{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T03:39:03Z","timestamp":1779334743082,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Commonwealth Scholarships in the UK"},{"name":"Government of Rwanda"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%.<\/jats:p>","DOI":"10.3390\/info13060303","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T01:48:12Z","timestamp":1655430492000},"page":"303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["LoRaWAN Based Indoor Localization Using Random Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Winfred","family":"Ingabire","sequence":"first","affiliation":[{"name":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G40BA, UK"},{"name":"Department of Electrical and Electronics Engineering, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6826-207X","authenticated-orcid":false,"given":"Hadi","family":"Larijani","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G40BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan M.","family":"Gibson","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G40BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayyaz-UI-Haq","family":"Qureshi","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G40BA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Ubiquitous and Seamless Localization: Fusing GNSS Pseudoranges and WLAN Signal Strengths","volume":"2017","author":"Richter","year":"2017","journal-title":"Mob. Inf. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kang, J.M., Yoon, T.S., Kim, E., and Park, J.B. (2020). Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map. Sensors, 20.","DOI":"10.3390\/s20082166"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yang, Z., Schafer, J., and Ganz, A. (2017, January 25\u201326). Disaster response: Victims localization using Bluetooth Low Energy sensors. Proceedings of the 2017 IEEE International Symposium on Technologies for Homeland Security (HST), Waltham, MA, USA.","DOI":"10.1109\/THS.2017.7943504"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Poulose, A., Kim, J., and Han, D.S. (2019). A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements. Appl. Sci., 9.","DOI":"10.3390\/app9204379"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6509","DOI":"10.1007\/s11277-017-4852-5","article-title":"Advanced Indoor Positioning Using Zigbee Wireless Technology","volume":"97","author":"Uradzinski","year":"2017","journal-title":"Wirel. Pers. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ingabire, W., Larijani, H., Gibson, R.M., and Qureshi, A.-U.-H. (2021). Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks. Algorithms, 14.","DOI":"10.3390\/a14110307"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Anjum, M., Khan, M.A., Hassan, S.A., Mahmood, A., and Gidlund, M. (2019, January 24\u201328). Analysis of RSSI fingerprinting in LoRa networks. Proceedings of the 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco.","DOI":"10.1109\/IWCMC.2019.8766468"},{"key":"ref_8","unstructured":"(2022, April 28). Semtech, LoRaWANspecificationv1.1. Available online: https:\/\/www.lora-alliance.org\/technology."},{"key":"ref_9","unstructured":"Bissett, D. (2018). Analysing Tdoa Localisation in LoRa Networks, Delft University of Technology."},{"key":"ref_10","first-page":"1","article-title":"Indoor Localization Enhancement Based on Time of Arrival Using Sectoring Method","volume":"12","author":"Daraj","year":"2020","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_11","first-page":"3033","article-title":"Angle of Arrival Estimation Through a Full-Hardware Approach for Adaptive Beamforming","volume":"67","author":"Avitabile","year":"2020","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_12","first-page":"763","article-title":"CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach","volume":"66","author":"Wang","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Poulose, A., and Han, D.S. (2020). UWB Indoor Localization Using Deep Learning LSTM Networks. Appl. Sci., 10.","DOI":"10.3390\/app10186290"},{"key":"ref_14","first-page":"69","article-title":"Indoor Localization System Using Wi-Fi Technology","volume":"19","author":"Abdul","year":"2019","journal-title":"Iraqi J. Comput. Commun. Control Syst. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez, N., Oca\u00f1a, M., Alonso, J.M., and Kim, E. (2017). Continuous space estimation: Increasing wifi-based indoor localization resolution without increasing the site-survey effort. Sensors, 17.","DOI":"10.3390\/s17010147"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2568","DOI":"10.1109\/COMST.2019.2911558","article-title":"A Survey of Indoor Localization Systems and Technologies","volume":"21","author":"Zafari","year":"2019","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"30149","DOI":"10.1109\/ACCESS.2018.2843325","article-title":"RSSI-Based Indoor Localization With the Internet of Things","volume":"6","author":"Sadowski","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e75","DOI":"10.1002\/itl2.75","article-title":"Experimental data set analysis of RSSI-based indoor and outdoor localization in LoRa networks","volume":"2","author":"Goldoni","year":"2019","journal-title":"Internet Technol. