Aquila Optimized Localization of Mobile nodes in Heterogeneous WSN with Reduced Complexity using MCL Square

Aquila Optimized Localization of Mobile nodes in Heterogeneous WSN with Reduced Complexity using MCL Square

© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Swapna M P, G. Satyavathy
DOI :  10.14445/22315381/IJETT-V70I4P221

How to Cite?

Swapna M P, G. Satyavathy, "Aquila Optimized Localization of Mobile nodes in Heterogeneous WSN with Reduced Complexity using MCL Square," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 245-257, 2022. Crossref,

Localization of mobile nodes in Heterogeneous Wireless Sensor Network (HSWN) requires more research and experiments. The majority of the localization protocols discuss locating the static nodes in the wireless network. This paper proposes the localization of mobile nodes in an HWSN, considering energy efficiency. The protocol Aquila Optimized Monte Carlo Localization(AOMCL) is a novel attempt to combine the mobile node localizing algorithm MCL and the new swarm intelligence algorithm, Aquila Optimizer. The protocol AOMCL reduces the sampling and filtering process of traditional MCL. AOMCL localizes the unknown nodes by generating an MCL square around the location-aware anchor nodes. The method efficiently reduces the time and complexity of localizing the unknown nodes. The experimental analysis of AOMCL in the Matlab simulator illustrates that the proposed protocol, AOMCL, has high localization accuracy, better localization coverage, and reduced complexity compared with the existing protocols, DEMCL, RMCL, and QMCL.

Filtering, HWSN, Localization, Location Prediction, MCL Square, Sampling.

