Abstract
Limited energy supply is a major concern when dealing with wireless sensor networks (WSNs). Therefore, routing protocols for WSNs should be designed to be energy efficient. This chapter considers a learning-based routing protocol for WSNs with mobile nodes, which is capable of handling both centralized and decentralized routing. A priori knowledge of the movement patterns of the nodes is exploited to select the best routing path, using a Bayesian learning algorithm. While simulation tools cannot generally prove that a protocol is correct, formal methods can explore all possible behaviors of network nodes to search for failures. We develop a formal model of the learning-based protocol and use the rewriting logic tool Maude to analyze both the correctness and efficiency of the model. Our experimental results show that the decentralized approach is twice as energy-efficient as the centralized scheme. It also outperforms the power-sensitive AODV (PS-AODV), an efficient but non-learning routing protocol. Our formal model of Bayesian learning integrates a real data-set which forces the model to conform to the real data. This technique seems useful beyond the case study of this chapter.
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References
Dijkstra, E.W.: A note on two problems in connection with graphs. Numer. Math. 1, 269–271 (1959)
Mitchell, T.M.: Machine Learning (ISE Editions). McGraw-Hill, Boston (1997)
Clavel, M., Durán, F., Eker, S., Lincoln, P., MartÃ-Oliet, N., Meseguer, J., Quesada, J.F.: Maude: specification and programming in rewriting logic. Theoret. Comput. Sci. 285, 187–243 (2002)
Meseguer, J.: Conditional rewriting logic as a unified model of concurrency. Theoret. Comput. Sci. 96, 73–155 (1992)
Kazemeyni, F., Owe, O., Johnsen, E.B., Balasingham, I.: Learning-based routing in mobilewireless sensor networks: applying formal modeling and analysis. In: Proceedings of IEEE 14th International Conference on Information Reuse and Integration—Workshop on Formal Methods Integration (FMi’13), pp. 1–8. IEEE (2013)
Olagbegi, B.S., Meghanathan, N.: A review of the energy efficient and secure multicast routing protocols for mobile ad hoc networks. CoRR, abs/1006.3366 (2010)
Liu, M., Cao, J., Chen, G., Wang, X.: An energy-aware routing protocol in wireless sensor networks. IEEE Sens. 9(1), 445–462 (2009)
Wang, J., Cho, J., Lee, S., Chen, K-C., Lee, Y-K.: Hop-based energy aware routing algorithm for wireless sensor networks. IEICE Trans. 93-B(2), 305–316 (2010)
Stojmenovic, I., Lin, X.: Power-aware localized routing in wireless networks. IEEE Trans. Parallel Distrib. Syst. 12(11), 1122–1133 (2001)
Uddin, M.Y.S., Ahmadi, H., Abdelzaher, T., Kravets, R.: A low-energy, multi-copy inter-contact routing protocol for disaster response networks. In: Proceedings of 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON’09), pp. 637–645. IEEE Press (2009)
Arroyo-Valles, R., Alaiz-Rodriguez, R., Guerrero-Curieses, A., Cid-Sueiro, J.: Q-probabilistic routing in wireless sensor networks. In: Proceedings of 3rd International Conference on Intelligent Sensors, Sensor Networks and Information (ISSNIP’07), pp. 1–6 (2007)
Barrett, C.L., Eidenbenz, S.J., Kroc, L., Marathe, M., Smith, J.P.: Parametric probabilistic routing in sensor networks. Mob. Netw. Appl. J. 10, 529–544 (2005)
Lindgren, A., Doria, A., Schelén, O.: Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mob. Comput. Commun. Rev. 7, 19–20 (2003)
Wang, P., Wang, T.: Adaptive routing for sensor networks using reinforcement learning. In: Proceedings of the Sixth International Conference on Computer and Information Technology (CIT ’06), pp. 219–219. IEEE Computer Society (2006)
Pandana, C., Liu, K.J.R.: Near-optimal reinforcement learning framework for energy-aware sensor communications. IEEE J. Sel. Areas Commun. 23, 209–232 (2002)
