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Multi-objective path planning algorithm for mobile charger in wireless rechargeable sensor networks

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Abstract

Wireless Rechargeable Sensor Networks based on wireless energy transmission successfully solve the problem of node death caused by energy shortage in traditional wireless sensor networks. Existing studies on joint energy replenishment and data collection have planned charging path first and then perform data collection. This approach does not effectively extend network life and improve network performance in data collection. In this paper, we jointly consider the impact of energy replenishment and data collection on network performance and use the Least Squares Support Vector Machine based regression prediction with dynamically changing energy consumption rate and data generation rate of sensor nodes. To solve the above problem, we propose a multi-objective path planning model for joint energy replenishment and data collection with the optimization objectives of maximizing the remaining lifetime of sensor nodes, maximizing the data collection of MC, and minimizing the amount of data loss. The conventional exact algorithm is difficult to solve the path planning problem, so heuristic algorithms have gradually become the main algorithm to solve the problem. Based on discrete fireworks algorithm and co-evolutionary algorithm, we present a grid-based multi-objective cooperation fireworks algorithm. Simulation results show that the proposed algorithm performs better in convergence and diversity. In terms of the average remaining lifetime of sensor nodes, the proposed algorithm is 0.43%, 0.52%, 1.97%, and 0.81% higher than MPACO, MODFA, NSGA-II, and SPEA-II, respectively. Similarly, the target value of solutions in data collection increased by 1.87%, 1.22%, 4.49%, and 2.10%, respectively.

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References

  1. Han, G., Wang, H., Miao, X., et al. (2020). A dynamic multipath scheme for protecting source-location privacy using multiple sinks in WSNs intended for IIoT. IEEE Transactions on Industrial Informatics, 16(8), 5527–5538.

    Article  Google Scholar 

  2. Koosheshi, K., & Ebadi, S. (2019). Optimization energy consumption with multiple mobile sinks using fuzzy logic in wireless sensor networks. Wireless Networks, 25(3), 1215–1234.

    Article  Google Scholar 

  3. Deng, F., Yue, X., Fan, X., et al. (2019). Multisource energy harvesting system for a wireless sensor network node in the field environment. IEEE Internet of Things Journal, 6(1), 918–927.

    Article  Google Scholar 

  4. Verma, G., & Sharma, V. (2019). A novel thermoelectric energy harvester for wireless sensor network application. IEEE Transactions on Industrial Electronics, 66(5), 3530–3538.

    Google Scholar 

  5. Abhinav, T., Lalatendu, M., Prasanta, K., et al. (2021). A fuzzy logic-based on-demand charging algorithm for wireless rechargeable sensor networks with multiple chargers. IEEE Transactions on Mobile Computing, 20(9), 2715–2727.

    Article  Google Scholar 

  6. Ouyang, W., Mohammad, S., Liu, X., et al. (2021). Importance-different charging scheduling based on matroid theory for wireless rechargeable sensor networks. IEEE Transactions on Wireless Communications, 20(5), 3284–3294.

    Article  Google Scholar 

  7. Han, G., Yang, X., Liu, L., et al. (2018). A joint energy replenishment and data collection algorithm in wireless rechargeable sensor networks. IEEE Internet of Things Journal, 5(4), 2596–2604.

    Article  Google Scholar 

  8. Liu, K., Peng, J., He, L., et al. (2019). An active mobile charging and data collection scheme for clustered sensor networks. IEEE Transactions on Vehicular Technology, 68(5), 5100–5133.

    Article  Google Scholar 

  9. Arora, V., Sharma, V., & Sachdeva, M. (2020). A multiple pheromone ant colony optimization scheme for energy-efficient wireless sensor networks. Soft Computing, 24(1), 543–553.

    Article  Google Scholar 

  10. Rault, T. (2019). Avoiding radiation of on-demand multi-node energy charging with multiple mobile chargers. Computer Communications, 134, 42–51.

    Article  Google Scholar 

  11. Zhang, Q., Xu, W., Liang, W., et al. (2019). An improved algorithm for dispatching the minimum number of electric charging vehicles for wireless sensor networks. Wireless Networks, 25(3), 1371–1384.

    Article  Google Scholar 

  12. Wei, Z., Wang, L., & Lyu Z., et al. (2018). A Multi-objective algorithm for joint energy replenishment and data collection in wireless rechargeable sensor networks. In 13th International Conference on Wireless Algorithms, Systems, and Applications (WASA), (vol. 10874, pp. 497–508).

  13. Guo, S., Wang, C., & Yang, Y. (2014). Joint mobile data gathering and energy provisioning in wireless rechargeable sensor networks. IEEE Transactions on Mobile Computing, 13(12), 2836–2852.

