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Implementation of an indoor localisation algorithm for Internet of Things

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dc.contributor.author Sotenga, PZ
dc.contributor.author Djouani, K
dc.contributor.author Kurien, AM
dc.contributor.author Mwila, Martin
dc.date.accessioned 2022-02-25T09:52:24Z
dc.date.available 2022-02-25T09:52:24Z
dc.date.issued 2020-06
dc.identifier.citation Sotenga, P., Djouani, K., Kurien, A. & Mwila, M. 2020. Implementation of an indoor localisation algorithm for Internet of Things. <i>Future Generation Computer Systems - The International Journal of eScience, 107.</i> http://hdl.handle.net/10204/12287 en_ZA
dc.identifier.issn 0167-739X
dc.identifier.issn 1872-7115
dc.identifier.uri https://doi.org/10.1016/j.future.2018.01.056
dc.identifier.uri http://hdl.handle.net/10204/12287
dc.description.abstract Internet of Things (IoT) is usually associated with the acquisition of sensor node information and controlling of things. However, the inadequateness of location information of these sensor nodes compromises the intelligence of the IoT network. With an emphasis on indoor environments, localisation in IoT has become a very important area for research due to the fact that well-established positioning services such as the Global Positioning System (GPS) are not viable indoors. In essence, the difficulty for satellite signals of such existing systems to reach indoor environments causes huge localisation inaccuracies. Therefore, indoor localisation in recent time has been based on Wireless Sensor Networks (WSN). In this work, distributive indoor localisation accuracy and computational performance as implemented in real life towards IoT are of concern. Therefore, this work presents a methodology comprised of a mix of existing techniques to integrate indoor localisation of sensor nodes and IoT in a realistic environment and at an acceptable degree of localisation accuracy. The major contribution presented in this work is the attainment of an acceptable level of localisation accuracy while maintaining high computational efficiency on a developed hardware sensor node prototype. In achieving this, the implementation issues regarding the complexity of localisation algorithm and hardware computational capabilities are tackled. In ensuring that this work is aligned with the IoT context, an Internet and Intranet enabled connectivity for real-time access to the location information of sensor nodes is developed and presented. This work is also achieved through the use of a distributive online based localisation algorithm based on Kalman filtering Received Signal Strength Indicator (RSSI) and Gauss–Newton Algorithm (GNA). A sensor node prototype capable of handling complex computations is developed and presented. A gateway device and an IoT framework are also proposed and implemented based on Linux, Apache, MySQL, PHP (LAMP) server to provide global and local access to real-time location information of the sensor nodes. The algorithm is first simulated for pre-validation and then ported to the sensor node prototypes. The computational power of the hardware is analysed based on the time it takes to perform the GNA based localisation process towards convergence. The Root Mean Square Error (RMSE) is computed for analysing the accuracy level of the proposed concept. This work further presents findings based on the effect of node orientation changes as the localisation process is performed and logged, which is an issue of great concerned to be tackled in future works. en_US
dc.format Abstract en_US
dc.language.iso en en_US
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S0167739X17320307 en_US
dc.source Future Generation Computer Systems - The International Journal of eScience, 107 en_US
dc.subject Computation en_US
dc.subject Gauss–Newton en_US
dc.subject Localisation en_US
dc.subject Internet of Things en_US
dc.subject IoT en_US
dc.subject LAMP en_US
dc.subject Received Signal Strength Indicator en_US
dc.subject Wireless Sensor Networks en_US
dc.subject WSN en_US
dc.title Implementation of an indoor localisation algorithm for Internet of Things en_US
dc.type Article en_US
dc.description.pages 1037-1046 en_US
dc.description.note © 2018 Elsevier B.V. All rights reserved. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://www.sciencedirect.com/science/article/pii/S0167739X17320307 en_US
dc.description.cluster Defence and Security en_US
dc.description.impactarea Unknown en_US
dc.identifier.apacitation Sotenga, P., Djouani, K., Kurien, A., & Mwila, M. (2020). Implementation of an indoor localisation algorithm for Internet of Things. <i>Future Generation Computer Systems - The International Journal of eScience, 107</i>, http://hdl.handle.net/10204/12287 en_ZA
dc.identifier.chicagocitation Sotenga, PZ, K Djouani, AM Kurien, and Martin Mwila "Implementation of an indoor localisation algorithm for Internet of Things." <i>Future Generation Computer Systems - The International Journal of eScience, 107</i> (2020) http://hdl.handle.net/10204/12287 en_ZA
dc.identifier.vancouvercitation Sotenga P, Djouani K, Kurien A, Mwila M. Implementation of an indoor localisation algorithm for Internet of Things. Future Generation Computer Systems - The International Journal of eScience, 107. 2020; http://hdl.handle.net/10204/12287. en_ZA
dc.identifier.ris TY - Article AU - Sotenga, PZ AU - Djouani, K AU - Kurien, AM AU - Mwila, Martin AB - Internet of Things (IoT) is usually associated with the acquisition of sensor node information and controlling of things. However, the inadequateness of location information of these sensor nodes compromises the intelligence of the IoT network. With an emphasis on indoor environments, localisation in IoT has become a very important area for research due to the fact that well-established positioning services such as the Global Positioning System (GPS) are not viable indoors. In essence, the difficulty for satellite signals of such existing systems to reach indoor environments causes huge localisation inaccuracies. Therefore, indoor localisation in recent time has been based on Wireless Sensor Networks (WSN). In this work, distributive indoor localisation accuracy and computational performance as implemented in real life towards IoT are of concern. Therefore, this work presents a methodology comprised of a mix of existing techniques to integrate indoor localisation of sensor nodes and IoT in a realistic environment and at an acceptable degree of localisation accuracy. The major contribution presented in this work is the attainment of an acceptable level of localisation accuracy while maintaining high computational efficiency on a developed hardware sensor node prototype. In achieving this, the implementation issues regarding the complexity of localisation algorithm and hardware computational capabilities are tackled. In ensuring that this work is aligned with the IoT context, an Internet and Intranet enabled connectivity for real-time access to the location information of sensor nodes is developed and presented. This work is also achieved through the use of a distributive online based localisation algorithm based on Kalman filtering Received Signal Strength Indicator (RSSI) and Gauss–Newton Algorithm (GNA). A sensor node prototype capable of handling complex computations is developed and presented. A gateway device and an IoT framework are also proposed and implemented based on Linux, Apache, MySQL, PHP (LAMP) server to provide global and local access to real-time location information of the sensor nodes. The algorithm is first simulated for pre-validation and then ported to the sensor node prototypes. The computational power of the hardware is analysed based on the time it takes to perform the GNA based localisation process towards convergence. The Root Mean Square Error (RMSE) is computed for analysing the accuracy level of the proposed concept. This work further presents findings based on the effect of node orientation changes as the localisation process is performed and logged, which is an issue of great concerned to be tackled in future works. DA - 2020-06 DB - ResearchSpace DP - CSIR J1 - Future Generation Computer Systems - The International Journal of eScience, 107 KW - Computation KW - Gauss–Newton KW - Localisation KW - Internet of Things KW - IoT KW - LAMP KW - Received Signal Strength Indicator KW - Wireless Sensor Networks KW - WSN LK - https://researchspace.csir.co.za PY - 2020 SM - 0167-739X SM - 1872-7115 T1 - Implementation of an indoor localisation algorithm for Internet of Things TI - Implementation of an indoor localisation algorithm for Internet of Things UR - http://hdl.handle.net/10204/12287 ER - en_ZA
dc.identifier.worklist 24410 en_US


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