Comparison of Some Static Path Planning Models Localization Performance in Obstacle-Presence Environment

Abstract

Static anchors are generally used for the localization of Unknown Nodes (UNNs) in Wireless Sensor Networks (WSNs). However, it would be a more efficient approach to design a Mobile Anchor (MA) trajectory to cover all UNNs instead and to have the MA travel to broadcast its position at specific points along that trajectory. With this logic, many studies have been published in the literature in recent years. SCAN, HILBERT, SPIRAL, LMAT, Z-curve, H-curve, and M-curves static path planning models are examined in this study. The localization performances of these path planning models are compared with different performance evaluation criteria using the Weighted Centroid Localization (WCL) technique in the obstacle-presence scenario. The simulation results show the advantages of the H-curve model over existing schemes. The SPIRAL model performs worse than other models in the obstacle-presence scenario.

Keywords

• localization
• mobile anchor node assisted localization
• obstacle-handling
• path planning
• static path planning.

Bazı Statik Yol Planlama Modellerinin Yerelleştirme Performanslarının Engelli Ortamda Karşılaştırılması

Öz

Kablosuz Algılayıcı Ağlar (Wireless Sensor Networks, WSNs)'de bilinmeyen düğümlerin yerelleştirilmesi işlemi için genellikle statik çapalar kullanılmaktadır. Ancak, bunun yerine bütün bilinmeyen düğümleri kapsayacak şekilde bir hareketli çapa yörüngesi tasarlamak ve hareketli çapayı bu yörünge boyunca konumunu belirli noktalarda yayınlamak üzere dolaştırmak, daha verimli bir yaklaşım olacaktır. Bu mantıkla, son yıllarda literatürde birçok çalışma yayınlanmıştır. Bu çalışmada, SCAN, HILBERT, SPIRAL, LMAT, Z-eğrisi, H- eğrisi ve M- eğrileri statik yol planlama modelleri incelenmiştir. Bu yol planlama modellerinin yerelleştirme performansları, düzgün şekilli engeller içeren ağlarda, Ağırlıklı Merkezi Yerelleştirme (Weighted Centroid Localization, WCL) tekniği kullanılarak farklı performans değerlendirme kriterleriyle karşılaştırılmıştır. Benzetim sonuçları, H-eğrisi modelinin mevcut şemalara göre avantajlarını göstermektedir. SPIRAL modeli ise düzgün şekilli engeller içeren senaryolarda diğer modellere göre daha kötü performans göstermektedir.

Anahtar Kelimeler

• yerelleştirme
• hareketli çapa düğüm destekli yerelleştirme
• engel yönetimi
• yol planlama
• statik yol planlama.

References

1. Puccinelli, D., & Haenggi, M. (2005). Wireless sensor networks: applications and challenges of ubiquitous sensing. IEEE Circuits and systems magazine, 5(3), 19-31.
2. Mao, G., Fidan, B., & Anderson, B. D. (2007). Wireless sensor network localization techniques. Computer networks, 51(10), 2529-2553.
3. Cheng, L., Wu, C., Zhang, Y., Wu, H., Li, M., & Maple, C. (2012). A survey of localization in wireless sensor network. International Journal of Distributed Sensor Networks, 8(12), 962523.
4. Singh, S. P., & Sharma, S. C. (2015). Range free localization techniques in wireless sensor networks: A review. Procedia Computer Science, 57, 7-16.
5. Yildiz, D., Karagol, S., & Ozgonenel, O. (2017, April). A hyperbolic location algorithm for various distributions of a Wireless Sensor Network. In 2017 5th International Istanbul Smart Grid and Cities Congress and Fair (ICSG) (pp. 75-79). IEEE.
6. Mondal, K., Karmakar, A., & Mandal, P. S. (2016). Path planning algorithms for mobile anchors towards range-free localization. Journal of Parallel and Distributed Computing, 97, 35-46.
7. He, T., Huang, C., Blum, B. M., Stankovic, J. A., & Abdelzaher, T. (2003, September). 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).
8. Han, G., Jiang, J., Zhang, C., Duong, T. Q., Guizani, M., & Karagiannidis, G. K. (2016). A survey on mobile anchor node assisted localization in wireless sensor networks. IEEE Communications Surveys & Tutorials, 18(3), 2220-2243.

