Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization

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Optimal performance and improved lifetime are the most desirable design benchmarks for WSNs and the mechanism for data gathering is a major constituent influencing these standards. Several researchers have provided significant evidence on the advantage of mobile sink (MS) in performing effective gathering of relevant data. However, determining the trajectory for MS is an NP-hard-problem. Especially in delay-inevitable applications, it is challenging to select the best-stops or rendezvous points (RPs) for MS and also to design an efficient route for MS to gather data. To provide a suitable solution to these challenges, we propose in this paper, a game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique. This is a distributed method of data gathering using MS, combining the optimal decision making skill of game theory in selecting the best RPs and computational swarm intelligence of enhanced ant colony optimization in choosing the best path for MS. GTAC-DG helps to reduce data transfer and management, energy consumption and delay in data delivery. The MS moves in a reliable and intelligent trajectory, extending the lifetime and conserving the energy of WSN. The simulation results prove the effectiveness of GTAC-DG in terms of metrics such as energy and network lifetime.

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  1. 1.

    Kim, B.-S., Kim, K.-I., Shah, B., Chow, F., & Kim, K. (2019). Wireless sensor networks for big data systems. Sensors, 19(7), 1565.

  2. 2.

    Osamy, W., Khedr, A. M., & Salim, A. (2019). Adaptive distributed service discovery protocol for Internet of Things based mobile wireless sensor networks. IEEE Sensor Journal, 19(22), 10869–10880.

  3. 3.

    Osamy, W., El-sawy, Ahmed A., & Khedr, Ahmed M. (2019). SATC: A simulated annealing based tree construction and scheduling algorithm for minimizing aggregation time in wireless sensor networks. Wireless Personal Communications, 108(2), 921–938.

  4. 4.

    Osamy, W., Khedr, A. M., Aziz, A., & El-Sawy, A. (2019). Cluster-tree routing scheme for data gathering in periodic monitoring applications. IEEE Access, 6, 77372–77387.

  5. 5.

    Osamy, W., Salim, A., & Khedr, A. M. (2018). An information entropy based-clustering algorithm in heterogeneous wireless sensor networks. Wireless Networks.

  6. 6.

    Salim, A., Osamy, W., & Khedr, A. M. (2014). IBLEACH: Effective LEACH protocol for wireless sensor networks. Wireless Networks, 20, 1515–1525.

  7. 7.

    Carlos-Mancilla, M., Lpez-Mellado, E., & Siller, M. (2016). Wireless sensor networks formation: Approaches and techniques. Journal of Sensors, 2016, 1–18.

  8. 8.

    Joshi, N., & Kansal, P. (2017). Data collection maximization of EH-WSN using mobile sink. In 2017 International conference on emerging trends in computing and communication technologies (ICETCCT).

  9. 9.

    Ghosh, N., & Banerjee, I. (2018). Application of mobile sink in wireless sensor networks. In 2018 10th International conference on communication systems & networks (COMSNETS), Bengaluru (pp. 507–509).

  10. 10.

    Thiruchelvi, A., & Karthikeyan, N. (2019). A novel pair based sink relocation and route adjustment in mobile sink WSN integrated IoT. In IET Communications.

  11. 11.

    Tunca, C., Isik, S., Donmez, M. Y., & Ersoy, C. (2014). Distributed mobile sink routing for wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 16(2), 877–897.

  12. 12.

    Bhushan, B., & Sahoo, G. (2019). E2SR2: An acknowledgement-based mobile sink routing protocol with rechargeable sensors for wireless sensor networks. Wireless Networks, 25(5), 2697–2721.

  13. 13.

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

  14. 14.

    Alsaafin, A., Khedr, A. M., & Aghbari, Z. A. (2018). Distributed trajectory design for data gathering using mobile sink in wireless sensor networks. AEU—International Journal of Electronics and Communications, 96, 1–12.

  15. 15.

    Ketshabetswe, L. K., Zungeru, A. M., Mangwala, M., Chuma, J. M., & Sigweni, B. (2019). Communication protocols for wireless sensor networks: A survey and comparison. Heliyon, 5(5), e01591.

  16. 16.

    Bhuiyan, B. A. (2018). An overview of game theory and some applications. Philosophy and Progress, 59(1–2), 111–128.

  17. 17.

    Habib, M. A., & Moh, S. (2019). Game theory-based routing for wireless sensor networks: A comparative survey. Applied Sciences, 9(14), 2896.

  18. 18.

    Kothawade, N., Biradar, A., Kodmelwar, K., Tambe, K., & Deshpande, V. (2016). Performance analysis of wireless sensor network by varying reporting rate. Indian Journal of Science and Technology, 9(26), 1–6.

