D. Vendhan, Kamaraj College of Engineering and Technology; Alagumeena A ,Kamaraj College of Engineering and Technology; Cithira S ,Kamaraj College of Engineering and Technology; Svetha M ,Kamaraj College of Engineering and Technology

Wireless Sensor Networks, Cluster Head, Base Station

The basic architecture of Wireless Sensor Networks is usually a hybrid type where it is a combination of infrastructure oriented and infrastructure less networks. The Communication from sensor to sensor head takes place through peer-to-peer architecture (infrastructure less) and the communication from Cluster Head to Base Station (BS) involves Broadcast Based (Infrastructure Oriented).This Hybrid architecture is to reduce the energy consumption of sensor nodes as it will be depleted soon when each sensor broadcasts sensed data to base station as and when it senses. Hence a cluster head will be elected for each cluster by considering the battery, memory and processing ability [2] [9]. All the sensors will send their sensed data to the cluster head in a peer-to-peer manner. Cluster head recovers the data and generates the signature using elgammal. Base station sends request to cluster heads of high & heterogeneous clusters and can receive the recovered data from cluster head by verifying signature. The encrypted binary packets are accumulated in BS and it is fed to a database after verification of packets from clusters.
    [1] T. Palpanas, “Real-time data analytics in sensor networks”, I Managing and Mining Sensor Data, Aggarwal Ed. New York, NY, USA: Springer, 2012. [2] H. Jiang, S. Jin, and C.Wang “Prediction or not? An energy efficient framework for clustering-based data collection in wireless sensor networks” IEEE Trans. Parallel Diatribe, Syst., vol. 22, no. 6, pp. 1064–1071, Jun 2011. [3] D. Tulane and S. Madden, “PAQ: Time series forecasting for approximate query answering in sensor networks” in Proc. Eur. Workshop Wireless Sen. Newt. , 2006, pp. 21–37. [4] R. A. F. Mini, M. D. V. Machado, A. A. F. Loureiro, and B. Nath, “Prediction-based energy map for wireless sensor networks” Ad Hoc Netw., vol. 3, pp. 235–253, 2005. [5] C. Luo, F. Wu, J. Sun, and C. W. Chen, “Efficient measurement generation and pervasive sparsity for compressive data gathering” IEEE Trans. Wireless Common., vol. 9, no. 12, pp. 3728–3738, Dec. 2010. [6] J. Rousselot, A. El-Hoiydi, and J.-D. Decotignie, “Low power medium access control protocols for wireless sensor networks,” in Proc. Eur. Wireless Conf., Jun. 2008, pp. 1–5. [7] D. Chu, A. Deshpande, J. M. Hellerstein, and W. Hong, “Approximate data collection in sensor networks using probabilistic models” in 22nd Int. Conf. Data Eng., 2006, p. 48. [8] Silberstein, G. Filpus, K. Munagala, and J. Yang, “Data-driven processing in sensor networks” in Proc. Conf. Innovative Data Syst. Res., 2007, pp. 10–21. [9] M. Hassani, E. Mller, P. Spaus, A. Faqolli, T. Palpanas, and T.Seidl, “Self-organizing energy aware clustering of nodes in sensor networks using relevant attributes,” in Proc. 4th Int. Workshop Knowl. Discovery Sen. Data, 2010, pp. 87–96. [10] D. Tulone and S. Madden, “An energy-efficient querying framework in sensor networks for detecting node similarities,” in Proc.Int. Conf. Modeling, Anal. Simul. Wireless Mobile Syst., 2006, pp. 191–300.
Paper ID: GRDCF002023
Published in: Conference : International Conference on Innovations in Engineering and Technology (ICIET - 2016)
Page(s): 94 - 98