Novel RSSI-Based localization in LoRaWAN using probability density estimation similarity-based techniques

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dc.contributor.affiliationGonzález-Palacio M., Universidad de Medellin, Carrera 87 # 30-65, Medellín, 050026, Colombia
dc.contributor.affiliationLuna-delRisco M., Universidad de Medellin, Carrera 87 # 30-65, Medellín, 050026, Colombia
dc.contributor.affiliationGarcía-Giraldo J., Universidad de Medellin, Carrera 87 # 30-65, Medellín, 050026, Colombia
dc.contributor.affiliationArrieta-González C., Universidad de Medellin, Carrera 87 # 30-65, Medellín, 050026, Colombia
dc.contributor.affiliationGonzález-Palacio L., Universidad Eafit, Carrera 49 # 7-50, Medellín, 50022, Colombia
dc.contributor.affiliationRöhrig C., Fachhochschule Dortmund, University of Applied Sciences and Arts, Sonnenstraß 96, Dortmund, 44139, Germany
dc.contributor.affiliationLe L.B., Institut National de la Recherche Scientifique, Rue De la Gauchetière 800, Montreal, H5A 1K6, Canada
dc.contributor.authorGonzález-Palacio M.
dc.contributor.authorLuna-delRisco M.
dc.contributor.authorGarcía-Giraldo J.
dc.contributor.authorArrieta-González C.
dc.contributor.authorGonzález-Palacio L.
dc.contributor.authorRöhrig C.
dc.contributor.authorLe L.B.
dc.date.accessioned2025-09-08T14:23:53Z
dc.date.available2025-09-08T14:23:53Z
dc.date.issued2025
dc.descriptionIn localization tasks of Internet of Things (IoT) End Nodes (ENs), the network lifetime and energy efficiency are critical. Due to power constraints, traditional systems like the Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), and Galileo may be unsuitable for IoT applications. As a result, Long-Range Wide Area Network (LoRaWAN) has gained attention due to its large coverage and low power requirements. Traditional localization strategies typically estimate the distance between the EN and Anchor Nodes (ANs) using the Received Signal Strength Indicator (RSSI) combined with a path loss model. However, the accuracy of such an approach can be compromised by different undesirable transmission effects, such as interference, affecting the RSSI. This work introduces a novel distance estimation method that leverages the similarity between Probability Density Functions (PDFs) of RSSI from measurement campaigns and those from deployed ENs. By employing metrics including the enhanced versions of Euclidean and Minkowski distances, the proposed approach surpasses conventional channel-based techniques, achieving a Mean Absolute Percentage Error (MAPE) of 3.9% for wireless environments with a shadowing standard deviation up to 16 dB. Furthermore, when utilizing Kernel Density Estimation (KDE) for localization, the method demonstrated an 95.1% enhancement in accuracy compared to the localization strategy based on the loglinear path loss model. © 2025 Elsevier B.V.
dc.identifier.doi10.1016/j.iot.2025.101551
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn25426605
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/9128
dc.language.isoeng
dc.publisher.facultyFacultad de Ingenieríasspa
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.publisher.programIngeniería en Energíaspa
dc.relation.citationvolume31
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85219700635&doi=10.1016%2fj.iot.2025.101551&partnerID=40&md5=08dfffea58221934714918a9ea98c43f
dc.relation.referencesMoradbeikie A., Keshavarz A., Rostami H., Paiva S., Lopes S.I., A cost-effective LoRaWAN-based IoT localization method using fixed reference nodes and dual-slope path-loss modeling, Internet Things, 24, (2023)
dc.relation.referencesRodriguez-Garcia P., Li Y., Lopez-Lopez D., Juan A.A., Strategic decision making in smart home ecosystems: A review on the use of artificial intelligence and internet of things, Internet Things, 22, (2023)
dc.relation.referencesBoujelben M., Benmessaoud Z., Abid M., Elleuchi M., An efficient system for water leak detection and localization based on IoT and lightweight deep learning, Internet Things, 24, (2023)
dc.relation.referencesFazlollahtabar H., Internet of things-based SCADA system for configuring/reconfiguring an autonomous assembly process, Robotica, 40, 3, pp. 672-689, (2022)
dc.relation.referencesKumar D., Kr Singh R., Mishra R., Fosso Wamba S., Applications of the internet of things for optimizing warehousing and logistics operations: A systematic literature review and future research directions, Comput. Ind. Eng., 171, (2022)