9. Alomari, A., Comeau, F., Phillips, W., & Aslam, N. (2018). New path planning model for mobile anchor-assisted localization in wireless sensor networks. Wireless Networks, 24(7), 2589-2607.
10. Koutsonikolas, D., Das, S. M., & Hu, Y. C. (2007). Path planning of mobile landmarks for localization in wireless sensor networks. Computer Communications, 30(13), 2577-2592.
11. Han, G., Xu, H., Duong, T. Q., Jiang, J., & Hara, T. (2013). Localization algorithms of wireless sensor networks: a survey. Telecommunication Systems, 52(4), 2419-2436.
12. Han, G., Zhang, C., Lloret, J., Shu, L., & Rodrigues, J. J. (2014). A mobile anchor assisted localization algorithm based on regular hexagon in wireless sensor networks. The Scientific World Journal, 2014.
13. Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. In Mobile computing (pp. 153-181). Springer, Boston, MA.
14. Han, G., Chao, J., Zhang, C., Shu, L., & Li, Q. (2014). The impacts of mobility models on DV-hop based localization in mobile wireless sensor networks. Journal of Network and Computer Applications, 42, 70-79.
15. Hu, Z., Gu, D., Song, Z., & Li, H. (2008, July). Localization in wireless sensor networks using a mobile anchor node. In 2008 IEEE/ASME international conference on advanced intelligent mechatronics (pp. 602-607). IEEE.
16. Han, G., Xu, H., Jiang, J., Shu, L., Hara, T., & Nishio, S. (2013). Path planning using a mobile anchor node based on trilateration in wireless sensor networks. Wireless Communications and Mobile Computing, 13(14), 1324-1336.
17. Rezazadeh, J., Moradi, M., Ismail, A. S., & Dutkiewicz, E. (2014). Superior path planning mechanism for mobile beacon-assisted localization in wireless sensor networks. IEEE Sensors Journal, 14(9), 3052-3064.
18. Kannadasan, K., Edla, D. R., Kongara, M. C., & Kuppili, V. (2019). M-curves path planning model for mobile anchor node and localization of sensor nodes using dolphin swarm algorithm. Wireless Networks, 1-15.
19. Blumenthal, J., Grossmann, R., Golatowski, F., & Timmermann, D. (2007, October). Weighted centroid localization in zigbee-based sensor networks. In 2007 IEEE international symposium on intelligent signal processing (pp. 1-6). IEEE.
20. Ou, C. H., & He, W. L. (2012). Path planning algorithm for mobile anchor-based localization in wireless sensor networks. IEEE Sensors Journal, 13(2), 466-475.
21. Zamalloa, M. Z., & Krishnamachari, B. (2007). An analysis of unreliability and asymmetry in low-power wireless links. ACM Transactions on Sensor Networks (TOSN), 3(2), 7-es.
22. Dezfouli, B., Radi, M., Abd Razak, S., Hwee-Pink, T., & Bakar, K. A. (2015). Modeling low-power wireless communications. Journal of Network and Computer Applications, 51, 102-126.
23. Chipcon, Dallas, TX, USA. CC1000 Low Power Radio Transceiver [Online]. Available: https://www.ti.com/product/CC1000 Accessed 11 June 2020.
24. Rappaport, T. S. (1996). Wireless communications: principles and practice (Vol. 2). New Jersey: prentice hall PTR.
25. Srinivasan, K., Dutta, P., Tavakoli, A., & Levis, P. (2010). An empirical study of low-power wireless. ACM Transactions on Sensor Networks (TOSN), 6(2), 1-49.