Coleri, S., Ergen, M., Koo, T.J.: Lifetime analysis of a sensor network with hybrid automata modelling. In: Proceedings of first ACM International Workshop on Wireless Sensor Networks and Applications (WSNA’02), pp. 98–104. ACM (2002)
Fehnker, A., van Hoesel, L., Mader, A.: Modelling and verification of the LMAC protocol for wireless sensor networks. In: Proceedings of the 6th International Conference on Integrated Formal Methods (IFM’07). Lecture Notes in Computer Science, vol. 4591, pp. 253–272. Springer (2007)
Tschirner, S., Xuedong, L., Yi, W.: Model-based validation of QoS properties of biomedical sensor networks. In: Proceedings of the 8th ACM & IEEE International conference on Embedded software (EMSOFT’08), pp. 69–78. ACM (2008)
Johnsen, E.B., Owe, O., Bjørk, J., Kyas, M.: An object-oriented component model for heterogeneous nets. In: Proceedings of the 6th International Symposium on Formal Methods for Components and Objects (FMCO 2007). Lecture Notes in Computer Science, vol. 5382, pp. 257–279. Springer (2008)
Ölveczky, P.C., Thorvaldsen, S.: Formal modeling, performance estimation, and model checking of wireless sensor network algorithms in real-time maude. Theoret. Comput. Sci. 410(2–3), 254–280 (2009)
Katelman, M., Meseguer, J., Hou, J.C.: Redesign of the LMST wireless sensor protocol through formal modeling and statistical model checking. In: Proceedings of the 10th International Conference on Formal Methods for Open Object-Based Distributed Systems (FMOODS’08). Lecture Notes in Computer Science, vol. 5051, pp. 150–169. Springer (2008)
Dong, J.S., Sun, J., Sun, J., Taguchi, K., Zhang, X.: Specifying and verifying sensor networks: an experiment of formal methods. In: 10th International Conference on Formal Engineering Methods (ICFEM’08), Lecture Notes in Computer Science, vol. 5256, pp. 318–337. Springer (2008)
Kulkarni, S.A., Rao, G.R.: Formal modeling of reinforcement learning algorithms applied for mobile ad hoc network. Int. J. Recent Trends Eng. (IJRTE) 2, 43–47 (2009)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artificial Intell. Res. 4, 237–285 (1996)
Box, G.E.P., Tiao, G.C.: Bayesian Inference in Statistical Analysis (Wiley Classics Library). Wiley-Interscience, New York (1992)
Shakya, S., McCall, J., Brown, D.: Using a Markov network model in a univariate EDA: an empirical cost-benefit analysis. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO’05, pp. 727–734. ACM (2005)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Plotkin, G.D.: A structural approach to operational semantics. J. Logic and Algebraic Program. (JLAP) 60–61, 17–139 (2004)
Kalbfleisch, J.G.: Probability and Statistical Inference, Vol. 1: Probability (Springer Texts in Statistics). Springer, Secaucus (1985)
Agha, G., Meseguer, J., Sen, K.: PMaude: rewrite-based specification language for probabilistic object systems. Electron. Notes Theoret. Comput. Sci. 153(2), 213–239 (2006)
Kazemeyni, F., Owe, O., Johnsen, E.B., Balasingham, I.: Learning-based routing in mobile wireless sensor networks: formal modeling and analysis for WSNs. Technical Report ISBN 82-7368-390-7, Department of Informatics, University of Oslo (2013)
Scott, J., Gass, R., Crowcroft, J., Hui, P., Diot, C., Chaintreau, A.: CRAWDAD trace: cambridge/haggle/imote/intel (v. 2006–01-31). http://crawdad.cs.dartmouth.edu/cambridge/haggle/imote/intel (2006)
Kazemeyni, F., Johnsen, E.B., Owe, O., Balasingham, I.: Formal modeling and validation of a power-efficient grouping protocol for WSNs. J. Logic Algebraic Program. (JLAP) 81(3), 284–297 (2012)
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Kazemeyni, F., Owe, O., Johnsen, E.B., Balasingham, I. (2014). Formal Modeling and Analysis of Learning-Based Routing in Mobile Wireless Sensor Networks. In: Bouabana-Tebibel, T., Rubin, S. (eds) Integration of Reusable Systems. Advances in Intelligent Systems and Computing, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-319-04717-1_6
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