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11778","DOI":"10.1109\/TVT.2019.2940272","article-title":"RSSI-Based LoRa Localization Systems for Large-Scale Indoor and Outdoor Environments","volume":"68","author":"Lam","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1109\/JIOT.2016.2627403","article-title":"Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC","volume":"4","author":"Javed","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Javed, A., Larijani, H., Wixted, A., and Emmanuel, R. (2017, January 26\u201328). Random neural networks based cognitive controller for HVAC in non-domestic building using LoRa. Proceedings of the 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Oxford, UK.","DOI":"10.1109\/ICCI-CC.2017.8109753"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TII.2016.2597746","article-title":"Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology","volume":"13","author":"Javed","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ahmad, J., Larijani, H., Emmanuel, R., Mannion, M., Javed, A., and Phillipson, M. (2017, January 24\u201327). Energy demand prediction through novel random neural network predictor for large non-domestic buildings. Proceedings of the 11th 2017 Annual IEEE International Systems Conference (SysCon), Montreal, QC, Canada.","DOI":"10.1109\/SYSCON.2017.7934803"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Qureshi, A.-U., Larijani, H., Javed, A., Mtetwa, N., and Ahmad, J. (2019, January 21\u201322). Intrusion Detection Using Swarm Intelligence. Proceedings of the 2019 UK\/China Emerging Technologies (UCET), Glasgow, UK.","DOI":"10.1109\/UCET.2019.8881840"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Qureshi, A.-U., Larijani, H., Ahmad, J., and Mtetwa, N. (2018, January 19\u201321). A Novel Random Neural Network Based Approach for Intrusion Detection Systems. Proceedings of the 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, UK.","DOI":"10.1109\/CEEC.2018.8674228"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Qureshi, A.-U., Larijani, H., Mtetwa, N., Javed, A., and Ahmad, J. (2019). RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection. Computers, 8.","DOI":"10.3390\/computers8030059"},{"key":"ref_27","unstructured":"Arai, K. (2021). LoRa RSSI Based Outdoor Localization in an Urban Area Using Random Neural Networks. Intelligent Computing. Lecture Notes in Networks and Systems, Springer."},{"key":"ref_28","unstructured":"Cerkez, C., Aybay, I., and Halici, U. (1997, January 12). A digital neuron realization for the random neural 431 network model. Proceedings of the International Conference on Neural Networks, Houston, TX, USA."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abdelbaki, H., Gelenbe, E., and El-Khamy, S.E. (2000, January 27). Analog hardware implementation of the random neural network model. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy.","DOI":"10.1109\/IJCNN.2000.860772"},{"key":"ref_30","unstructured":"Abdelbaki, H. (2022, April 28). Random Neural Network Simulator (rnnsim) v. 2. Free Simulator. Available online: ftp:\/\/ftp.mathworks.com\/pub\/contrib\/v5\/nnet\/rnnsimv2."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1109\/TCSVT.2002.806808","article-title":"A study of real-time packet video quality using random neural networks","volume":"12","author":"Mohamed","year":"2002","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_32","unstructured":"MultiTech (2022, April 28). MultiTech Conduit LoRa IoT Starter Kit. Available online: https:\/\/www.alliot.co.uk\/product\/multitech-conduit-iot-starter-kit\/."},{"key":"ref_33","unstructured":"Multitech (2022, April 28). Portable, Handheld End-Point Device for Conducting LoRa\u00ae Site Surveys (MTDOT-BOX Series). Available online: https:\/\/www.multitech.com\/brands\/multiconnect-mdot-box."},{"key":"ref_34","unstructured":"Tera Term (2022, April 28). Tera Term Software. Available online: https:\/\/ttssh2.osdn.jp\/index.html.en."},{"key":"ref_35","unstructured":"Destiny-College (2022, April 28). Destiny-College Building Glasgow City. Available online: https:\/\/destiny-college.com\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1162\/neco.1989.1.4.502","article-title":"Random Neural Networks with Negative and Positive Signal and Product Form Solution","volume":"1","author":"Gelenbe","year":"1989","journal-title":"Neural Comput."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Islam, B., Islam, T., Kaur, J., and Nirjon, S. (2019, January 11\u201315). LoRaIn: Making a Case for LoRa in Indoor Localization. Proceedings of the 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kyoto, Japan.","DOI":"10.1109\/PERCOMW.2019.8730767"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kim, K., Li, S., Heydariaan, M., Smaoui, N., Gnawali, O., Suh, W., Suh, M.J., and Kim, J.I. (2021). Feasibility of LoRa for Smart Home Indoor Localization. Appl. Sci., 11.","DOI":"10.3390\/app11010415"},{"key":"ref_39","unstructured":"Henriksson, R. (2016). Indoor Positioning in LoRaWAN Networks. [Master\u2019s Thesis, Halmers University of Technology]."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Han, L., Jiang, L., Kong, Q., Wang, J., Zhang, A., and Song, S. (2019). Indoor Localization within Multi-Story Buildings Using MAC and RSSI Fingerprint Vectors. Sensors, 19.","DOI":"10.3390\/s19112433"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/IOTM.0001.2000019","article-title":"RSSI Fingerprinting-Based Localization Using Machine Learning in LoRa Networks","volume":"3","author":"Anjum","year":"2020","journal-title":"IEEE Internet Things Mag."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Manzoni, P., Calafate, C.T., Cano, J.-C., and Hern\u00e1ndez-Orallo, E. (2019). Indoor Vehicles Geolocalization Using LoRaWAN. Futur. Internet, 11.","DOI":"10.3390\/fi11060124"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/6\/303\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:33:13Z","timestamp":1760139193000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/6\/303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,16]]},"references-count":42,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["info13060303"],"URL":"https:\/\/doi.org\/10.3390\/info13060303","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,16]]}}}