News

 Statistics

Number of Journals34
Number of Issues1,306
Number of Articles9,427
Article View9,189,002
PDF Download5,621,607
Golihaghighi, N., Biguesh, M. (2020). Node Selection in a Cooperating Position Finding Distributed System Concerning the Computational Complexity Reduction. Electronic and Cyber Defense, 7(2), 15-23.
N. Golihaghighi; M. Biguesh. "Node Selection in a Cooperating Position Finding Distributed System Concerning the Computational Complexity Reduction". Electronic and Cyber Defense, 7, 2, 2020, 15-23.
Golihaghighi, N., Biguesh, M. (2020). 'Node Selection in a Cooperating Position Finding Distributed System Concerning the Computational Complexity Reduction', Electronic and Cyber Defense, 7(2), pp. 15-23.
Golihaghighi, N., Biguesh, M. Node Selection in a Cooperating Position Finding Distributed System Concerning the Computational Complexity Reduction. Electronic and Cyber Defense, 2020; 7(2): 15-23.

Node Selection in a Cooperating Position Finding Distributed System Concerning the Computational Complexity Reduction

رادار
Article 2, Volume 7, Issue 2 - Serial Number 22, July 2020, Pages 15-23    PDF (1.2 M)
Document Type: Original Article
Authors
N. Golihaghighi1; M. Biguesh* 2
1PhD student, Faculty of Electrical and Computer Engineering, Shiraz University, Iran
2Professor, Faculty of Electrical and Computer Engineering, Shiraz University, Iran
Receive Date: 11 December 2019,  Revise Date: 06 March 2020,  Accept Date: 26 March 2020 
Abstract
Cooperative positioning utilizes information received from all the nodes in a network to estimate the position of a target node. This requires high amount of data exchange and data processing in high density networks. This paper proposes a low computational complexity algorithm to select a number of nodes among all possible nodes to cooperate in position finding. Position of nodes are estimated using both the distances between the target node and its cooperated nodes and also the information shared by these nodes. The nodes selection algorithm is proposed according to the Cramer-Rao Lower Bound, which considers the precision of distance measurements, the geometry of nodes and the uncertainty in the information shared by nodes. This fast computing algorithm reduces required computations without significantly decreasing the position estimation performance.
Keywords
Position Finding; Cooperative; Node Selection; Distributed Networks
References

   [1]      S. V. Shojadini, R. Kabiri, A. Kermani, and M. Fereydooni,“A New Method for Minimization of the Effect of Noise in Measuring Delays Between Receiver Sites in Geolocation Based on TDOA of Signals,” Journal of “Radar”, vol. 3, no. 3, pp. 55-63, 2015. (In Persian)

   [2]      A. Gholipour, B Zakeri, and Kh. Mafinejad, “Near-Field Source Localizationin Non-homogeneus Environments,” Journal of “Radar”, vol. 4, no. 1, pp. 49-56, 2016. (In Persian)

   [3]      S. V. d. Velde, G. T. F. de Abreu, and H. Steendam, “Improved Censoring and NLOS Avoidance for Wireless Localization in Dense Networks,” IEEE Journal on Selected Areas in Communications, vol. 33, pp. 2302-2312, 2015.

   [4]      Z. Zhao, R. Zhang, X. Cheng, L. Yang, and B. Jiao, “Network Formation Games for the Link Selection of Cooperative Localization in Wireless Networks,” in Proc. 2014 IEEE International Conference on Communications (ICC), pp. 4577 – 4582, 2014.

   [5]      M. Youssef, A. Agrawal, and A. U. Shankar, “WLAN Location Determination Via Clustering and Probability Distributions,” in Proc. The First IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 143 – 150, March 2003.

   [6]      G. Chandrasekaran, M. A. Ergin, J. Yang, S. Liu, Y. Chen, M. Gruteser, and R. P. Martin , “Empirical Evaluation of  the Limits on Localization Using Signal Strength,” in Proc. 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, pp. 1-9, 2009.

   [7]      G.M. Hoang, B. Denis, J. H¨arri, and D. T.M. Slock, “Select Thy Neighbors: Low Complexity Link Selection for High Precision Cooperative Vehicular Localization,” in Proc. IEEE Vehicular Networking Conference (VNC), pp. 36-43, 2015.

   [8]      M. Z. Win, W. Dai, Y. Shen, G. Chrisikos, and H. V. Poor, “Network Operation Strategies for Efficient Localization and Navigation,” Proceedings of the IEEE, vol. 106, pp. 1224-1254, July 2018.

   [9]      T. Wang, A. Conti, and M. Z. Win, “Network Navigation with Scheduling: Distributed Algorithms,” IEEE/ACM Transactions on Networking, vol. 27, pp. 1319-1329, August 2019.

[10]      M. Angjelichinoski, D. Denkovski, V. Atanasovski, and L. Gavrilovska, “Cramer Rao Lower Bounds of RSS-Based Localization with Anchor Position Uncertainty,”' IEEE Transactions on Information Theory, vol. 61, no. 5, May 2015.

[11]      A. F. García-Fernández , L. Svensson, and S. Särkkä, “Cooperative Localisation Using Posterior Linearization Belief Propagation,” IEEE Transactions on Vehicular Technology, vol. 67, pp. 832-836, 2018.

[12]      H. Naseri, and V. Koivunen, “A Bayesian Algorithm for Distributed Network Localization Using Distance and Direction Data,” IEEE Transactions on Signal and Information Processing over Networks, vol. 5, pp. 290-304, June 2019.

[13]      F. Meyer, O. Hlinka, and F. Hlawatsch, “Sigma Point Belief Propagation,” IEEE Signal Processing Letters, vol. 21, no. 2, pp. 145-149, February 2014.

[14]      S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, p. 47, 1993.

[15]      S. Firdaus, and Md. A. Uddin, “A Survey on Clustering Algorithms and Complexity Analysis,” IJCSI International Journal of Computer Science Issues, vol. 12, pp. 62-85, March 2015.

[16]       “K-means Clustering,” https://en.wikipedia.org/wiki.

[17]      J. B. MacQueen, “Some Methods for Classification and Analysis of Multivariate Observations,” in Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297, 1967.

[18]      T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An Efficient K-Means Clustering Algorithm: Analysis And Implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, July 2002.

[19]      D. Arthur and S. Vassilvitskii, “K-Means++: the Advantages of Careful Seeding,” in Proc. Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035, 2007.

Statistics
Article View: 1,011
PDF Download: 373