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Periodic data collection from mobile sensors with unpredictable motion along road networks

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Abstract

Tracking mobile objects in road networks is one of the important applications of wireless sensor networks (WSNs). It includes road safety, smart transportation, vehicle safety, vehicle tracking, traffic monitoring, etc. In this work, we assume that the sensors are installed on vehicles which are moving on the road. These mobile sensors need to be tracked/visited periodically within a pre-defined time interval. In our scenario, the mobility or trajectory of the mobile sensors are unpredictable. In many applications nowadays, the mobile sensors have unpredictable mobility. In this paper, our aim is to collect data by visiting the mobile sensors periodically using fewer number of mobile sinks. The mobile sinks subsequently deliver the collected data to a stationary base station. Time-bound periodic data collection from the mobile sensors along with their unpredictable motion is even more challenging than the data collection from static sensors. Here, we propose a deterministic data gathering algorithm using the solution of Chinese Postman Problem. We measure the performance of the proposed solution. Due to non-availability of any existing solution, we have compared our algorithm with a heuristic algorithm and a variation of an existing solution. The experiment results show that our proposed data gathering algorithm performs well.

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References

  1. Ahr, D., & Reinelt, G. (2006). A tabu search algorithm for the min-max k-chinese postman problem. Computers and Operations Research, 33, 3403–3422.

    Article  MathSciNet  Google Scholar 

  2. Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communication and Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking: Research Trends and Applications, 2(5), 483–502.

  3. Cheng, C. F., & Yu, C. F. (2016). Data gathering in wireless sensor networks: A combine-tsp-reduce approach. IEEE Transactions on Vehicular Technology, 65(4), 2309–2324.

    Article  Google Scholar 

  4. Das, A., Mazumder, A., Sen, A., & Mitton, N.(2016). On mobile sensor data collection using data mules. In IEEE international conference on computing, networking and communications (ICNC) (pp. 1–7).

  5. Das, A., Shirazipourazad, S., Hay, D., & Sen, A. (2019). Tracking of multiple targets using optimal number of uavs. IEEE Transactions On Aerospace And Electronic Systems, 55(4), 1769–1784.

    Article  Google Scholar 

  6. Dash, D. (2018). Approximation algorithm for data gathering from mobile sensors. Elsevier Pervasive and Mobile Computing, 46, 34–48.

    Article  Google Scholar 

  7. Dash, D. (2020). Approximation algorithms for road coverage using wireless sensor networks for moving objects monitoring. IEEE Transaction on Intelligent Transportation Systems, 21(11).

  8. Dash, D., Kumar, N., Ray, P. P., & Kumar, N. (2021). Reducing data gathering delay for energy efficient wireless data collection by jointly optimizing path and speed of mobile sink. IEEE Systems Journal, 15(3), 3173–3184.

    Article  Google Scholar 

  9. Eiselt, H. A., Gendreau, M., & Laporte, G. (1995). Arc routing problems, part i: The chinese postman problem. Operations Research, 43(2), 231–242.

    Article  MathSciNet  Google Scholar 

  10. Faheem, M., Butt, R. A., Raza, B., Ashraf, M. W., Ngadi, M. A., & Gungor, V. C. (2019). Energy efficient and reliable data gathering using internet of software-defined mobile sinks for wsns-based smart grid applications. Elsevier, Computer Standards & Interfaces, 66, 103–341.

    Google Scholar 

  11. Ghosh, N. R. S., & Banerjee, I. (2016). Efficient polling point determination and physical model based throughput maximisation in wireless sensor network. In IEEE 24th international conference on software, telecommunications and computer networks (SoftCOM) (pp. 1–5).

  12. Han, G., Guan, H., Wu, J., Chan, S., Shu, L., & Zhang, W. (2019). An uneven cluster-based mobile charging algorithm for wireless rechargeable sensor networks. IEEE Systems Journal, 13(4).

  13. Huang, H., & Savkin, A. V. (2016). Optimal path planning for a vehicle collecting data in a wireless sensor network. In IEEE 35th Chinese control conference (CCC) (pp. 8460–8463).

  14. Huang, H., & Savkin, A. V. (2017). An energy efficient approach for data collection in wireless sensor networks using public transportation vehicles. Elsevier, AEU-International Journal of Electronics and Communications, 75, 108–118.

    Google Scholar 

  15. Endeavor Robotics. (2014). iRobot 510 PackBot Multi-Mission Robot. Retrieved Feb, 2022, from http://www.army-technology.com/projects/irobot-510-packbot-multi-mission-robot.

  16. Jawaligi, S. S., & Biradar, G. S. (2017). Single mobile sink based energy efficiency and fast data gathering protocol for wireless sensor networks. Wireless Sensor Network, 9, 117–144.

