ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Distributed Load Balancing Algorithm for Wireless Sensor Network


Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, India
2 Department of Computer Science and Engineering, Universal College of Engineering and Technology, Vallioor, India
3 Department of Computer Science, The Madurai Diraviyam Thayumanavar Hindu College, India
     

   Subscribe/Renew Journal


A Wireless Sensor Network (WSN) comprises of spatially scattered autonomous sensors to screen physical or natural conditions and to amiably go their information through the system to a Base Station. Grouping is a basic assignment in Wireless Sensor Networks for vitality effectiveness and system quality. Grouping through Central Processing Unit in remote sensor systems is outstanding and being used for quite a while. In this paper, we propose a few procedures that balance the vitality utilization of these hubs and guarantee greatest system lifetime by adjusting the activity stack as similarly as could be expected under the circumstances. Directly grouping through dispersed strategies is being produced for conveying with the issues like system lifetime and vitality. In our work, we connected both concentrated and conveyed k-means clustering calculation in system test system. K-means is a model based algorithm that surrogates between two noteworthy advances, passing on perceptions to groups and processing cluster focuses until the point when a ceasing standard is satisfied. Improved results are accomplished and related which demonstrate that conveyed clustering is compelling than brought together grouping.

Keywords

Wireless Sensor Network, Clustering, K-Means, Network Stability.
Subscription Login to verify subscription
User
Notifications
Font Size

  • F. Zhao and L.J. Guibas, “Wireless Sensor Networks: An Information Processing Approach”, Morgan Kaufmann Publishers, 2004.

  • V. Raghunathan, C. Schurgers, S. Park and M.B. Srivastava, “Energy-Aware Wireless Micro Sensor Networks”, IEEE Signal Processing Magazine, Vol. 19, No. 2, pp. 40-50, 2002.

  • Jennifer Yick, Biswanath Mukherjee and Dipak Ghosal, “Wireless Sensor Network Survey”, Computer Networks, Vol. 52, No. 12, pp. 2292-2330, 2008.

  • A. Ameer Ahmed Abbasi and Mohamed Younis, “A Survey on Clustering Algorithms for Wireless Sensor Networks”, Computer Communications, Vol. 30, No. 14-15, pp. 2826-2841, 2007.

  • S. Bandyopadhyay and E.J. Coyle, “An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks”, Proceedings of IEEE 22nd Annual Joint Conference on IEEE Computer and Communications Societies, pp. 1713-1723, 2003.

  • O. Younis, M. Krunz and S. Ramasubramanian, “Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges”, IEEE Networks, Vol. 20, No. 3, pp. 20-25, 2006.

  • S.P. Lloyd, “Least-Squares Quantization in PCM”, IEEE Transactions on Information Theory, Vol. 28, No. 2, pp. 129-137, 1982.

  • C.R. Lin and M. Gerla, “Adaptive Clustering for Mobile Wireless Networks”, IEEE Journal on Selected Areas in Communications, Vol. 15, No. 7, pp. 1265-1275, 1997.

  • I.S. Dhillon and D.S. Modha, “A Data-Clustering Algorithm on Distributed Memory Multiprocessors”, Large-Scale Parallel Data Mining, pp. 245-260, 2000.

  • R.O. Duda, P.E. Hart and D.G. Stork, “Pattern Classification”, 2nd Edition, Wiley, 2002.

  • P.A. Forero, A. Cano and G.B. Giannakis, “Distributed Feature-based Modulation Classification using Wireless Sensor Networks”, Proceedings of IEEE International Conference on Military Communications, pp. 1-5, 2008.

  • P.A. Forero, A. Cano and G.B. Giannakis, “Consensus-Based-Means Algorithm for Distributed Learning using Wireless Sensor Networks”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 11-14, 2008.

  • P.A. Forero, A. Cano and G.B. Giannakis “Consensus-based Distributed Expectation-Maximization Algorithm for Density Estimation and Classification using Wireless Sensor Networks”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1989-1992, 2008.

  • C. Godsil and G. Royle, “Algebraic Graph Theory”, Springer, 2000.

  • Seema Shivapur, Suvarna G. Kanakaraddi and A.K. Chikaraddi, “Load Balancing Techniques in Wireless Sensor Networks: A Comparative Study”, International Journal of Emerging Technology in Computer Science and Electronics, Vol. 14, No. 2, pp. 21-28, 2015.

  • Dipak Wajgi and Nileshsingh V. Thakur, “Load Balancing based Approach to Improve Lifetime of Wireless Sensor Network”, International Journal of Wireless and Mobile Networks, Vol. 4, No. 4, pp. 12-17, 2012.

  • Roshani Talmale, M. Nirupama Bhat and Nita Thakare, “Analysis of Load Balancing and Fault Tolerant Routing Protocol for Wireless Sensor Network”, Helix, Vol. 8, No. 5, pp. 3946-3949, 2018.


Abstract Views: 576

PDF Views: 1




  • Distributed Load Balancing Algorithm for Wireless Sensor Network

Abstract Views: 576  |  PDF Views: 1

Authors

K. Navaz
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, India
S. Athinarayanan
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, India
S. Sameena
Department of Computer Science and Engineering, Universal College of Engineering and Technology, Vallioor, India
R. Kavitha
Department of Computer Science, The Madurai Diraviyam Thayumanavar Hindu College, India

Abstract


A Wireless Sensor Network (WSN) comprises of spatially scattered autonomous sensors to screen physical or natural conditions and to amiably go their information through the system to a Base Station. Grouping is a basic assignment in Wireless Sensor Networks for vitality effectiveness and system quality. Grouping through Central Processing Unit in remote sensor systems is outstanding and being used for quite a while. In this paper, we propose a few procedures that balance the vitality utilization of these hubs and guarantee greatest system lifetime by adjusting the activity stack as similarly as could be expected under the circumstances. Directly grouping through dispersed strategies is being produced for conveying with the issues like system lifetime and vitality. In our work, we connected both concentrated and conveyed k-means clustering calculation in system test system. K-means is a model based algorithm that surrogates between two noteworthy advances, passing on perceptions to groups and processing cluster focuses until the point when a ceasing standard is satisfied. Improved results are accomplished and related which demonstrate that conveyed clustering is compelling than brought together grouping.

Keywords


Wireless Sensor Network, Clustering, K-Means, Network Stability.

References