A Prediction Algorithm based on Markov Chains for finding the Minimum Cost Path in a Mobile WSNs
Keywords:
Markov model, RSSI, MWSNAbstract
In this paper we propose the usage of a prediction technique based on Markov Chains to predict nodes positions with the aim of obtain short paths at minimum energy consumption. Specifically, the valuable information from the mobility prediction method is provided to our distributed routing algorithm in order to take the best network decisions considering future states of network resources. In this sense, in each network node, the mobility method employed is based on a Markov model to forecast future RSSI states of neighboring nodes for determining if they will be farther or closer within the next steps. The approach is evaluated considering different algorithms such as: Distance algorithm, Distance Away algorithm and Random algorithm. In addition, with the aim of performing comparisons against optimal values, we present a mathematical optimization model for finding the minimum cost path between a source and a destination node considering all network nodes are mobile. This paper is an extended variant of [8].References
Ahmed A. A. (2013); An enhanced real-time routing protocol with load distribution for mobile wireless sensor networks, Computer Networks, 57, 2013. https://doi.org/10.1016/j.comnet.2013.02.003
Ahmed A. A. (2007); Real-Time Wireless Sensor Networks, University of Virginia, 2007.
Akyildiz, I. F.; Vuran, M. C. (2010); Wireless Sensor Networks, Vol. 4, John Wiley & Sons, Hoboken, 2010.
Buchli, B.; Sutton, F.; Beutel, J. (2012); GPS-Equipped Wireless Sensor Network Node for High-Accuracy Positioning Applications, Wireless Sensor Networks Lecture Notes in Computer Science, Springer, 7158, 179-195, 2012.
De Araujo, G. M.; J. Kaiser, J.; Becker, L. B. (2012); An Optimized Markov Model to Predict Link Quality in Mobile Wireless Sensor Networks, Computers and Communications, 307-312, 2012.
De Araujo, G. M.; J. Kaiser, J.; Becker, L. B. (2014), Genetic Machine Learning Approach for Link Quality Prediction in Mobile Wireless Sensor Networks, Cooperative Robots and Sensor Networks, 1-14, 2014.
Li, S.; Ma, X.; Wang, X.; Tan, M. (2011); Energy-efficient multipath routing in wireless sensor network considering wireless interference, Journal of Control Theory and Applications, 9(1), 127-132, 2011. https://doi.org/10.1007/s11768-011-0263-4
Montoya, G. A.; Donoso, Y. (2018); A Prediction Algorithm based on Markov Chains for finding the Minimum Cost Path in a Mobile Wireless Sensor Network, Proceedings of the 7th International Conference on Computers Communications and Control, IEEE, 169 - 175, 2018.
Torkestani, J. A. (2012); Mobility prediction in mobile wireless networks. Journal of Network and Computer Applications, 35(5), 1633-1645, 2012. https://doi.org/10.1016/j.jnca.2012.03.008
Zheng, J.; Jamalipour, A. (2009); Wireless Sensor Networks: A Networking Perspective, Wiley, 2009.
[Online] A Community Resource for Archiving Wireless Data At Dartmouth. http://crawdad.org/.
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
