Design and Development of an Energy-Efficient Sensory Data Collection with Mobile Sinks Consuming Cluster Constructed Rendezvous Node
DOI:
https://doi.org/10.51983/ajcst-2017.6.2.1786Keywords:
WSN, cluster Head, Rendezvous Node(RN)Abstract
In Wireless Sensor Networks (WSN) applications include an arrangement of separated urban zones secured by sensor hubs (SNs) checking ecological parameters. Single-bounce exchange of information from SNs that exist in the MS’s range or overwhelming contribution of system outskirts hubs in information recovery, preparing, buffering and conveying errands. These hubs risk fast itality fatigue bringing about the loss of system availability and diminished system lifetime. Proposed framework goes for limiting the general system overhead and vitality use related with the multihop information recovery handle while additionally guaranteeing adjusted vitality utilization among SNs and delayed system lifetime. This is accomplished through building bunch structures comprised of part hubs that course their deliberate information to their doled out group head (CH). CHs perform information separating upon crude information misusing potential spatial-transient information access and forward the sifted data to fitting end hubs with adequate lingering vitality, situated in nearness to the MS’s direction. This approach fabricates a bunching structure on top of the sensor arrange. That way, high information collection proportions are conceivable since information from the hubs of a similar bunch, for the most part, are firmly connected and along these lines accumulation at each group head impressively diminishes the information sent to RNs. This thusly prompts much lower vitality utilization in the WSN and furthermore a great deal less information is cradled at RNs, diminishing so the likelihood of support floods at an RN.
References
E. Hamida and G. Chelius, “Strategies for Data Dissemination to Mobile Sinks in Wireless Sensor Networks,” IEEE Wireless Comm., vol. 15, no. 6, pp. 31-37, Dec. 2008.
S. Olariu and I. Stojmenovic, “Design Guidelines for Maximizing Lifetime and Avoiding Energy Holes in Sensor Networks with Uniform Distribution and Uniform Reporting,” Proc. IEEE INFOCOM, 2006.
J. Luo and J-P. Hubaux, “Joint Mobility and Routing for Lifetime Elongation in Wireless Sensor Networks,” Proc. IEEE INFOCOM, 2005.
M. Demirbas, O. Soysal, and A. Tosun, “Data Salmon: A Greedy Mobile Basestation Protocol for Efficient Data Collection in Wireless Sensor Networks,” Proc. Int’l Conf. Distributed Computing in Sensor Systems ( DCOSS ’07 ), pp. 267-280, 2007.
S. Greenberg (1988), ‘Using Unix: Collected Traces of 168 Users’, master’s thesis, Dept. of Computer Science, Univ. of Calgary, Alberta, Canada.
Y. Horman and Kaminka G.A.(2007), ‘Removing Biases in Unsupervised Learning of Sequential Patterns’, Intelligent Data Analysis, vol. 11, no. 5, pp. 457-480.
J.A. Iglesias, A. Ledezma, and A. Sanchis. (2009), ‘Creating User Profiles from a Command-Line Interface: A Statistical Approach’, Proc. Int’l Conf. User Modeling, Adaptation, and Personalization (UMAP), pp. 90-101.
N. Kasabov. (Dec. 2001), ‘Evolving Fuzzy Neural Networks for Supervised/ Unsupervised Knowledge-Based Learning’, IEEE Trans. Systems, Man and Cybernetics-Part B: Cybernetics, vol. 31, no. 6, pp. 902-918.
D. Kalles and T. Morris. (1996), ‘Efficient Incremental Induction of Decision Trees,”’ Machine Learning, vol. 24, no. 3, pp. 231- 242.
Schonlau M. Dumouchel W. Ju W.H. Karr A.F. and Theus(2001), ‘Computer Intrusion: Detecting Masquerades’, Statistical Science, vol. 16, pp. 58-74.
F.J.Ferrer-Troyano, J.S. Aguilar-Ruiz and J.C.R Santos (2006), ‘Data Streams Classification by Incremental Rule Learning with Parameterized Generalization’, Proc. ACM Symp. Applied Computing (SAC), pp. 657-661.
T. Kohonen, J. Kangas, J. Laaksonen, and K. Torkkola .(1992), ‘Lvq pak: A Program Package for the Correct Application of Learning Vector Quantization Algorithms’, Proc. IEEE Int’l Conf. Neural Networks, pp. 725-730.
D. Godoy and A. Amandi. (2005), ‘User Profiling in Personal Information Agents: A Survey’, Knowledge Eng. Rev., vol. 20, no.4, pp. 329-361.
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