Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks

[EN] In this paper, we proposed a new wireless localization technique based on the ideology of social network analysis (SNA), to study the different properties of networks as a graph. Centrality is a main concept in SNA, so we propose using closeness centrality (CC) as a measurement to denote the importance of the node inside the network due to its geo-location to others. The node with highest degree of CC is chosen as a cluster heads, then each cluster head can form its trilateration process to collect data from its cluster. The selection of closest cluster based on CC values, and the unknown node's location can be estimated through the trilateration process. To form a perfect trilateration, the cluster head chooses three anchor nodes. The proposed algorithm provides high accuracy even in different network topologies like concave shape, O shape, and C shape as compared to existing received signal strength indicator (RSSI) techniques. Matlab simulation results based on practical radio propagation data sets showed a localization error of 0.32 m with standard deviation of 0.26 m.This work was fully supported by the Vice Chancellor Doctoral Scholarship at Auckland University of Technology, New Zealand.Ahmad, T.; Li, XJ.; Seet, B.; Cano, J. (2020). Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks. Electronics. 9(5):1-19. https://doi.org/10.3390/electronics9050738S11995Zhou, B., Yao, X., Yang, L., Yang, S., Wu, S., Kim, Y., & Ai, L. (2019). Accurate Rigid Body Localization Using DoA Measurements from a Single Base Station. Electronics, 8(6), 622. doi:10.3390/electronics8060622Ahmad, T., Li, X., & Seet, B.-C. (2017). Parametric Loop Division for 3D Localization in Wireless Sensor Networks. Sensors, 17(7), 1697. doi:10.3390/s17071697Kaur, A., Kumar, P., & Gupta, G. P. (2019). A weighted centroid localization algorithm for randomly deployed wireless sensor networks. Journal of King Saud University - Computer and Information Sciences, 31(1), 82-91. doi:10.1016/j.jksuci.2017.01.007Khelifi, F., Bradai, A., Benslimane, A., Rawat, P., & Atri, M. (2018). A Survey of Localization Systems in Internet of Things. Mobile Networks and Applications, 24(3), 761-785. doi:10.1007/s11036-018-1090-3Sanchez-Iborra, R., G. Liaño, I., Simoes, C., Couñago, E., & Skarmeta, A. (2018). Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats. Electronics, 8(1), 15. doi:10.3390/electronics8010015Sayed, A. H., Tarighat, A., & Khajehnouri, N. (2005). Network-based wireless location: challenges faced in developing techniques for accurate wireless location information. IEEE Signal Processing Magazine, 22(4), 24-40. doi:10.1109/msp.2005.1458275Maşazade, E., Ruixin Niu, Varshney, P. K., & Keskinoz, M. (2010). Energy Aware Iterative Source Localization for Wireless Sensor Networks. IEEE Transactions on Signal Processing, 58(9), 4824-4835. doi:10.1109/tsp.2010.2051433Yang, X., Kong, Q., & Xie, X. (2009). One-Dimensional Localization Algorithm Based on Signal Strength Ratio. International Journal of Distributed Sensor Networks, 5(1), 79-79. doi:10.1080/15501320802571822Xie, S., Wang, T., Hao, X., Yang, M., Zhu, Y., & Li, Y. (2019). Localization and Frequency Identification of Large-Range Wide-Band Electromagnetic Interference Sources in Electromagnetic Imaging System. Electronics, 8(5), 499. doi:10.3390/electronics8050499Zhu, X., Wu, X., & Chen, G. (2013). Relative localization for wireless sensor networks with linear topology. Computer Communications, 36(15-16), 1581-1591. doi:10.1016/j.comcom.2013.07.007Meng, W., Xiao, W., & Xie, L. (2011). An Efficient EM Algorithm for Energy-Based Multisource Localization in Wireless Sensor Networks. IEEE Transactions on Instrumentation and Measurement, 60(3), 1017-1027. doi:10.1109/tim.2010.2047035Lim, H., & Hou, J. C. (2009). Distributed localization for anisotropic sensor networks. ACM Transactions on Sensor Networks, 5(2), 1-26. doi:10.1145/1498915.1498917Xiaohong Sheng, & Yu-Hen Hu. (2005). Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks. IEEE Transactions on Signal Processing, 53(1), 44-53. doi:10.1109/tsp.2004.838930Yun Wang, Xiaodong Wang, Demin Wang, & Agrawal, D. P. (2009). Range-Free Localization Using Expected Hop Progress in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 20(10), 1540-1552. doi:10.1109/tpds.2008.239Huang, H., & Zheng, Y. R. (2018). Node localization with AoA assistance in multi-hop underwater sensor networks. Ad Hoc Networks, 78, 32-41. doi:10.1016/j.adhoc.2018.05.005Zàruba, G. V., Huber, M., Kamangar, F. A., & Chlamtac, I. (2006). Indoor location tracking using RSSI readings from a single Wi-Fi access point. Wireless Networks, 13(2), 221-235. doi:10.1007/s11276-006-5064-1Singh, M., & Khilar, P. M. (2015). An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network. Wireless Networks, 22(8), 2537-2550. doi:10.1007/s11276-015-1116-8Yiqiang Chen, Qiang Yang, Jie Yin, & Xiaoyong Chai. (2006). Power-efficient access-point selection for indoor location estimation. IEEE Transactions on Knowledge and Data Engineering, 18(7), 877-888. doi:10.1109/tkde.2006.112Alzoubi, K., Li, X.-Y., Wang, Y., Wan, P.-J., & Frieder, O. (2003). Geometric spanners for wireless ad hoc networks. IEEE Transactions on Parallel and Distributed Systems, 14(4), 408-421. doi:10.1109/tpds.2003.1195412Safa, H. (2014). A novel localization algorithm for large scale wireless sensor networks. Computer Communications, 45, 32-46. doi:10.1016/j.comcom.2014.03.020Kaemarungsi, K., & Krishnamurthy, P. (2012). Analysis of WLAN’s received signal strength indication for indoor location fingerprinting. Pervasive and Mobile Computing, 8(2), 292-316. doi:10.1016/j.pmcj.2011.09.003Patwari, N., Hero, A. O., Perkins, M., Correal, N. S., & O’Dea, R. J. (2003). Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing, 51(8), 2137-2148. doi:10.1109/tsp.2003.814469Niculescu, D. (2003). Telecommunication Systems, 22(1/4), 267-280. doi:10.1023/a:1023403323460Mahyar, H., Hasheminezhad, R., Ghalebi K., E., Nazemian, A., Grosu, R., Movaghar, A., & Rabiee, H. R. (2018). Compressive sensing of high betweenness centrality nodes in networks. Physica A: Statistical Mechanics and its Applications, 497, 166-184. doi:10.1016/j.physa.2017.12.145Plets, D., Bastiaens, S., Martens, L., & Joseph, W. (2019). An Analysis of the Impact of LED Tilt on Visible Light Positioning Accuracy. Electronics, 8(4), 389. doi:10.3390/electronics8040389RSSI Datasethttps://github.com/pspachos/RSSI-DatasetAhmad, T., Li, X. J., & Seet, B.-C. (2019). Noise Reduction Scheme for Parametric Loop Division 3D Wireless Localization Algorithm Based on Extended Kalman Filtering. Journal of Sensor and Actuator Networks, 8(2), 24. doi:10.3390/jsan8020024Benson, S. J., Ye, Y., & Zhang, X. (2000). Solving Large-Scale Sparse Semidefinite Programs for Combinatorial Optimization. SIAM Journal on Optimization, 10(2), 443-461. doi:10.1137/s105262349732800

Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks

Localization in wireless sensor networks not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localization in static WSN, not enough work is done for mobile WSNs, owing to the complexity due to node mobility. Most of the existing techniques for localization in mobile WSNs uses Monte-Carlo localization, which is not only time-consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this paper, we propose a technique called Dead Reckoning Localization for mobile WSNs. In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localized for the first time using three anchor nodes. For their subsequent localizations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node Using Bezouts theorem. A dead reckoning approach is used to select one of the two estimated locations. We have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201

Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust

A Multi-hop Topology Control Based on Inter-node Range Measurement for Wireless Sensor Networks Node Localization

In centralized range-based localization techniques, sufficiency of inter-node range information received by the base station strongly affects node position estimation results. Successful data aggregation is influenced by link stability of each connection of routes, especially in a multi-hop topology model. In general, measuring the inter-node range is only performed for position determination purposes. This research introduces the use of inter-node range measurement information for link selection in a multi-hop route composition in order to increase the rate of data aggregation. Due to irregularity problems of wireless media, two areas of node communication have been considered. The regular communication area is the area in which other nodes are able to perform symmetrical communication to the node without failure. The irregular area is the area in which other nodes are seldom able to communicate. Due to its instability, some existing methods tried to avoid the irregular area completely. The proposed method, named Virtual Boundaries (VBs) prioritizes these areas. The regular communication area’s nodes have high priority to be selected as link vertices; however, when there is no link candidate inside this area, nodes within the irregular area will be selected with respect to their range to the parent node. This technique resulted in a more robust multi-hop topology that can reduce isolated node numbers and increase the percentage of data collected by the base station accordingly

Distributed Recognition of Reference Nodes for Wireless Sensor Network Localization

All known localization techniques for wireless sensor and ad-hoc networks require certain set of reference nodes being used for position estimation. The anchor-free techniques in contrast to anchor-based do not require reference nodes called anchors to be placed in the network area before localization operation itself, but they can establish own reference coordinate system to be used for the relative position estimation. We observed that contemporary anchor-free localization algorithms achieve a low localization error, but dissipate significant energy reserves during the recognition of reference nodes used for the position estimation. Therefore, we have proposed the optimized anchor-free localization algorithm referred to as BRL (Boundary Recognition aided Localization), which achieves a low localization error and mainly reduces the communication cost of the reference nodes recognition phase. The proposed BRL algorithm was investigated throughout the extensive simulations on the database of networks with the different number of nodes and densities and was compared in terms of communication cost and localization error with the known related algorithms such as AFL and CRP. Through the extensive simulations we have observed network conditions where novel BRL algorithm excels in comparison with the state of art