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Improved DV-Hop based on Squirrel search algorithm for localization in wireless sensor networks

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Abstract

The ability to obtain the accurate location of nodes in wireless sensor networks is crucial for practical applications. The sensed data is meaningless if it is not accompanied by its location. Range-free localization techniques are favored to overcome the hardware limitations of sensor nodes and to avoid the costly range-based techniques. DV-Hop is a range-free localization algorithm that is well-known for its simplicity. However, it suffers from low accuracy and poor stability. In this paper, an enhanced variant of the DV-Hop algorithm is used to estimate the distance between the unknown nodes and anchor nodes, then the position estimation phase is formulated as a minimization problem solved by means of the recently developed squirrel search algorithm (SSA). The SSA is utilized to find the locations of the unknown sensor nodes. Our proposed algorithm is thus called SSIDV-Hop algorithm. The performance of our proposed algorithm is compared to that of existing localization algorithms including the DV-Hop, PSODV-Hop, GADV-Hop, and DEIDV-Hop algorithms. Extensive simulations showed that our proposed algorithm is superior to other existing algorithms as it achieved higher localization accuracy, better stability and faster convergence rate.

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References

  1. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer networks, 38(4), 393–422.

    Article  Google Scholar 

  2. Winkler, M., Tuchs, K. D., Hughes, K., & Barclay, G. (2008). Theoretical and practical aspects of military wireless sensor networks. Journal of Telecommunications and Information Technology, 37–45.

  3. Naz, P., Hengy, S., & Hamery, P. (2012). Soldier Detection using Unattended Acoustic and Seismic Sensors. In Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR III (Vol. 8389, p. 83890T). International Society for Optics and Photonics.

  4. Deng, Z., Wu, Q., Lv, X., Zhu, B., Xu, S., & Wang, X. (2019). Application Analysis of Wireless Sensor Networks in Nuclear Power Plant. International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection for Nuclear Power Plant (pp. 135–148). Singapore: Springer.

    Google Scholar 

  5. Li, J., Kang, X., Long, Z., Meng, J., & Huang, X. (2016). The Application of the Wireless Sensor Network in Intelligent Monitoring of Nuclear Power Plants. International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection for Nuclear Power Plant (pp. 179–188). Singapore: Springer.

    Google Scholar 

  6. Tang, V. W., Zheng, Y., & Cao, J. (2006). An Intelligent Car Park Management System based on Wireless Sensor Networks. In 2006 First International Symposium on Pervasive Computing and Applications (pp. 65-70). IEEE.

  7. Ghorpade, S. N., Zennaro, M., & Chaudhari, B. S. (2020). GWO Model for Optimal Localization of IoT-Enabled Sensor Nodes in Smart Parking Systems. IEEE Transactions on Intelligent Transportation Systems.

  8. Majumder, S., Aghayi, E., Noferesti, M., Memarzadeh-Tehran, H., Mondal, T., Pang, Z., & Deen, M. J. (2017). Smart Homes for Elderly Healthcare-Recent Advances and Research Challenges. Sensors, 17(11), 2496.

    Article  Google Scholar 

  9. Ghayvat, H., Mukhopadhyay, S., Gui, X., & Suryadevara, N. (2015). WSN-and IOT-based Smart Homes and Their Extension to Smart Buildings. Sensors, 15(5), 10350–10379.

    Article  Google Scholar 

  10. Liu, K., Abu-Ghazaleh, N., & Kang, K. D. (2007). Location verification and trust management for resilient geographic routing. Journal of parallel and distributed computing, 67(2), 215–228.

    Article  Google Scholar 

  11. Li, Z., Li, R., Wei, Y., & Pei, T. (2010). Survey of Localization Techniques in Wireless Sensor Networks. Information Technology Journal, 9(8), 1754–1757.

    Article  Google Scholar 

  12. Nazir, U., Shahid, N., Arshad, M. A., & Raza, S. H. (2012). Classification of Localization Algorithms for Wireless Sensor Network: A Survey. In 2012 International conference on open source systems and technologies (pp. 1-5). IEEE.

