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An Energy-efficient Data Collection Scheme by Mobile Element based on Markov Decision Process for Wireless Sensor Networks

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Abstract

Wireless sensor networks allow efficient data collection and transmission in IoT environments. Since wireless sensor networks consist of a few to several sensor nodes which are spatially distributed, the data collection in the networks is a difficult task. Due to multi-hope connectivity and energy constraint, mobile elements are sent to the network to collect data from these sensor nodes directly by one hope communication. The mobile elements need an efficient moving strategy to minimize the data-gathering latency and energy consumption while maximizing the rate of gathering data. The goal is to estimate an effectual movement policy of the mobile elements that improve the rewards and data collection rate in non-stationary environments. In the proposed scheme, a movements policy for the mobile elements is formulated through the Markov decision process. The computer simulation shows that the proposed scheme significantly improves the data gathering rate and avoids the buffer overflow condition of the sensors to reduce the data loss and energy consumption of sensor nodes and mobile elements.

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References

  1. Kandris, D., Nakas, C., Vomvas, D., & Koulouras, G. (2020). Applications of wireless sensor networks: An up-to-date survey. Applied System Innovation, 3(1), 14.

    Article  Google Scholar 

  2. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Article  Google Scholar 

  3. Soundari, A. G., & Jyothi, V. (2020). Energy efficient machine learning technique for smart data collection in wireless sensor networks. Circuits, Systems, and Signal Processing, 39(2), 1089–1122.

    Article  Google Scholar 

  4. Jairam, B. G., & Ashoka, D. V. (2019). Multiple mobile elements based energy efficient data gathering technique in wireless sensor networks. In Digital Business, Lecture Notes on Data Engineering and Communications Technologies: Springer. Vol. 21.

  5. Singh, S. K., & Kumar, P. (2020). A comprehensive survey on trajectory schemes for data collection using mobile elements in WSNs. Journal of Ambient Intelligence and Humanized Computing, 11(1), 291–312.

    Article  Google Scholar 

  6. Ma, J., Shi, S., Gu, X., & Wang, F. (2020). Heuristic mobile data gathering for wireless sensor networks via trajectory control. International Journal of Distributed Sensor Networks, 16(5), 1550147720907052.

    Article  Google Scholar 

  7. Cheng, C.-F., & Yu, C.-F. (2015). Data gathering in wireless sensor networks: A combine–TSP–reduce approach. IEEE Transactions on Vehicular Technology, 65(4), 2309–2324.

    Article  Google Scholar 

  8. Chao, F., He, Z., Feng, R., Wang, X., Chen, X., Li, C., & Yang, Y. (2021). Predictive Trajectory-Based Mobile Data Gathering Scheme for Wireless Sensor Networks. Complexity, 2021, 1–17.

    Google Scholar 

  9. Raj, A., & Prakash, S. (2020). Path discovery approach for mobile data gathering in WSN. International Journal of Computer Applications in Technology, 64(2), 133–142.

    Article  Google Scholar 

  10. Marwaha, S., Tham, C. K., Srinivasan D. (2002). Mobile agents based routing protocol for mobile ad hoc networks. In IEEE. pp. 163–167.

  11. Alsheikh, M. A., Hoang, D. T., Niyato, D., Tan, H.-P., & Lin, S. (2015). Markov decision processes with applications in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 17(3), 1239–1267.

    Article  Google Scholar 

  12. Raj, P. P., Khedr, A. M., & Al, A. Z. (2020). Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization. Wireless Networks, 26(4), 2983–2998.

    Article  Google Scholar 

  13. Dash, D., Kumar, N., Ray, P. P., & Kumar, N. (2020). Reducing data gathering delay for energy efficient wireless data collection by jointly optimizing path and speed of mobile sink. IEEE Systems Journal, 15, 317.

    Google Scholar 

  14. Kumar, N., Dash, D. (2017). Maximum data gathering through speed control of path-constrained mobile sink in WSN. In IEEE; pp. 1–4.

  15. Najjar-Ghabel, S., Farzinvash, L., & Razavi, S. N. (2020). Mobile sink-based data gathering in wireless sensor networks with obstacles using artificial intelligence algorithms. Ad Hoc Network, 106, 102243.