  19. 19.

    Lin, H., Bai, D., Gao, D., & Liu, Y. (2016). Maximum data collection rate routing protocol based on topology control for rechargeable wireless sensor networks. Sensors, 16(8), 1201.

  20. 20.

    Dorigo, M., & Stützle, T. (2018). Ant colony optimization: Overview and recent advances. Handbook of Metaheuristics International Series in Operations Research & Management Science.

  21. 21.

    Kakde, K. R., & Kadam, M. (2017). Performance analysis of tree cluster based data gathering for WSNs. In 2017 International conference on intelligent computing and control (I2C2).

  22. 22.

    Jayram, B. G., & Ashoka, D. (2016). Validation of multiple mobile elements based data gathering protocols for dynamic and static scenarios in wireless sensor networks. Procedia Computer Science, 92, 260–266.

  23. 23.

    Neamatollahi, P., Abrishami, S., Naghibzadeh, M., Yaghmaee Moghaddam, M. H., & Younis, O. (2018). Hierarchical clustering-task scheduling policy in cluster-based wireless sensor networks. IEEE Transactions on Industrial Informatics, 14(5), 1876–1886.

  24. 24.

    Safia, A., Aghbari, Z., & Kamel, I. (2017). Efficient data collection by mobile sink to detect phenomena in Internet of Things. Information, 8(4), 123.

  25. 25.

    Mishra, D. P., & Kumar, R. (2019). Hybrid sink repositioning mechanism for Wireless Sensor Network. International Journal of Research in Advent Technology, 7(3), 1442–1447.

  26. 26.

    Tang, J., Guo, S., & Yang, Y. (2015). Dellat: Delivery latency minimization in wireless sensor networks with mobile sink. In IEEE international conference on communications (ICC), London (pp. 6481–6486).

  27. 27.

    Kuhlani, H., Wang, X., Hawbani, A., & Busaileh, O. (2019). Heuristic data dissemination for mobile sink networks. Wireless Networks.

  28. 28.

    Chen, F., Zhang, J., Tang, J., & Wang, T. (2017). Energy-efficient data-gathering rendezvous algorithms with mobile sinks for wireless sensor networks. International Journal of Sensor Networks, 23(4), 248.

  29. 29.

    Kumar, N., & Dash, D. (2017). Time-sensitive data collection with path-constrained mobile sink in WSN. In 2017 Third international conference on research in computational intelligence and communication networks (ICRCICN).

  30. 30.

    Gao, Y., Wang, J., Wu, W., Sangaiah, A. K., & Lim, S.-J. (2019). Travel route planning with optimal coverage in difficult wireless sensor network environment. Sensors, 19(8), 1838.

  31. 31.

    Salarian, H., Chin, K.-W., & Naghdy, F. (2014). An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Transactions on Vehicular Technology, 63(5), 2407–2419.

  32. 32.

    Ghotra, A. (2017). Optimizing inter cluster ant colony optimization data aggregation algorithm with rendezvous nodes and mobile sink. Wireless Sensor Network, 09(01), 16–24.

  33. 33.

    Vijayashree, R., & Dhas, C. S. G. (2019). Energy efficient data collection with multiple mobile sink using artificial bee colony algorithm in large-scale WSN. Automatika, 60(5), 555–563.

  34. 34.

    Wang, J., Gao, Y., Liu, W., Sangaiah, A. K., & Kim, H.-J. (2019). Energy efficient routing algorithm with mobile sink support for wireless sensor networks. Sensors (Basel), 19(7), 1494.

  35. 35.

    Yang, G., Xu, H., He, X., Wang, G., Xiong, N., & Wu, C. (2016). Tracking mobile sinks via analysis of movement angle changes in WSNs. Sensors, 16(4), 449.

  36. 36.

    AlSkaif, T., Zapata, M. G., & Bellalta, B. (2015). Game theory for energy efficiency in wireless sensor networks: Latest trends. Journal of Network and Computer Applications, 54(1), 33–61.

  37. 37.

    Lin, D., & Wang, Q. (2019). An energy-efficient clustering algorithm combined game theory and dual-cluster-head mechanism for WSNs. IEEE Access, 7, 49894–49905.

  38. 38.

    Hendrarini, N., Asvial, M., & Sari, R.-F. (2019). Optimization of heterogeneous sensor networks with clustering mechanism using game theory algorithm. In Proceedings of the 2nd international conference on software engineering and information management (ICSIM).

  39. 39.

    Yang, L., Lu, Y., & Zhong, Y. (2016). A hybrid, game theory based, and distributed clustering protocol for wireless sensor networks. Wireless Networks, 22(3), 1007–1021.