    Article  Google Scholar 

  17. Kaswan, A., Nitesh, K., & Jana, P. K. (2017). Energy efficient path selection for mobile sink and data gathering in wireless sensor networks. Elsevier, AEU-International Journal of Electronics and Communications, 73, 110–118.

    Google Scholar 

  18. Konstantopoulos, C., Vathis, N., Pantziou, G., & Gavalas, D. (2018). Employing mobile elements for delay-constrained data gathering in wsns. Elsevier Computer Networks, 135, 108–131.

    Article  Google Scholar 

  19. Kumar, N., & Dash, D. (2018). Mobile data sink-based time-constrained data collection from mobile sensors: A heuristic approach. IET Wireless Sensor Systems, 8(3), 129–135.

    Article  Google Scholar 

  20. Lin, H., & Üstert, H. (2014). Exact and heuristic algorithms for data-gathering cluster-based wireless sensor network design problem. IEEE/ACM Transaction on Networking, 22(3), 903–916.

    Article  Google Scholar 

  21. Lyu, Z., Wei, Z., Wang, X., Fan, Y., Xia, C., & Shi, L. (2020). A periodic multinode charging and data collection scheme with optimal traveling path in wrsns. IEEE Systems Journal. https://doi.org/10.1109/JSYST.2020.2977984

    Article  Google Scholar 

  22. Ma, C., Liang, W., & Zheng, M. (2017). Delay constrained relay node placement in two-tiered wireless sensor networks: A set-covering-based algorithm. Elsevier, Journal of Network and Computer Applications, 93, 76–90.

    Article  Google Scholar 

  23. Ma, M., Yang, Y., & Zhao, M. (2013). Tour planning for mobile data-gathering mechanisms in wireless sensor networks. IEEE Transactions on Vehicular Technology, 62(4), 1472–1483.

    Article  Google Scholar 

  24. Sidorenko, G., Thunberg, J., Sjobergy, K., & Vinel, A. (2020). Vehicle-to-vehicle communication for safe and fuel-efficient platooning. In IEEE intelligent vehicles symposium (pp. 795–802).

  25. Tao, L., Zhang, X. M., & Liang, W. (2019). Efficient algorithms for mobile sink aided data collection from dedicated and virtual aggregation nodes in energy harvesting wireless sensor networks. IEEE Transactions on Green Communications and Networking, 3(4), 1058–1071.

    Article  Google Scholar 

  26. Tseng, Y., Wu, F., & Lai, W. (2013). Opportunistic data collection for disconnected wireless sensor networks by mobile mules. Ad Hoc Networks, Elsevier, 11(3), 1150–1164.

    Article  Google Scholar 

  27. Wang, J., Cao, Y., Li, B., Kim, H., & Lee, S. (2017). Particle swarm optimization based clustering algorithm with mobile sink for wsns. Future Generation Computer Systems, 76, 452–457.

    Article  Google Scholar 

  28. Yin, X., Zhu, J., & Wu, Y. L. Z. (2015). Mobile data gathering with time-constraints in wireless sensor networks. In Springer international conference on WASA (pp. 696–705).

  29. Yao, Y., Cao, Q., & Vasilakos, A. V. (2015). Edal: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Transactions on Networking(TON), 23(3), 810–823.

  30. Yarinezhad, R., & Azizi, S. (2021). An energy-efficient routing protocol for the internet of things networks based on geographical location and link quality. Computer Networks, 193, 108–116.

    Article  Google Scholar 

  31. Yarinezhad, R., & Hashemi, S. N. (2019). Distributed faulty node detection and recovery scheme for wireless sensor networks using cellular learning automata. Wireless Networks, 25(5), 2901–2917.

    Article  Google Scholar 

  32. Yarinezhad, R., & Hashemi, S. N. (2020). Exact and approximate algorithms for clustering problem in wireless sensor networks. IET Communications, 14(4), 580–587.

    Article  Google Scholar 

  33. Yarinezhad, R., & Sarabi, A. (2018). Reducing delay and energy consumption in wireless sensor networks by making virtual grid infrastructure and using mobile sink. Elsevier. AEU-International Journal of Electronics and Communications, 84, 144–152.

    Google Scholar 

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Acknowledgements

This work is supported by the Science and Engineering Research Board, a statutory body of the Department of Science and Technology (DST), Govt. of India [Grant number: ECR/2016/002040 and ECR/2016/001035]. We also like to thank the anonymous reviewers for their valuable comments.

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  1. Department of Computer Science and Engineering, National Institute of Technology Patna, Ashok Rajpath, Patna, India

    Sabah Tazeen, Dinesh Dash & Suddhasil De

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  1. Sabah Tazeen
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  2. Dinesh Dash
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Correspondence to Dinesh Dash.

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Tazeen, S., Dash, D. & De, S. Periodic data collection from mobile sensors with unpredictable motion along road networks. Wireless Netw 28, 1505–1520 (2022). https://doi.org/10.1007/s11276-022-02915-z

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