirahesh, SS 2012,‘Hole detection for increasing coverage in wireless sensor network using triangular structure’, International Journal of Computer Science Issues, vol. 9, no. 1, pp.213-218.
[9] Bing Cheng 2014, ‘Modified Particle Swarm Optimization for Hybrid Wireless Sensor Networks Coverage’, Journal of Networks,doi:10.4304/jnw.9.01.56-62,vol.9, no.1,pp.56-62.
[10] Omar Banimelhem, Moad Mowafi & Walid Aljoby 2013, ‘Genetic Algorithm Based Node Deployment in Hybrid Wireless Sensor Networks’, Communications and Network, http://dx.doi.org/10.4236/cn.2013.54034vol.5, pp.273-279.
[11] Shang, W. Ruml, and Y. Zhang. Localization from mere connectivity. In ACM MobiHoc , 2003.
[12] Shang and W. Ruml. Improved MDS-based localization. In IEEE Infocom , 2004
[13] X. Ji and H. Zha. Sensor positioning in wireless sensor networks using multidimensional scaling. In IEEE Infocom , 2004.
[14] Niculescu and B. Nath. Ad hoc positioning system (aps). In IEEE Globecom , pages 2926 – 2931, 2001.
[15] Savvides, H. Park, and M. Srivastava. The bits and flops of the n-hop multilateration primitive for node localization problems. In ACM International Workshop on Wireless Sensor Networks and Applications (WSNA) , 2002
[16] Doherty, K. Pister, and L. Ghaoui. Convex position estimation in wireless sensor networks. In IEEE Infocom , 2001
[17] K. Cheng, V. Tam, and K. Lui. Improving aps with anchor selection in anisotropic networks. In Proceedings of the International Conference on Networking and Services (ICNS’05) , October 2005 H. Lim and J. C. Hou. Localization for anisotropic sensor networks. In IEEE Infocom , 2005
[18] Lim and J. C. Hou. Localization for anisotropic sensor networks. In IEEE Infocom, 2005
[19] J. Li, B. Zhang, L. Cui and S. Chai, (2012) ’An Extended Virtual Force-Based Approach to Distributed Self-Deployment in Mobile Sensor Networks’, International Journal of Distributed Sensor Networks.
[20] A. Howard, M. J. Mataric and G. S. Sukhatme, “Mobile Sensor Network Deployment Using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem,” In Proceedings of 6th Int. Conf. Distributed Autonomous Robotic System, Fukuoka, Japan, pp. 299-308, 2002.
[21] Nonlinear Programming (2nd Edition). Athena Scientific, 1999