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

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    [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). 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    Indoor Localization Based on Wireless Sensor Networks

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    Indoor localization techniques based on wireless sensor networks (WSNs) have been increasingly used in various applications such as factory automation, intelligent building, facility management, security, and health care. However, existing localization techniques cannot meet the accuracy requirement of many applications. Meanwhile, some localization algorithms are affected by environmental conditions and cannot be directly used in an indoor environment. Cost is another limitation of the existing localization algorithms. This thesis is to address those issues of indoor localization through a new Sensing Displacement (SD) approach. It consists of four major parts: platform design, SD algorithm development, SD algorithm improvement, and evaluation. Platform design includes hardware design and software design. Hardware design is the foundation for the system, which consists of the motion sensors embedded on mobile nodes and WSN design. Motion sensors are used to collect motion information for the localizing objects. A WSN is designed according to the characteristics of an indoor scenario. A Cloud Computing based system architecture is developed to support the software design of the proposed system. In order to address the special issues in an indoor environment, a new Sensing Displacement algorithm is developed, which estimates displacement of a node based on the motion information from the sensors embedded on the node. The sensor assembly consists of acceleration sensors and gyroscope sensors, separately sensing the acceleration and angular velocity of the localizing object. The first SD algorithm is designed in a way to be used in a 2-D localization demo to validate the proposal. A detailed analysis of the results of 2-D SD algorithm reveals that there are two critical issues (sensor’s noise and cumulative error) affecting the measurement results. Therefore a low-pass filter and a modified Kalman filter are introduced to solve the issue of sensor’s noises. An inertia tensor factor is introduced to address the cumulative error in a 3-D SD algorithm. Finally, the proposed SD algorithm is evaluated against the commercial AeroScout (WiFi-RFID) system and the ZigBee based Fingerprint algorithm

    Reference Nodes Selection for Anchor-Free Localization in Wireless Sensor Networks

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    DizertačnĂ­ prĂĄce se zabĂœvĂĄ nĂĄvrhem novĂ©ho bezkotevnĂ­ho lokalizačnĂ­ho algoritmu slouĆŸĂ­cĂ­ho pro vĂœpočet pozice uzlĆŻ v bezdrĂĄtovĂœch senzorovĂœch sĂ­tĂ­ch. ProvedenĂ© studie ukĂĄzaly, ĆŸe dosavadnĂ­ bezkotevnĂ­ lokalizačnĂ­ algoritmy, pracujĂ­cĂ­ v paralelnĂ­m reĆŸimu, dosahujĂ­ malĂœch lokalizačnĂ­ch chyb. Jejich nevĂœhodou ovĆĄem je, ĆŸe pƙi sestavenĂ­ mnoĆŸiny referenčnĂ­ch uzlu spotƙebovĂĄvajĂ­ daleko větĆĄĂ­ mnoĆŸstvĂ­ energie neĆŸ algoritmy pracujĂ­cĂ­ v inkrementĂĄlnĂ­m reĆŸimu. ParalelnĂ­ lokalizačnĂ­ algoritmy vyuĆŸĂ­vajĂ­ pro určenĂ­ pozice referenčnĂ­ uzly nachĂĄzejĂ­cĂ­ se na protilehlĂœch hranĂĄch bezdrĂĄtovĂ© sĂ­tě. NovĂœ lokalizačnĂ­ algoritmus označenĂœ jako BRL (Boundary Recognition aided Localization) je zaloĆŸen na myĆĄlence decentralizovaně detekovat uzly leĆŸĂ­cĂ­ na hranici sĂ­ti a pouze z tĂ©to mnoĆŸiny vybrat potƙebnĂœ počet referenčnĂ­ch uzlu. PomocĂ­ navrĆŸenĂ©ho pƙístupu lze znaĆŸně snĂ­ĆŸit mnoĆŸstvĂ­ energie spotƙebovanĂ© v prĆŻběhu procesu vĂœběru referenčnĂ­ch uzlĆŻ v senzorovĂ©m poli. DalĆĄĂ­m pƙínosem ke snĂ­ĆŸenĂ­ energetickĂœch nĂĄroku a zĂĄroveƈ zachovĂĄnĂ­ nĂ­zkĂ© lokalizačnĂ­ chyby je vyuĆŸitĂ­ procesu multilaterace se tƙemi, eventuĂĄlně čtyƙmi referenčnĂ­mi body. V rĂĄmci prĂĄce byly provedeny simulace několika dĂ­lčích algoritmu a jejich funkčnost byla ověƙena experimentĂĄlně v reĂĄlnĂ© senzorovĂ© sĂ­ti. NavrĆŸenĂœ algoritmus BRL byl porovnĂĄn z hlediska lokalizačnĂ­ chyby a počtu zpracovanĂœch paketĆŻ s několika znĂĄmĂœmi lokalizačnĂ­mi algoritmy. VĂœsledky simulacĂ­ dokĂĄzaly, ĆŸe navrĆŸenĂœ algoritmus pƙedstavuje efektivnĂ­ ƙeĆĄenĂ­ pro pƙesnou a zĂĄroveƈ nĂ­zkoenergetickou lokalizaci uzlĆŻ v bezdrĂĄtovĂœch senzorovĂœch sĂ­tĂ­ch.The doctoral thesis is focused on a design of a novel anchor free localization algorithm for wireless sensor networks. As introduction, the incremental and concurrent anchor free localization algorithms are presented and their performance is compared. It was found that contemporary anchor free localization algorithms working in the concurrent manner achieve a low localization error, but dissipate signicant energy reserves. A new Boundary Recognition Aided Localization algorithm presented in this thesis is based on an idea to recognize the nodes placed on the boundary of network and thus reduce the number of transmission realized during the reference nodes selection phase of the algorithm. For the position estimation, the algorithm employs the multilateration technique that work eectively with the low number of the reference nodes. Proposed algorithms are tested through the simulations and validated by the real experiment with the wireless sensor network. The novel Boundary Recognition Aided Localization algorithm is compared with the known algorithms in terms of localization error and the communication cost. The results show that the novel algorithm presents powerful solution for the anchor free localization.

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

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    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 reÂŹstricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted CenÂŹtroid 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
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