    Article  Google Scholar 

  16. Abassi, S., Anis, I., Kashif, M., Tayab, U. B. (2021). Implimentation of novel framework for efficient data gathering with multiple mobile sink sensor nodes in WSN.

  17. He, X., Fu, X., & Yang, Y. (2019). Energy-efficient trajectory planning algorithm based on multi-objective PSO for the mobile sink in wireless sensor networks. IEEE Access, 7, 176204–176217.

    Article  Google Scholar 

  18. Aslanyan, H., Leone, P., Rolim, J. (2010). Data propagation with guaranteed delivery for mobile networks. Exp Algorithms. pp 386–397.

  19. Kim, D., Abay, B. H., Uma, R., Wu, W., Wang, W., Tokuta, A. O. (2012). Minimizing data collection latency in wireless sensor network with multiple mobile elements. In IEEE. pp. 504–512.

  20. Gao, S., Zhang, H., & Das, S. K. (2011). Efficient data collection in wireless sensor networks with path-constrained mobile sinks. IEEE Transactions on Mobile Computing, 10(4), 592–608.

    Article  Google Scholar 

  21. Sugihara, R., & Gupta, R. K. (2008). Improving the data delivery latency in sensor networks with controlled mobility. Springer.

    Book  Google Scholar 

  22. Akanksha, E., Sharma, N., Gulati, K. (2021). Review on reinforcement learning, research evolution and scope of application. In IEEE; pp. 1416–1423.

  23. Yun, W.-K., & Yoo, S.-J. (2021). Q-Learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks. IEEE Access., 9, 10737–10750.

    Article  Google Scholar 

  24. https://www.mathworks.com/matlabcentral/fileexchange/46629-tsp-zip.

  25. Johnson, D. S., & McGeoch, L. A. (1997). The traveling salesman problem: A case study in local optimization. Local search in combinatorial optimization, 1(1), 215–310.

  26. Davendra, D. (2010). Traveling salesman problem: Theory and applications. Springer.

    Book  Google Scholar 

  27. Yuan, Y., Tian, Z., Wang, C., Zheng, F., & Lv, Y. (2020). A Q-learning-based approach for virtual network embedding in data center. Neural Computing and Applications, 32(7), 1995–2004.

    Article  Google Scholar 

  28. Wang, Z. M., Basagni, S., Melachrinoudis, E., & Petrioli, C. (2005, January). Exploiting sink mobility for maximizing sensor networks lifetime. In Proceedings of the 38th annual Hawaii international conference on system sciences (HICSS’05), Hawaii.

  29. Rahimi, M., Shah, H., Sukhatme, G. S., Heideman, J., & Estrin, D. (2003, September). Studying the feasibility of energy harvesting in a mobile sensor network. In 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422) (Vol. 1, pp. 19–24). IEEE.

  30. Wang, Y.-C., Peng, W.-C., & Tseng, Y.-C. (2010). Energy-balanced dispatch of mobile sensors in a hybrid wireless sensor network. IEEE Transactions on Parallel and Distributed Systems, 21(12), 1836–1850.

    Article  Google Scholar 

  31. Lai, Y., Xie, J., Lin, Z., Wang, T., & Liao, M. (2015). Adaptive data gathering in mobile sensor networks using speedy mobile elements. Sensors., 15(9), 23218–23248.

    Article  Google Scholar 

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Acknowledgements

This research was supported by Education and Research promotion program of KOREATECH in 2021 and also by Basic Science Research Programs through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1A6A1A03025526). 

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Authors and Affiliations

  1. Advanced Technology Research Center, Korea University of Technology and Education, Cheonan, Republic of Korea

    Ihsan Ullah & Chan-Myung Kim

  2. Department of Computer Science and Engineering, Future Convergence Engineering, Korea University of Technology and Education, Cheonan, Republic of Korea

    Joo-Seong Heo & Youn-Hee Han

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  1. Ihsan Ullah
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  2. Chan-Myung Kim
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  3. Joo-Seong Heo
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  4. Youn-Hee Han
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Correspondence to Youn-Hee Han.

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Ullah, I., Kim, CM., Heo, JS. et al. An Energy-efficient Data Collection Scheme by Mobile Element based on Markov Decision Process for Wireless Sensor Networks. Wireless Pers Commun 123, 2283–2299 (2022). https://doi.org/10.1007/s11277-021-09241-1

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