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EER-Al:An Energy Efficient Routing Protocol Based on Automated Learning Method


Affiliations
1 Department of Computer Engineering, Istanbul Sabahttin Zaim University, Turkey
     

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The issue of energy in a wireless sensor network is one of the most important challenges for these networks. This issue is also being considered today in the new IoT topic. This paper studies the ability of the learning automata model to solve the problem in the sensor networks. Because they have capabilities such as low computational load, ability to use in distributed environments, and inaccurate information, require the least feedback from the environment, etc. One of the solutions to energy optimization is to provide routing protocols. In the routing area, a routing protocol based on learning automata has been proposed in which the network lifetime criterion is considered. The simulation results and the comparison of the proposed protocol with other protocols indicate that this protocol has better performance in the energy conversation and network lifetime.

Keywords

Wireless Sensor Networks, Energy Efficiency, Routing Protocol, Fault Tolerance, Automated Learning.
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  • Z. Mohammed and E. Ahmed, “Internet of Things Applications, Challenges and Related Future Technologies”, World Scientific News, Vol. 67, No. 2, pp. 126-148, 2017.

  • F. Kiani, “AR-RBFS: Aware-Routing Protocol Based on Recursive Best-First Search Algorithm for Wireless Sensor Networks”, Journal of Sensors, Vol. 2016, pp. 1-10, 2016.

  • L.B. Bhajantri and N. Nalini, “A Fault Tolerance Approach to Topology Control in Distributed Sensor Networks”, Proceedings of IEEE International Conference on Advanced Communication Control and Computing Technologies, pp. 208-212, 2012.

  • H. Bagci, I. Korpeoglu and A. Yazici, “A Distributed Fault-Tolerant Topology Control Algorithm for Heterogeneous Wireless Sensor Networks”, IEEE Transactıons on Parallel and Dıstrıbuted Systems, Vol. 26, No. 4, pp. 914-923, 2015.

  • F. Kiani, “Maximizing Wireless Sensor Network Lifetime Based on Linear Programming Method”, International Research Journal of Engineering and Technology, Vol. 3, No. 3, pp. 1354-1359, 2018.

  • J.N. Al-Karaki and A.E. Kamal, “Routing Techniques in Wireless Sensor Networks: A Survey”, Proceedings of the IEEE International Conference on Wireless Communications, pp. 6-28, 2004.

  • A. Sarkar and T. Murugan, “Routing Protocols for Wireless Sensor Networks: What the Literature Says?”, Alexandria Engineering Journal, Vol. 55, No. 4, pp. 3173-3183, 2016.

  • A. Avizienis, J.C. Laprie and B. Randell, “Fundamental concepts of dependability”, Proceedings of the Technical report, UCLA CSD Report no. 010028, 2001.

  • F. Koushanfar, M. Potkonjak and A. Sangiovanni, “Fault-Tolerance Techniques for Sensor Networks”, Proceedings of IEEE International Conference on Sensors, pp. 1491-1496, 2002.

  • V. Saritha, P. V. Krishna, S. Misra and M.S. Obaidat, “Learning Automata based Optimized Multipath routing using Leapfrog Algorithm for VANETs”, Proceedings of International Conference on Mobile and Wireless Networking, pp. 1-5, 2017.

  • K.S. Narendra and M.A.L. Thathachar, “Learning Automata: An Introduction”, Prentice Hall, 1989.

  • Z. Shariat, A. Movaghar and, M. Hoseinzadeh, “A Learning Automata and Clustering-based Routing Protocol for Named Data Networking”, Telecommunication System, Vol. 65, No. 1, pp. 9-29, 2017.

  • H. Ge and Sh. Li, “A Parameter-Free Learning Automaton Scheme”, Available at: https://pdfs.semanticscholar.org/4499/e181afb78f0ce8043accf2db37c3a90314c0.pdf

  • F. Kiani, “Reinforcement Learning Based Routing Protocol for Wireless Body Sensor Networks”, Proceedings of IEEE 7th International Symposium on Cloud and Service Computing, pp. 71-78, 2017.

  • K. Arulkuraman, M. Peter, M. Brundage and A. Bharath, “Deep Reinforcement Learning: A Brief Survey”, IEEE Signal Processing Magazine, Vol. 5, No. 3, pp. 26-38, 2017.

  • C. S. Chasparis, “Stochastic Stability of Perturbed Learning Automata in Positive-Utility Games”, Available at: https://arxiv.org/pdf/1709.05859.pdf

  • G. Barto and P. Anandan, “Pattern-Recognizing Stochastic Learning Automata”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 3, pp. 360-375, 1985.

  • S. Jabbar et al., “Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network”, Wireless Communications and Mobile Computing, Vol. 2018, pp. 1-21, 2018.

  • K. Arasu and R. Ganesan, “Effective Implementation of Energy Aware Routing for Wireless Sensor Network”, Materials Today: Proceedings, Vol. 5, No. 1, pp. 1186-1193, 2018.

  • S. Aswale and V.R. Ghorbade, “LQEAR: Link Quality and Energy-Aware Routing for Wireless Multimedia Sensor Networks”, Wireless Personal Communications, Vol. 97, No. 1, pp. 1291-1304, 2017.

  • M. Ilyas and I. Mahgoub, “Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems”, CRC Press, 2005.

  • H. Zhoue et al., “A Multiple-Dimensional Tree Routing Protocol for Multi sink Wireless Sensor Networks based on Ant Colony Optimization”, International Journal of Distributed Sensor Networks, Vol. 2012, pp. 1-10, 2012.

  • C. Intanagonwiwat et al., “Directed Diffusion for Wireless Sensor Networking”, IEEE/ACM Proceedings of the Transactions on Networking, Vol. 11, No. 1, pp. 2-16, 2003.

  • M. Ankit et al., “TinyLAP: A Scalable Learning Automata-Based Energy Aware Routing Protocol for Sensor Networks”, Proceedings of International Conference on Wireless and Communications, pp. 1-8, 2006.

  • D. De, W. Song and Sh. Tang, “EAR: An Energy and Activity-Aware Routing Protocol for Wireless Sensor Networks in Smart Environments”, The Computer Journal, Vol. 55, No. 12, pp. 1492-1506, 2012.

  • T. Roosta, “Probabilistic Geographic Routing protocol for Ad Hoc and Sensor Networks”, Proceedings of International Workshop Wireless Ad Hoc Networks, pp. 1-8, 2006.


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  • EER-Al:An Energy Efficient Routing Protocol Based on Automated Learning Method

Abstract Views: 189  |  PDF Views: 5

Authors

Farzad Kiani
Department of Computer Engineering, Istanbul Sabahttin Zaim University, Turkey

Abstract


The issue of energy in a wireless sensor network is one of the most important challenges for these networks. This issue is also being considered today in the new IoT topic. This paper studies the ability of the learning automata model to solve the problem in the sensor networks. Because they have capabilities such as low computational load, ability to use in distributed environments, and inaccurate information, require the least feedback from the environment, etc. One of the solutions to energy optimization is to provide routing protocols. In the routing area, a routing protocol based on learning automata has been proposed in which the network lifetime criterion is considered. The simulation results and the comparison of the proposed protocol with other protocols indicate that this protocol has better performance in the energy conversation and network lifetime.

Keywords


Wireless Sensor Networks, Energy Efficiency, Routing Protocol, Fault Tolerance, Automated Learning.

References