[1] Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E. Wireless Sensor Networks: A Survey. Comput Netw, 38(4) (2002)393-422.
[2] Singhm, Khilar PM.Mobile Beacon-Based Range-Free Localization Method for Wireless Sensor Networks. Wirel Netw,23(4) (2017) 1285-1300.
[3] Yan, X., Yang, Z., Song, A., Yang, W., Liu, Y., & Zhu, R., A Novel Multihop Range-Free Localization Based on Kernel Learning Approach for the Internet of Things. Wireless Personal Communications, 87(1) (2016) 269–292.
[4] Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H., & Liu, Y., Smart Manufacturing Is Based on Cyber-Physical Systems and Beyond. Journal of Intelligent Manufacturing, 30(8) (2019) 2805–2817.
[5] Moh`d Alia, O., & Al-Ajouri, A., Maximizing Wireless Sensor Network Coverage With Minimum Cost using A Harmony Search Algorithm. IEEE Sensors Journal, 17(3) (2017) 882–896.
[6] Adulyasas, A., Sun, Z., & Wang, N.,Connected Coverage Optimization for Sensor Scheduling in Wireless Sensor Networks. IEEE Sensors Journal, 15(7) (2015) 3877–3892
[7] Abu-Mahfouz, A. M., & Hancke, G. P., Alwadha Localization Algorithm: Yet More Energy Efficient. IEEE Access, 5(5) (2017) 6661– 6667.
[8] Kirci, P., & Chaouchi, H., Recursive and Ad Hoc Routing-Based Localization in Wireless Sensor Networks. Computer Standards & Interfaces, 44 (2016) 258–263.
[9] Pandey S, Varma S., A Range-Based Localization System in Multihop Wireless Sensor Networks: A Distributed Cooperative Approach. Wirel Pers Commun 86(2) (2016) 615–634.
[10] Alippi C, Vanini G., A RSSI-Based and Calibrated Centralized Localization Technique for Wireless Sensor Networks. Fourthannual IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW`06). IEEE, (2006) 5.
[11] Wang P, Xue F, Li H, Cui Z, Chen J., A Multi-Objective Dvhop Localization Algorithm Based on NSGA-II in the Internet of Things.Mathematics, 7(2) (2019) 184.
[12] Yan J, Qiao R, Tang L, Zheng C, Fan B., A Fuzzy Decision-Based WSN Localization Algorithm for Wise Healthcare. China Communications, 16(4) (2019) 208–218.
[13] Zou D, Chen S, Han S, Meng W, an D, Li J, Zhao W., Design A Practical WSN-Based Fingerprint Localization System. Mobile Networks and Applications, (2019) 1–13.
[14] Ruz ML, Garrido J, Jim`enez J, Virrankoski R, V`azquez F., Simulation Tool for Analyzing Cooperative Localization Algorithms for Wireless Sensor Networks. Sensors 19(13) (2019) 2866.
[15] Xiong H, Sichitiu ML., A Lightweight Localization Solution for Small, Low Resources WSNS. J Sens Actuator Netw 8(2) (2019) 26.
[16] Bulusu N, Heidemann J, Estrin D., GPS-Less Low-Cost Outdoor Localization for Very Small Devices. IEEE Pers Commun, 7 (2000) 28–34.
[17] Niculescu D, Nath B (2003) DV Based Positioning in Ad Hoc Networks. Telecommun Syst, 22 (2003) 267–280.
[18] Nagpal R, Shrobe H, Bachrach J (2003) Organizing A Global Coordinate System From Local Information on an Ad Hoc Sensor Network. Lect Not Comp Sci, 2634 (2003) 333–348.
[19] J. Xing, Y. Ren, L. Yang, J. Sha, and J. Sun, Distributed Grid-Based Localization Algorithm for Mobile Wireless Sensor Networks, Springer, Berlin Heidelberg, (2012).
[20] H. Akcan, V. Kriakov, H. Brönnimann, and A. Delis, Managing Cohort Movement of Mobile Sensors Via GPS-Free and CompassFree Node Localization, Journal of Parallel & Distributed Computing, 70(7) (2010) 743–757.
[21] S. Fujii, A. Uchiyama, T. Umeda, H. Yamaguchi, and T. Higashino, Trajectory Estimation Algorithm for Mobile Nodes using Encounter Information and Geographical Information, Pervasive and Mobile Computing, 8(2) (2012) 249–270.
[22] Singh SP, Sharma S., Range Free Localization Techniques in Wireless Sensor Networks: A Review. Procedia Computer Science, 57 (2015) 7–16
[23] A. El Assaf, S. Zaidi, S. Affes, N. Kandil, Low-Cost Localization for Multihop Heterogeneous Wireless Sensor Networks, IEEE Trans. Wirel. Commun. 15 (1) (2015) 472–484, Doi: 10.1109/TWC.2015.2475255.
[24] W. Wu, X. Wen, H. Xu, L. Yuan, Q. Meng, Efficient Range-Free Localization using Elliptical Distance Correction in Heterogeneous Wireless Sensor Networks, Int. J. Distrib. Sens. Netw. 14 (1) (2018). Doi: 10.1177/1550147718756274
[25] Y. Xing, W. Huangfu, X. Dai, X. Hu, Node Localization Based on Multiple Radio Transmission Power Levels for Wireless Sensor Networks, in in International Conference on 5G for Future Wireless Networks, Springer, Cham, (2017) 19–27.Doi: 10.1007/978-3319-72823-0_3. April