dc.relation.referencesLiu Y., Chen R., Zhou Y., Liu M., Hui Y., Cheng N., RFID-based vehicle localization using virtual wideband multi-frequency continuous wave, IEEE J. Radio Freq. Identif., 7, pp. 222-232, (2023)
dc.relation.referencesPolvara R., Del Duchetto F., Neumann G., Hanheide M., Navigate-and-seek: a robotics framework for people localization in agricultural environments, IEEE Robot. Autom. Lett., 6, 4, pp. 6577-6584, (2021)
dc.relation.referencesThangavelu S., Anbazhagan R., Perumal S., Gopikrishna E., Siddartha M., Unmanned aerial vehicle localization for device-to-device communication in fifth generation networks using modified penguin search optimization, Comput. Electr. Eng., 109, (2023)
dc.relation.referencesShu Y., Xu P., Niu X., Chen Q., Qiao L., Liu J., High-rate attitude determination of moving vehicles with GNSS: GPS, BDS, GLONASS, and Galileo, IEEE Trans. Instrum. Meas., 71, pp. 1-13, (2022)
dc.relation.referencesZhang Z., Pan L., Current performance of open position service with almost fully deployed multi-GNSS constellations: GPS, GLONASS, Galileo, BDS-2, and BDS-3, Adv. Space Res., 69, 5, pp. 1994-2019, (2022)
dc.relation.referencesChen K., Tan G., Cao J., Lu M., Fan X., Modeling and improving the energy performance of GPS receivers for location services, IEEE Sens. J., 20, 8, pp. 4512-4523, (2019)
dc.relation.referencesSanislav T., Mois G.D., Zeadally S., Folea S.C., Energy harvesting techniques for internet of things (IoT), IEEE Access, 9, pp. 39530-39549, (2021)
dc.relation.referencesLovarelli D., Brandolese C., Leliveld L., Finzi A., Riva E., Grotto M., Provolo G., Development of a new wearable 3D sensor node and innovative open classification system for dairy cows’ behavior, Animals, 12, 11, (2022)
dc.relation.referencesShaikh F.K., Karim S., Zeadally S., Nebhen J., Recent trends in internet-of-things-enabled sensor technologies for smart agriculture, IEEE Internet Things J., 9, 23, pp. 23583-23598, (2022)
dc.relation.referencesJanssen T., Koppert A., Berkvens R., Weyn M., A survey on IoT positioning leveraging LPWAN, GNSS, and LEO-PNT, IEEE Internet Things J., 10, 13, pp. 11135-11159, (2023)
dc.relation.referencesMarquez L.E., Calle M., Understanding LoRa-based localization: Foundations and challenges, IEEE Internet Things J., 10, 13, pp. 11185-11198, (2023)
dc.relation.referencesAlmuhaya M.A., Jabbar W.A., Sulaiman N., Abdulmalek S., A survey on Lorawan technology: Recent trends, opportunities, simulation tools and future directions, Electronics, 11, 1, (2022)
dc.relation.referencesYegin A., Kramp T., Dufour P., Gupta R., Soss R., Hersent O., Hunt D., Sornin N., 3 - LoRaWAN protocol: specifications, security, and capabilities, LPWAN Technologies for IoT and M2M Applications, pp. 37-63, (2020)
dc.relation.referencesGoldsmith A., Wireless Communications, (2005)
dc.relation.referencesZare M., Battulwar R., Seamons J., Sattarvand J., Applications of wireless indoor positioning systems and technologies in underground mining: A review, Min. Met. Explor., 38, pp. 2307-2322, (2021)
dc.relation.referencesLaoudias C., Moreira A., Kim S., Lee S., Wirola L., Fischione C., A survey of enabling technologies for network localization, tracking, and navigation, IEEE Commun. Surv. Tutor., 20, 4, pp. 3607-3644, (2018)
dc.relation.referencesChen H., Yang J., Hao Z., Qi T., Liu T., Research on indoor multi-floor positioning method based on LoRa, Comput. Netw., 254, (2024)
dc.relation.referencesBaldini G., Bonavitacola F., LoRa radio frequency fingerprinting with residual of variational mode decomposition and hybrid machine-learning/deep-learning optimization, Electronics, 13, 10, (2024)
dc.relation.referencesIslam K.Z., Murray D., Diepeveen D., Jones M.G., Sohel F., LoRa localisation using single mobile gateway, Comput. Commun., 219, pp. 182-193, (2024)
dc.relation.referencesMohar S.S., Goyal S., Kaur R., JAYA NL-WSN: Jaya algorithm for node localization issue in wireless sensor network, Wirel. Pers. Commun., 137, 1, pp. 287-324, (2024)
dc.relation.referencesChen H., Xing F., Yang Q., Shu Y., Shi Z., Chen J., Tao Z., A lightweight mobile-anchor-based multi-target outdoor localization scheme using LoRa communication, IEEE Trans. Green Commun. Netw., (2023)
dc.relation.referencesMagsi S.A., Khir M.H.B.M., Nawi I.B.M., Hasan M.A., Ullah Z., Khan F.U., Saboor A., Siddiqui M.A., Experimental evaluation of trilateration-based outdoor localization with LoRaWAN, Comput. Mater. Contin., 75, 1, (2023)
dc.relation.referencesPerkovic T., Dujic Rodic L., Sabic J., Solic P., Machine learning approach towards LoRaWAN indoor localization, Electronics, 12, 2, (2023)