  13. Arias, J., Zuloaga, A., Lázaro, J., Andreu, J., & Astarloa, A. (2004). Malguki: an RSSI based ad hoc location algorithm. Microprocessors and Microsystems, 28(8), 403–409.

    Article  Google Scholar 

  14. Savvides, A., Han, C. C., & Strivastava, M. B. (2001). Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors. In Proceedings of the 7th annual international conference on Mobile computing and networking (pp. 166-179).

  15. Niculescu, D., & Nath, B. (2003). Ad Hoc Positioning System (APS) Using AoA. In IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No. 03CH37428) (Vol. 3, pp. 1734-1743). IEEE.

  16. Niculescu, D., & Nath, B. (2003). DV Based Positioning in Ad Hoc Networks. Telecommunication Systems, 22(1–4), 267–280.

    Article  Google Scholar 

  17. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less Low-Cost Outdoor Localization For Very Small Devices. IEEE personal communications, 7(5), 28–34.

    Article  Google Scholar 

  18. Nagpal, R., Shrobe, H., & Bachrach, J. (2003). Organizing a Global Coordinate System from Local Information on an Ad Hoc Sensor Network. In Information processing in sensor networks (pp. 333-348). Springer, Berlin, Heidelberg.

  19. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003). Range-Free Localization Schemes for Large Scale Sensor Networks. In Proceedings of the 9th annual international conference on Mobile computing and networking (pp. 81-95).

  20. Shakshuki, E., Elkhail, A. A., Nemer, I., Adam, M., & Sheltami, T. (2019). Comparative Study on Range Free Localization Algorithms. Procedia Computer Science, 151, 501–510.

    Article  Google Scholar 

  21. Yang, J., Cai, Y., Tang, D., & Liu, Z. (2019). A Novel Centralized Range-Free Static Node Localization Algorithm with Memetic Algorithm and Lévy Flight. Sensors, 19(14), 3242.

    Article  Google Scholar 

  22. Ghasemi-Marzbali, A. (2020). A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm. Soft Computing, 1–33.

  23. Adnan, M., Razzaque, M. A., Ahmed, I., & Isnin, I. F. (2014). Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey. Sensors, 14(1), 299–345.

    Article  Google Scholar 

  24. Rao, P. S., Banka, H., & Jana, P. K. (2015). A Gravitational Search Algorithm for Energy Efficient Multi-sink Placement in Wireless Sensor Networks. In International conference on swarm, evolutionary, and memetic computing (pp. 222-234). Springer, Cham.

  25. Rao, P. S., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless networks, 23(7), 2005–2020.

    Article  Google Scholar 

  26. Rao, P. S., & Banka, H. (2017). Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach. Wireless Networks, 23(2), 433–452.

    Article  Google Scholar 

  27. Kulkarni, R. V., Förster, A., & Venayagamoorthy, G. K. (2010). Computational Intelligence in Wireless Sensor Networks: A Survey. IEEE communications surveys & tutorials, 13(1), 68–96.

    Article  Google Scholar 

  28. Jain, M., Singh, V., & Rani, A. (2019). A novel nature-inspired algorithm for optimization: Squirrel search algorithm. Swarm and evolutionary computation, 44, 148–175.

    Article  Google Scholar 

  29. Basu, M. (2019). Squirrel Search Algorithm for Multi-region Combined Heat and Power Economic Dispatch Incorporating Renewable Energy Sources. Energy, 182, 296–305.

    Article  Google Scholar 

  30. Sakthivel, V. P., Suman, M., & Sathya, P. D. (2020). Squirrel search algorithm for economic dispatch with valve-point effects and multiple fuels. Energy Sources, Part B: Economics, Planning, and Policy, 15(6), 351–382.

    Article  Google Scholar 

  31. Chen, X., & Zhang, B. (2012). Improved DV-Hop Node Localization Algorithm in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 8(8), 213980.

    Article  Google Scholar 

  32. Peng, B., & Li, L. (2015). An improved localization algorithm based on genetic algorithm in wireless sensor networks. Cognitive Neurodynamics, 9(2), 249–256.