[26] L. Hu, D. Evans, Localization for Mobile Sensor Networks, in Tenth International Conference on Mobile Computing and Networking (Mobicom’04), Philadelphia, Pennsylvania, USA, (2004) 45–57.
[27] A. Baggio and K. Langendoen, Monte Carlo Localization for Mobile Wireless Sensor Networks, Ad Hoc Networks, 6(5) (2008) 718– 733.
[28] L. Jianpo, S. Ming, Y. Xie, and S. Jisheng, A MCL Mobile Node Localization Algorithm Based on Fuzzy Theory, Computer Applications and Software, 30(12) (2013) 147–150.
[29] Z. Juqin, C. Yangyan, S. Xiaoping, and X. Guo, Research on Node Localization Algorithm Based on Adaptive Monte Carlo Algorithm for Dynamic Sensor Networks, Applied Laser, 36(4) (2016) 446–450.View At: Google Scholar.
[30] T. I. A. N. Haoshan, L. I. Cuiran, X. I. E. Jianli, and L. I. A. N. G. Yingxin, Node Localization Algorithm for WSN Based on Time Sequence Monte Carlo, Chinese Journal of Sensors and Actuators, 29(11) (2016) 1724–1730.
[31] M. Qin and R. Zhu, A Monte Carlo Localization Method Based on Differential Evolution Optimization Applied Into Economic Forecasting in Mobile Wireless Sensor Networks, EURASIP Journal on Wireless Communications and Networking, Article ID 32, 2018(1) 2018.
[32] Abualigah, L.; Diabat, A. Advances in Sine Cosine Algorithm: A Comprehensive Survey. Artif. Intell. Rev. 54 (2021) 2567–2608.
[33] Kennedy, J.; Eberhart, R. Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Neural Networks (ICNN ’93), Perth, WA, Australia, IEEE: Piscataway, NJ, USA, 27(1) (1995).
[34] Dorigo, M.; Birattari, M.; Stutzle, T. Ant Colony Optimization. IEEE Comput. Intell. 1 (2006) 28–39.
[35] Yang, X.S. A New Metaheuristic Bat-Inspired Algorithm. in Nature Inspired Cooperative Strategies for Optimization (NICSO); Springer: Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey Wolf Optimizer. Adv. Eng. Softw. 69 (2014) 46–61.
[36] Gandomi, A.H.; Yang, X.S.; Alavi, A.H. Cuckoo Search Algorithm: A Metaheuristic Approach to Solve Structural Optimization Problems. Eng. Comput. 29 (2013) 17–35.
[37] Mirjalili, S.; Lewis, A. the Whale Optimization Algorithm. Adv. Eng. Softw. 95 (2016) 51–67.
[38] Heidari, A.A.; Mirjalili, S.; Faris, H.; Aljarah, I.; Mafarja, M.; Chen, H.L. Harris Hawks Optimization: Algorithm and Applications. Future Gener. Comput. Syst. 97 (2019) 849–872.
[39] Abualigah, L.; Yousri, D.; Elaziz, M.A.; Ewees, A.A.; Al-Qaness, M.A.A.; Gandomi, A.H. Aquila Optimizer: A Novel Meta-Heuristic Optimization Algorithm. Comput. Ind. Eng. 157 (2021) 107250.
[40] J. Ramkumar and R. Vadivel, Multi-Adaptive Routing Protocol for Internet of Things Based Ad-Hoc Networks, Wirel. Pers. Commun., (2021) 1–23. Doi: 10.1007/S11277-021-08495-Z.
[41] J. Ramkumar and R. Vadivel, Intelligent Fish Swarm Inspired Protocol (IFSIP) for Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks, Int. J. Comput. Digit. Syst, 10(1) (2020) 1063–1074. Doi: Http://Dx.Doi.Org/10.12785/Ijcds/100196.
[42] J. Ramkumar and R. Vadivel, Improved Wolf Prey Inspired Protocol for Routing in Cognitive Radio Ad Hoc Networks, Int. J. Comput. Networks Appl., 7(5) (2020) 126–136. Doi: 10.22247/Ijcna/2020/202977.
[43] J. Ramkumar, R. Vadivel, and B. Narasimhan, Constrained Cuckoo Search Optimization Based Protocol for Routing in Cloud Network, Int. J. Comput. Networks Appl., Doi: 10.22247/Ijcna/2021/210727.
[44] J. Ramkumar and R. Vadivel, Whale Optimization Routing Protocol for Minimizing Energy Consumption in Cognitive Radio Wireless Sensor Network, Int. J. Comput. Networks Appl., 8(4). Doi: 10.22247/Ijcna/2021/209711.
[45] F. Dellaert, D. Fox, W. Burgard, S. Thrun, Monte Carlo Localization for Mobile Robots, in IEEE International Conference on Robotics and Automation (ICRA99), Detroit, Michigan, USA, (1999).
[46] S. Thrun, D. Fox, W. Burgard, F. Dellaert, Robust Monte Carlo Localization for Mobile Robots, Artificial Intelligence 128 (1–2) (2001) 99–141.
[47] L. Hu, D. Evans, Localization for Mobile Sensor Networks, in Tenth International Conference on Mobile Computing and Networking (Mobicom`04), Philadelphia, Pennsylvania, USA, (2004) 45–57.
[48] J. Y. Lu and C. Wang, A New Monte Carlo Mobile Node Localization Algorithm Based on Newton Interpolation, EURASIP Journal on Wireless Communications and Networking, 2018(1) (2018) Article ID 161.