A Study on Data Collection Behaviors for a Class of Multi-Hop Wireless Sensor Networks Based on Probability Model
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This paper presents an approach to analyze data collection behavior for a class of multi-hop wireless sensor networks (WSNs). Based on probabilistic modeling of the IEEE 802.15.4 MAC protocol, the collected data is estimated using a pseudo-random method of Monte Carlo simulation. Then data collection behavior is modeled as the mean and variation of the probability distribution function curves. Effects of expanding the network size on reception rate were studied by varying the number of nodes in each cluster and hop count. Estimating the amount of data to be collected at the sink node, survivor function and confidence interval have been proposed as metrics for the reliability of data collection.
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I. F. Akyildiz and M. C. Vuran, Wireless Sensor Networks, 2010, John Wiley & Sons.
Wang, W., Peng, Y., An improved routing algorithm for ZigBee Networks, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 2321-2325.
N. Patwari,et al,Locating the Nodes: Cooperative Localization in Wireless Sensor Networks, IEEE Signal Processing Magazine, 2005, 22, pp. 54 – 69.
Malathi, L., Gnanamurthy, R.K., Energy efficient data collection framework for WSN with layers and uneven clusters, (2014) International Review on Computers and Software (IRECOS), 9 (4), pp. 701-709.
Demirkol, C. Ersoy, and F. Alagoz,MAC Protocols for Wireless Sensor Networks: a Survey, IEEE Communications Magazine, 2006, 44, pp. 115-121.
Paolo Baronti,et al, Wireless sensor networks: A survey on the state of the art and the 802.15.4 and ZigBee standards, Computer Communications, 2007, 30, pp. 1655–1695.
Buratti and R. Verdone, Performance Analysis of IEEE 802.15.4 Non Beacon-Enabled Mode, IEEE Trans. on Vehicular Technology, 2009, 58, pp. 3480-3493.
Tashtarian, F., Haghighat, A.T., Yaghmaee, M.H., Mazinani, S.M., Honary, M.T., On global clustering algorithm: Layer-oriented approach for multi hop wireless sensor networks, (2009) International Review on Computers and Software (IRECOS), 4 (5), pp. 567-576.
B. Hull, K. Jamieson and H. Balakrishnan, Mitigating congestion in wireless sensor networks, Proceedings of the 2nd international conference on Embedded networked sensor systems, 2004.
Muhammad Faheem et al, Energy Based Efficiency Evaluation of Tree-Based Routing Protocols for Wireless Sensor Networks (WSNs),International Review on Computers and Software (IRECOS), 2013, 8(3), pp.688-697.
G. Shafer and P.P. Shenoy, Probability Propagation, Annals of Mathematics and Artificial Intelligence, 1990, 2, pp. 327-351.
Rubinstein RY. Simulation and the Monte Carlo method, 1989, John Wiley &Sons,lnc.
Luis D. Hern~indez, Serafin Moral ,Antonio Salmerdn, A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques, (1998) International Journal of Approximate Reasoning, 18, pp. 53-91.
A. Cerpa, J. L. Wong, L. Kuang, M. Potkonjak and D. Estrin., Statistical Model of Lossy Links in Wireless Sensor Networks.,CENS Technical Report 0041, 2004.
K.Jaksukam and S.Vorapojpisut, A Probability Model of a Class of Multi-hop WSNs, International Conference on Embedded Systems and Intelligent Technology (ICESIT), 2014.
Wichmann F.A., and Hill N.J., The psychometric function: I. Fitting, sampling, and goodness of fit, Percept Psychophysics. , 2001,63(8), pp.1293-313.
Rameshwar D. Gupta and DebasisKundu, Generalized Logistic Distributions,Journal of Applied Statistical Sciences, 2010, 18 (1), pp. 51-66.
Öztürk, F. veİhsanKarabulut, Interval estimators for the parameters of the normal distribution, Communications, Series A1: Mathematics and Statistics, 2006, 55(1), pp.23-32.
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