Journal of Engineering and Applied Sciences
Year:
2018
Volume:
13
Issue:
2
Page No.
406 - 414
An Efficient Localization based on Relevance Vector Machine with
Glow-Worm Swarm Optimization for Wireless Sensor Networks
Authors :
M. Arun
and
P. Manimegalai
References
Abe, S., 2015. Fuzzy support vector machines for multilabel classification. Pattern Recognit., 48: 2110-2117.
Direct Link | Boukerche, A., H.A. Oliveira, E.F. Nakamura and A.A. Loureiro, 2007. Localization systems for wireless sensor networks. IEEE. Wirel. Commun., 14: 6-12.
CrossRef | Direct Link | Branch, J.W., C. Giannella, B. Szymanski, R. Wolff and H. Kargupta, 2013. In-network outlier detection in wireless sensor networks. Knowledge Inform. Syst., 34: 23-54.
CrossRef | Cortes, C. and V. Vapnik, 1995. Support-vector networks. Mach. Learn., 20: 273-297.
CrossRef | Direct Link | Das, B.B. and S.K. Ram, 2016. Localization using beacon in wireless sensor networks to detect faulty nodes and accuracy improvement through DV-Hop algorithm. Proceedings of the International Conference on Inventive Computation Technologies (ICICT) Vol. 1, August 26-27, 2016, IEEE, Coimbatore, India, ISBN:978-1-5090-1286-2, pp: 1-5.
Hai, T.H. and E.N. Huh, 2008. Detecting selective forwarding attacks in wireless sensor networks using two-hops neighbor knowledge. Proceedings of the 7th IEEE International Symposium on Network Computing and Applications, July 10-12, 2008, Cambridge, MA., pp: 325-331.
Johann, P. and R. Hamboker, 1994. Parametric Statistical Theory. Walter de Gruyter, Berlin, Germany, ISBN:978-3-11-014030-6, Pages: 377.
Krishnanand, K.N. and D. Ghose, 2005. Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. Proceedings of the IEEE Swarm Intelligence Symposium, June 8-10, 2005, Pasadena, California, USA., pp: 84-91.
Krishnanand, K.N. and D. Ghose, 2009. Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell., 3: 87-124.
CrossRef | Direct Link | Lee, J., B. Choi and E. Kim, 2013. Novel range-free localization based on multidimensional support vector regression trained in the primal space. IEEE. Trans. Neural Netw. Learn. Syst., 24: 1099-1113.
CrossRef | Direct Link | Liu, A., M. Dong, K. Ota and J. Long, 2015. PHACK: An efficient scheme for selective forwarding attack detection in WSNs. Sens., 15: 30942-30963.
CrossRef | PubMed | Direct Link | Morelande, M.R., B. Moran and M. Brazil, 2008. Bayesian node localisation in wireless sensor networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008), March 31-April 4, 2008, IEEE, Las Vegas, Nevada, USA., ISBN:978-1-4244-1483-3, pp: 2545-2548.
Nguyen, C., O. Georgiou and Y. Doi, 2015. Maximum likelihood based multihop localization in wireless sensor networks. Proceedings of the IEEE International Conference on Communications (ICC), June 8-12, 2015, IEEE, London, UK., ISBN:978-1-4673-6431-7, pp: 6663-6668.
Pan, J.J., S.J. Pan, J. Yin, L.M. Ni and Q. Yang, 2012. Tracking mobile users in wireless networks via semi-supervised colocalization. IEEE. Trans. Pattern Anal. Mach. Intell., 34: 587-600.
CrossRef | PubMed | Direct Link | Salman, N., M. Ghogho and A.H. Kemp, 2014. Optimized low complexity sensor node positioning in wireless sensor networks. IEEE. Sensors J., 14: 39-46.
CrossRef | Direct Link | Shao, H.J., X.P. Zhang and Z. Wang, 2014. Efficient closed-form algorithms for AOA based self-localization of sensor nodes using auxiliary variables. IEEE. Trans. Signal Process., 62: 2580-2594.
CrossRef | Direct Link | Simonetto, A. and G. Leus, 2014. Distributed maximum likelihood sensor network localization. IEEE. Trans. Signal Process., 62: 1424-1437.
CrossRef | Direct Link | Tang, T., H. Liu, H. Song and B. Peng, 2016. Support Vector Machine Based Range-Free Localization Algorithm in Wireless Sensor Network. In: Machine Learning and Intelligent Communications, Xin-Lin, H. (Ed.). Springer, Berlin, Germany, ISBN:978-3-319-52729-1, pp: 150-158.
Tipping, M.E., 2001. Sparse Bayesian learning and the relevance vector machine. J. Machine Learn. Res., 1: 211-244.
Direct Link | Tran, D.A. and T. Nguyen, 2008. Localization in wireless sensor networks based on support vector machines. IEEE Trans. Parallel Distrib. Syst., 19: 981-994.
CrossRef | Direct Link | Wei, L., Y. Yang, R.M. Nishikawa, M.N. Wernick and A. Edwards, 2005. Relevance vector machine for automatic detection of clustered microcalcifications. IEEE. Trans. Med. Imaging, 24: 1278-1285.
CrossRef | Direct Link | Yoo, J. and H.J. Kim, 2015. Target localization in wireless sensor networks using online semi-supervised support vector regression. Sens., 15: 12539-12559.
CrossRef | Direct Link | Youssef, A.M. and M. Youssef, 2007. A taxonomy of localization schemes for wireless sensor networks. Proceedings of the International Conference on Wireless Networks (ICWN 07), June 25-28, 2007, CSREA Press, Las Vegas, Nevada, pp: 444-450.