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Wireless Sensor Node Localization based on LNSM and Hybrid TLBO : Unilateral technique for Outdoor Location

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper aims at localization of the anchor node (fixed node) by pursuit nodes (movable node) in outdoor location. Two methods are studied for node localization. The first method is based on LNSM (Log Normal Shadowing Model) technique to localize the anchor node and the second method is based on Hybrid TLBO (Teacher Learning Based Optimization Algorithm) - Unilateral technique. In the first approach the ZigBee protocol has been used to localize the node, which uses RSSI (Received Signal Strength Indicator) values in dBm. LNSM technique is implemented in the self-designed hardware node and localization is studied for Outdoor location. The statistical analysis using RMSE (root mean square error) for outdoor location is done and distance error found to be 35 mtrs. The same outdoor location has been used and statistical analysis is done for localization of nodes using Hybrid TLBO-Unilateral technique. The Hybrid-TLBO Unilateral technique significantly localizes anchor node with distance error of 0.7 mtrs. The RSSI values obtained are normally distributed and standard deviation in RSSI value is observed as 1.01 for outdoor location. The node becomes 100% discoverable after using hybrid TLBO- Unilateral technique.
Rocznik
Strony
389--397
Opis fizyczny
Bibliogr. 30 poz., il., tab., wykr.
Twórcy
autor
  • Department of Electronics, Instrumentation & Control Engineering, University of Petroleum & Energy Studies, Dehradun, INDIA
autor
  • Department of Electronics, Instrumentation & Control Engineering, University of Petroleum & Energy Studies, Dehradun, INDIA
autor
  • Department of CIT, University of Petroleum & Energy Studies, Dehradun, INDIA
Bibliografia
  • [1] Guerriero, M., et al., Some aspects of DOA estimation using a network of blind sensors. Signal Processing, 2008. 88(11): p. 2640-2650.
  • [2] Singh, S.P. and S. Sharma, Range free localization techniques in wireless sensor networks: A review. Procedia Computer Science, 2015. 57: p. 7-16.
  • [3] Doherty, L. and L. El Ghaoui. Convex position estimation in wireless sensor networks. in INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. 2001. IEEE.
  • [4] Bulusu, N., J. Heidemann, and D. Estrin, GPS-less low-cost outdoor localization for very small devices. IEEE personal communications, 2000 7(5): p. 28-34.
  • [5] He, T., et al. Range-free localization schemes for large scale sensor networks. in Proceedings of the 9th annual international conference on Mobile computing and networking. 2003. ACM.
  • [6] Almuzaini, K.K. and T.A. Gulliver. A new distributed range-free localization algorithm for wireless networks. in Vehicular Technology Conference Fall (VTC 2009-Fall), 2009 IEEE 70th. 2009. IEEE.
  • [7] Aiello, M., R. de Jong, and J. de Nes. Bluetooth broadcasting: How far can we go? An experimental study. in Pervasive Computing (JCPC), 2009 Joint Conferences on. 2009. IEEE.
  • [8] Specification, Z., ZigBee Alliance. ZigBee Document 053474r06, Version, 2006. 1.
  • [9] Chruszczyk, Ł. and A. Zajc, Comparison of indoor/outdoor, RSSI-based positioning using 433, 868 or 2400 MHz ISM bands. International Journal of Electronics and Telecommunications, 2016. 62(4): p. 395-399.
  • [10] Rao, R.V., Teaching-Learning-Based Optimization Algorithm, in Teaching Learning Based Optimization Algorithm2016, Springer. p. 9-39.
  • [11] Vivek Kaundal, Paawan Sharma, Devender Saini, Manish Prateek, Location Fingerprinting Supported Unilateral Algorithm based on Experimental Study of Localization in Disaster Prone Area International Journal of Computer Science and Information Security, 2016. 14: p. 162-175.
  • [12] Halder, S. and A. Ghosal, A survey on mobility-assisted localization techniques in wireless sensor networks. Journal of Network and Computer Applications, 2016. 60: p. 82-94.
  • [13] He, T., et al., Range-free localization and its impact on large scale sensor networks. ACM Transactions on Embedded Computing Systems (TECS), 2005. 4(4): p. 877-906.
  • [14] Anzai, D. and S. Hara. An RSSI-based MAP localization method with channel parameters estimation in wireless sensor networks. in Vehicular Technology Conference, 2009. VTC Spring 2009. IEEE 69th. 2009. IEEE.
  • [15] Cheng, G., Accurate TOA-based UWB localization system in coal mine based on WSN. Physics Procedia, 2012. 24: p. 534-540.
  • [16] Dakkak, M., et al., Indoor localization method based on RTT and AOA using coordinates clustering. Computer networks, 2011. 55(8): p. 1794-1803.
  • [17] Gezici, S., A survey on wireless position estimation. Wireless personal communications, 2008. 44(3): p. 263-282.
  • [18] Gharghan, S.K., et al., Accurate Wireless Sensor Localization Technique Based on Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling. IEEE Sensors Journal, 2016. 16(2): p. 529-541.
  • [19] Pires, R.P., et al., Evaluation of an rssi-based location algorithm for wireless sensor networks. IEEE Latin America Transactions, 2011. 9(1): p. 830-835.
  • [20] Blywis, B., et al. A localization framework for wireless mesh networks-anchor-free distributed localization in the des-testbed. in Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on. 2010. IEEE.
  • [21] Huang, C.-N. and C.-T. Chan, ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI. Procedia Computer Science, 2011. 5: p. 58-65.
  • [22] Liu, W., et al. Radio map position inference algorithm for indoor positioning systems. in 2012 18th IEEE International Conference on Networks (ICON). 2012. IEEE.
  • [23] Luo, X., W.J. OBrien, and C.L. Julien, Comparative evaluation of Received Signal-Strength Index (RSSI) based indoor localization techniques for construction jobsites. Advanced Engineering Informatics, 2011. 25(2): p. 355-363.
  • [24] Mao, G., B. Fidan, and B.D. Anderson, Wireless sensor network localization techniques. Computer networks, 2007. 51(10): p. 2529-2553.
  • [25] Meng, W., L. Xie, and W. Xiao, Decentralized TDOA sensor pairing in multihop wireless sensor networks. IEEE Signal Processing Letters, 2013. 20(2): p. 181-184.
  • [26] Mesmoudi, A., M. Feham, and N. Labraoui, Wireless sensor networks localization algorithms: a comprehensive survey. arXiv preprint arXiv:1312.4082, 2013.
  • [27] Nasipuri, A. and K. Li. A directionality based location discovery scheme for wireless sensor networks. in Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications. 2002. ACM.
  • [28] Savvides, A., C.-C. Han, and M.B. Strivastava. Dynamic fine-grained localization in ad-hoc networks of sensors. in Proceedings of the 7th annual international conference on Mobile computing and networking. 2001. ACM.
  • [29] Hanusz, Z., J. Tarasinska, and W. Zielinski, Shapiro-Wilk test with known mean. REVSTAT-Statistical Journal, 2016. 14(1): p. 89-100.
  • [30] Thadewald, T. and H. Bning, Jarque Bera test and its competitors for testing normalitya power comparison. Journal of Applied Statistics, 2007. 34(1): p. 87-105.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-60d23cb8-2be6-4a52-891b-8c832060c843
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