  40. 40.

    Liu, Q., & Liu, M. (2017). Energy-efficient clustering algorithm based on game theory for wireless sensor networks. International Journal of Distributed Sensor Networks, 13(11), 155014771774370.

  41. 41.

    Zayene, M., Habachi, O., Meghdadi, V., Ezzedine, T., & Cances, J. P. (2019). A coalitional game-theoretic framework for cooperative data exchange using instantly decodable network coding. IEEE Access, 7, 26752–26765.

  42. 42.

    Koley, I., & Samanta, T. (2018). Mobile sink based data collection for energy efficient coordination in wireless sensor network using cooperative game model. Telecommunication Systems, 71(3), 377–396.

  43. 43.

    Yang, L., Lu, Y., Xiong, L., Tao, Y., & Zhong, Y. (2017). A game theoretic approach for balancing energy consumption in clustered wireless sensor networks. Sensors, 17(11), 2654.

  44. 44.

    Haghighi, M., Maraslis, K., Tryfonas, T., & Oikonomou, G. (2015). Game theoretic approach towards energy-efficient task distribution in wireless sensor networks, 2015 Ieee Sensors.

  45. 45.

    Wang, J., Cao, J., Sherratt, R. S., & Park, J. H. (2017). An improved ant colony optimization-based approach with mobile sink for wireless sensor networks. The Journal of Supercomputing, 74(12), 6633–6645.

  46. 46.

    Rajasekaran, A., & Nagarajan, V. (2018) Cluster-based wireless sensor networks using ant colony optimization. In International conference on intelligent data communication technologies and Internet of Things (ICICI) 2018 Lecture notes on data engineering and communications technologies (pp. 42–55).

  47. 47.

    Zhang, H., Li, Z., Shu, W., & Chou, J. (2019). Ant colony optimization algorithm based on mobile sink data collection in industrial wireless sensor networks. EURASIP Journal on Wireless Communications and Networking.

  48. 48.

    Kumar, P., Amgoth, T., & Annavarapu, C. S. (2018). R ACO-based mobile sink path determination for wireless sensor networks under non-uniform data constraints. Applied Soft Computing, 69, 528–540.

  49. 49.

    Mehto, A., Tapaswi, S., & Pattanaik, K. K. (2019). A review on rendezvous based data acquisition methods in wireless sensor networks with mobile sink. Wireless Networks.

  50. 50.

    Basillis, G. (2014). Prolonging network lifetime in wireless sensor networks with path-constrained mobile sink. International Journal of Advanced Computer Science and Applications.

  51. 51.

    Liu, X. (2015). An optimal-distance-based transmission strategy for lifetime maximization of wireless sensor networks. Sensors Journal IEEE, 15(6), 3484–3491.

  52. 52.

    Reed, M., Yiannakou, A., & Evering, R. (2014). An ant colony algorithm for the multi-compartment vehicle routing problem. Applied Soft Computing, 15, 169–176.

  53. 53.

    Kefi, S., Rokbani, N., & Alimi, A. M. (2016). Solving the traveling salesman problem using ant colony metaheuristic, a review. In International conference on hybrid intelligent systems (pp. 421–430). Cham: Springer.

  54. 54.

    Neto, R. T., & Godinho Filho, M. (2013). Literature review regarding ant colony optimization applied to scheduling problems: Guidelines for implementation and directions for future research. Engineering Applications of Artificial Intelligence, 26(1), 150–61.

  55. 55.

    Cecilia, J. M., Garca, J. M., Nisbet, A., Amos, M., & Ujaldn, M. (2013). Enhancing data parallelism for ant colony optimization on GPUs. Journal of Parallel and Distributed Computing, 73(1), 52–61.

  56. 56.

    Shwe, H., & Adachi, F. (2011). Power efficient adaptive network coding in wireless sensor networks. In IEEE ICC (pp. 1–5).

  57. 57.

    Cheong, P. Y., Aggarwal, D., Hanne, T., & Dornberger, R. (2017). Variation of ant colony optimization parameters for solving the travelling salesman problem. In IEEE 4th International conference on soft computing & machine intelligence (ISCMI), Port Louis (pp. 60–65).

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Correspondence to Ahmed M. Khedr.

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Raj, P.V.P., Khedr, A.M. & Aghbari, Z.A. Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization. Wireless Netw (2020) doi:10.1007/s11276-020-02254-x

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  • Ant colony optimization (ACO)
  • Data gathering (DG)
  • Game theory (GT)
  • Rendezvous points (RPs)
  • Mobile sink (MS) trajectory
  • Wireless sensor network (WSN)