    Article  Google Scholar 

  33. Han, D., Yu, Y., Li, K. C., & de Mello, R. F. (2020). Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks. Sensors, 20(2), 343.

    Article  Google Scholar 

  34. Chen, H., Sezaki, K., Deng, P., & So, H. C. (2008). An Improved DV-Hop Localization Algorithm for Wireless Sensor Networks. In 2008 3rd IEEE Conference on Industrial Electronics and Applications (pp. 1557-1561). IEEE.

  35. Chen, H., Sezaki, K., Deng, P., & So, H. C. (2008). An Improved DV-Hop Localization Algorithm with Reduced Node Location Error for Wireless Sensor Networks. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 91(8), 2232–2236.

    Article  Google Scholar 

  36. Salama, M., & Kandil, M. (2016). An Improved DV-Hop Localization Algorithm based on Modified Hop-size. In 2016 World Symposium on Computer Applications & Research (WSCAR) (pp. 83-86). IEEE.

  37. Kumar, S., & Lobiyal, D. K. (2013). An Advanced DV-Hop Localization Algorithm for Wireless Sensor Networks. Wireless personal communications, 71(2), 1365–1385.

    Article  Google Scholar 

  38. Kumar, S., & Lobiyal, D. K. (2017). Novel DV-Hop localization algorithm for wireless sensor networks. Telecommunication Systems, 64(3), 509–524.

    Article  Google Scholar 

  39. Zhang, B., Ji, M., & Shan, L. (2012). A Weighted Centroid Localization Algorithm Based on DV-hop for Wireless Sensor Network. In 2012 8th international conference on wireless communications, networking and mobile computing (pp. 1-5). IEEE.

  40. Song, G., & Tam, D. (2015). Two Novel DV-Hop Localization Algorithms for Randomly Deployed Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 11(7), 187670.

    Article  Google Scholar 

  41. Tomic, S., & Mezei, I. (2016). Improvements of DV-Hop localization algorithm for wireless sensor networks. Telecommunication Systems, 61(1), 93–106.

    Article  Google Scholar 

  42. Shahzad, F., Sheltami, T. R., & Shakshuki, E. M. (2016). DV-maxHop: A Fast and Accurate Range-Free Localization Algorithm for Anisotropic Wireless Networks. IEEE Transactions on Mobile Computing, 16(9), 2494–2505.

    Article  Google Scholar 

  43. Cui, L., Xu, C., Li, G., Ming, Z., Feng, Y., & Lu, N. (2018). A high accurate localization algorithm with DV-Hop and differential evolution for wireless sensor network. Applied Soft Computing, 68, 39–52.

    Article  Google Scholar 

  44. Song, L., Zhao, L., & Ye, J. (2019). DV-hop Node Location Algorithm Based on GSO in Wireless Sensor Networks. Journal of Sensors, 2019.

  45. Mehrabi, M., Taheri, H., & Taghdiri, P. (2017). An improved DV-Hop localization algorithm based on evolutionary algorithms. Telecommunication Systems, 64(4), 639–647.

    Article  Google Scholar 

  46. Sharma, G., & Kumar, A. (2018). Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks. Telecommunication Systems, 67(2), 163–178.

    Article  Google Scholar 

  47. Wang, Y., & Du, T. (2019). An Improved Squirrel Search Algorithm for Global Function Optimization. Algorithms, 12(4), 80.

    Article  MathSciNet  Google Scholar 

  48. Mantegna, R. N. (1994). Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. Physical Review E, 49(5), 4677.

    Article  Google Scholar 

  49. Li, G., Zhao, S., Wu, J., Li, C., & Liu, Y. (2019). DV-Hop Localization Algorithm Based on Minimum Mean Square Error in Internet of Things. Procedia computer science, 147, 458–462.

    Article  Google Scholar 

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Correspondence to Mohamed G. Abd El Ghafour.

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Abd El Ghafour, M.G., Kamel, S.H. & Abouelseoud, Y. Improved DV-Hop based on Squirrel search algorithm for localization in wireless sensor networks. Wireless Netw 27, 2743–2759 (2021). https://doi.org/10.1007/s11276-021-02618-x

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