dc.relation.referencesSvertoka E., Rusu-Casandra A., Burget R., Marghescu I., Hosek J., Ometov A., LoRaWAN: Lost for localization?, IEEE Sens. J., 22, 23, pp. 23307-23319, (2022)
dc.relation.referencesSimka M., Polak L., On the RSSI-based indoor localization employing LoRa in the 2.4 GHz ISM band, Radioengineering, 31, 1, pp. 135-143, (2022)
dc.relation.referencesZhu H., Tsang K.-F., Liu Y., Wei Y., Wang H., Wu C.K., Chi H.R., Extreme RSS based indoor localization for LoRaWAN with boundary autocorrelation, IEEE Trans. Ind. Inform., 17, 7, pp. 4458-4468, (2020)
dc.relation.referencesLam K.-H., Cheung C.-C., Lee W.-C., RSSI-based LoRa localization systems for large-scale indoor and outdoor environments, IEEE Trans. Veh. Technol., 68, 12, pp. 11778-11791, (2019)
dc.relation.referencesKwasme H., Ekin S., RSSI-based localization using LoRaWAN technology, IEEE Access, 7, pp. 99856-99866, (2019)
dc.relation.referencesSavazzi P., Goldoni E., Vizziello A., Favalli L., Gamba P., A wiener-based RSSI localization algorithm exploiting modulation diversity in LoRa networks, IEEE Sens. J., 19, 24, pp. 12381-12388, (2019)
dc.relation.referencesOkumura Y., Field strength and its variability in VHF and UHF land-mobile radio service, Rev. Electr. Commun. Lab., 16, pp. 825-873, (1968)
dc.relation.referencesJouhari M., Saeed N., Alouini M.-S., Amhoud E.M., A survey on scalable LoRaWAN for massive IoT: Recent advances, potentials, and challenges, IEEE Commun. Surv. Tutor., 25, 3, pp. 1841-1876, (2023)
dc.relation.referencesAlobaidy H.A., Nordin R., Singh M.J., Abdullah N.F., Haniz A., Ishizu K., Matsumura T., Kojima F., Ramli N., Low-altitude-platform-based airborne IoT network (LAP-AIN) for water quality monitoring in harsh tropical environment, IEEE Internet Things J., 9, 20, pp. 20034-20054, (2022)
dc.relation.referencesBatalha I.D.S., Lopes A.V.R., Lima W.G., Barbosa Y.H., Neto M.C.D.A., Barros F.J., Cavalcante G.P., Large-scale modeling and analysis of uplink and downlink channels for LoRa technology in suburban environments, IEEE Internet Things J., 9, 23, pp. 24477-24491, (2022)
dc.relation.referencesEl Chall R., Lahoud S., El Helou M., LoRaWAN network: Radio propagation models and performance evaluation in various environments in Lebanon, IEEE Internet Things J., 6, 2, pp. 2366-2378, (2019)
dc.relation.referencesStrang G., Linear Algebra and its Applications, (2012)
dc.relation.referencesGonzalez-Palacio M., Tobon-Vallejo D., Sepulveda-Cano L.M., Rua S., Le L.B., Machine-learning-based combined path loss and shadowing model in LoRaWAN for energy efficiency enhancement, IEEE Internet Things J., 10, 12, pp. 10725-10739, (2023)
dc.relation.referencesDeza E., Deza M.M., Deza M.M., Deza E., Encyclopedia of Distances, (2009)
dc.relation.referencesKullback S., Leibler R.A., On information and sufficiency, Ann. Math. Stat., 22, 1, pp. 79-86, (1951)
dc.relation.referencesParzen E., On estimation of a probability density function and mode, Ann. Math. Stat., 33, 3, pp. 1065-1076, (1962)
dc.relation.referencesScott D.W., Multivariate Density Estimation: Theory, Practice, and Visualization, (2015)
dc.relation.referencesBhattacharyya A., On a measure of divergence between two statistical populations defined by their probability distribution, Bull. Calcutta Math. Soc., 35, pp. 99-110, (1943)
dc.relation.referencesLoRa-Alliance A., RP002-1.0.1 LoRaWAN® regional parameters, (2022)
dc.relation.referencesGonzalez-Palacio M., Tobon-Vallejo D., Sepulveda-Cano L.M., Luna-delRisco M., Roehrig C., Le L.B., Machine-learning-assisted transmission power control for LoRaWAN considering environments with high signal-to-noise variation, IEEE Access, (2024)
dc.relation.referencesBroyden C.G., The convergence of a class of double-rank minimization algorithms 1. general considerations, IMA J. Appl. Math., 6, 1, pp. 76-90, (1970)
dc.relation.referencesRappaport T.S., Wireless Communications: Principles and Practice, 2/E, (2010)
dc.rights.accesoRestricted access
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceInternet of Things (The Netherlands)
dc.sourceInternet. Thing.
dc.sourceScopus
dc.subjectKernel Density Estimation
dc.subjectLocalization
dc.subjectLoRaWAN
dc.subjectProbability Density Function
dc.titleNovel RSSI-Based localization in LoRaWAN using probability density estimation similarity-based techniques
dc.typeArticle
dc.type.localArtículospa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

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