    Article  Google Scholar 

  14. Xie, L., Shi, Y., Hou, Y., et al. (2015). A mobile platform for wireless charging and data collection in sensor networks. IEEE Journal on Selected Areas in Communications, 33(8), 1521–1533.

    Google Scholar 

  15. Wang, C., Li, J., Ye, F., et al. (2016). A mobile data gathering framework for wireless rechargeable sensor networks with vehicle movement costs and capacity constraints. IEEE Transactions on Computers, 65(8), 2411–2427.

    Article  MathSciNet  MATH  Google Scholar 

  16. Zhong, P., Li, Y., Liu, W., et al. (2017). Joint mobile data collection and wireless energy transfer in wireless rechargeable sensor networks. Sensors, 17(8), 1881–2003.

    Article  Google Scholar 

  17. Rostami, S., Rashidi, F., & Safari, H. (2019). Prediction of oil-water relative permeability in sandstone and carbonate reservoir rocks using the CSA-LSSVM algorithm. Journal of Petroleum Science and Engineering, 173, 170–186.

    Article  Google Scholar 

  18. He, L., Li, W., & Zhang, Y. (2019). A discrete multi-objective fireworks algorithm for flowshop scheduling with sequence-dependent setup times. Swarm and Evolutionary Computation, 51, 230–275.

    Article  Google Scholar 

  19. Wang, H., Ren, X., & Tu, X. (2017). Bee and Frog Co-Evolution Algorithm and its application. Applied Soft Computing, 56, 182–198.

    Article  Google Scholar 

  20. Wang, Y., Liu, H., Wei, F., et al. (2018). Cooperative coevolution with formula-based variable grouping for large-scale global optimization. Evolutionary computation, 26(4), 569–596.

    Article  Google Scholar 

  21. Kucukyilmaz, T., & Kiziloz, H. (2018). Cooperative parallel grouping genetic algorithm for the one-dimensional bin packing problem. Computers & Industrial Engineering, 125, 157–170.

    Article  Google Scholar 

  22. Xu, B., Zhang, Y., Gong, D., et al. (2018). Environment sensitivity-based cooperative co-evolutionary algorithms for dynamic multi-objective optimization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(6), 1877–1890.

    Article  Google Scholar 

  23. Cao, B., Zhao, J., Lv, Z., et al. (2017). A distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm for large-scale optimization. IEEE Transactions on Industrial Informatics, 13(4), 2030–2038.

    Article  Google Scholar 

  24. Liang, Z., Wang, X., Lin, Q., et al. (2018). A novel multi-objective co-evolutionary algorithm based on decomposition approach. Applied Soft Computing, 73, 50–66.

    Article  Google Scholar 

  25. Wu, X., & Che, A. (2019). A memetic differential evolution algorithm for energy-efficient parallel machine scheduling. Omega-International Journal of Management Science, 82, 155–165.

    Article  Google Scholar 

  26. Deng, W., Xu, J., & Song, Y. (2020). An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application. International Journal of Bio-Inspired Computation, 16(3), 158–170.

    Article  Google Scholar 

  27. Rajpoot, P., & Dwivedi, P. (2020). Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches. Wireless Networks, 26(1), 215–251.

    Article  Google Scholar 

  28. Cao, B., Zhao, J., Lv, Z., et al. (2021). Diversified personalized recommendation optimization based on mobile data. IEEE Transactions on Intelligent Transportation System, 22(4), 2133–2139.

    Article  Google Scholar 

  29. Mokshin, A., Mokshin, V., & Sharnin, L. (2019). Adaptive genetic algorithms used to analyze behavior of complex system. Communications in Nonlinear Science and Numerical Simulation, 71, 174–186.

    Article  MathSciNet  MATH  Google Scholar 

  30. Deb, K., Pratap, A., Agarwal, S., et al. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (62002097), the Anhui Science and Technology Major Project of China (201903a05020049), the Fundamental Research Funds for the Central Universities (PA2022GDGP0028), the Scientific and Technological Achievements Cultivation Project of Intelligent Manufacturing Institute of HFUT (IMIPY2021003).

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Authors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Xinchen Wang, Zengwei Lyu, Zhenchun Wei, Liangliang Wang, Yang Lu & Lei Shi

  2. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei, China

    Zengwei Lyu, Zhenchun Wei, Yang Lu & Lei Shi

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  1. Xinchen Wang
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  2. Zengwei Lyu
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  3. Zhenchun Wei
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  4. Liangliang Wang
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  5. Yang Lu
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  6. Lei Shi
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Correspondence to Zhenchun Wei.

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Wang, X., Lyu, Z., Wei, Z. et al. Multi-objective path planning algorithm for mobile charger in wireless rechargeable sensor networks. Wireless Netw 29, 267–283 (2023). https://doi.org/10.1007/s11276-022-03126-2

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