WSN-based wildlife localization framework in dense forests through optimization techniques

Vista sencilla de ítem
dc.contributor.affiliationGonzález-Palacio M., Universidad de Medellin, Carrera 87 #30-65, Antioquia, Medellin, 050026, Colombia
dc.contributor.affiliationGonzález-Palacio L., Universidad Eafit, Carrera 49 #7-50, Antioquia, Medellin, 050022, Colombia
dc.contributor.affiliationAguilar J., IMDEA Networks Institute, Av. Mar Mediterráneo, 22, Madrid, Leganés, 28918, Spain, CEMISID, Facultad de Ingeniería, Universidad de Los Andes, Mérida, 501, Venezuela
dc.contributor.affiliationLe L.B., Institut National de la Recherche Scientifique, Rue De la Gauchetière 800, Montreal, H5A 1K6, QC, Canada
dc.contributor.authorGonzález-Palacio M.
dc.contributor.authorGonzález-Palacio L.
dc.contributor.authorAguilar J.
dc.contributor.authorLe L.B.
dc.date.accessioned2025-09-08T14:23:57Z
dc.date.available2025-09-08T14:23:57Z
dc.date.issued2025
dc.descriptionWildlife in forests is threatened by land use changes, requiring tracking to characterize movement patterns and propose preservation policies. The positioning uses GPS-based collars (End Nodes (ENs)), which are energy-consuming and require a line of sight with the satellites, a condition rarely fulfilled in forests. It motivates using Wireless Sensor Networks, which rely on the Received Signal Strength Indicator (RSSI) and Time of Flight (ToF) to determine the distance between the EN and Anchor Nodes (ANs) with known positions and, subsequently, apply trilateration. However, existing approaches may have significant errors due to multipath and shadow fading caused by dense canopies. Thus, this paper proposes a three-step framework to address these limitations. First, it optimizes the ANs positions, increasing the redundancy of trilateration and coverage, enhancing the likelihood of accurate localization, and ensuring sufficient data to mitigate adverse channel effects. Second, it presents an optimization problem that minimizes the variance of distance estimation since the associated errors can increase exponentially. Finally, it scores the ANs with the most reliable position estimations to mitigate the effects of outliers. Numerical studies show that our optimized AN placement improves coverage by 25% compared to random or equispaced strategies. The distance estimator achieves a Mean Average Percentage Error (MAPE) below 7%, outperforming the Wiener-based estimator at 20%. Finally, our scoring method reduced MAPE to 5.53% with a standard deviation of 7.15% compared with the median strategy that achieved 9.66% and a standard deviation of 15.87% when ten ANs are placed in a region of 100 km2. © 2025
dc.identifier.doi10.1016/j.adhoc.2025.103815
dc.identifier.instnameinstname:Universidad de Medellínspa
dc.identifier.issn15708705
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.urihttp://hdl.handle.net/11407/9139
dc.language.isoeng
dc.publisher.facultyFacultad de Ingenieríasspa
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.publisher.programIngeniería en Energíaspa
dc.relation.citationvolume173
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-86000186903&doi=10.1016%2fj.adhoc.2025.103815&partnerID=40&md5=cb41487ca0e4cfa99435e4f6c0856cda
dc.relation.referencesAlbert J.S., Carnaval A.C., Flantua S.G., Lohmann L.G., Ribas C.C., Riff D., Carrillo J.D., Fan Y., Figueiredo J.J., Guayasamin J.M., Human impacts outpace natural processes in the amazon, Science, 379, 6630, (2023)
dc.relation.referencesIUCN J.S., The IUCN red list of threatened species, (2024)
dc.relation.referencesLovejoy T.E., Nobre C., Amazon tipping point: Last chance for action, pp. 1-2, (2019)
dc.relation.referencesManville A.M., Levitt B.B., Lai H.C., Health and environmental effects to wildlife from radio telemetry and tracking devices—state of the science and best management practices, Front. Vet. Sci., 11, (2024)
dc.relation.referencesGould L.A., Manning A.D., McGinness H.M., Hansen B.D., A review of electronic devices for tracking small and medium migratory shorebirds, Anim. Biotelemetry, 12, 1, (2024)
dc.relation.referencesBeeck S., Mitchell C.N., Jensen A.B., Stenseng L., Pinto Jayawardena T., Olesen D.H., Experimental determination of the ionospheric effects and cycle slip phenomena for Galileo and GPS in the Arctic, Remote. Sens., 15, 24, (2023)
dc.relation.referencesPortas R., Aschenborn O.H., Melzheimer J., Le Roux M., Uiseb K.H., Czirjak G.A., Wachter B., GPS telemetry reveals a zebra with anthrax as putative cause of death for three cheetahs in the namib desert, Front. Vet. Sci., 8, (2021)
dc.relation.referencesOwen-Smith N., Goodall V., Coping with savanna seasonality: comparative daily activity patterns of a frican ungulates as revealed by GPS telemetry, J. Zool., 293, 3, pp. 181-191, (2014)
dc.relation.referencesCabezas J., Yubero R., Visitacion B., Navarro-Garcia J., Algar M.J., Cano E.L., Ortega F., Analysis of accelerometer and GPS data for cattle behaviour identification and anomalous events detection, Entropy, 24, 3, (2022)
dc.relation.referencesu-blox J., NEO-6 series: Versatile u-blox 6 GPS modules, (2025)
dc.relation.referencesSkylab J., 4G pet dog and cat GPS mini tracker collar C032, (2025)
dc.relation.referencesBabic N.L., Johnstone C.P., Reljic S., Sergiel A., Huber D., Reina R.D., Evaluation of physiological stress in free-ranging bears: current knowledge and future directions, Biol. Rev., 98, 1, pp. 168-190, (2023)
dc.relation.referencesPirti A., Accuracy analysis of GPS positioning near the forest environment, Croat. J. For. Eng.: J. Theory Appl. For. Eng., 29, 2, pp. 189-199, (2008)
dc.relation.referencesLink Labs A., What is active RFID?, (2025)
dc.relation.referencesU.S. Department of Agriculture A., Using RFID technology for forest sample zone investigations, (2014)
dc.relation.referencesPereira E., Araujo I., Silva L.F.V., Batista M., Junior S., Barboza E., Santos E., Gomes F., Fraga I.T., Davanso R., Et al., RFID technology for animal tracking: A survey, IEEE J. Radio Freq. Identif., 7, pp. 609-620, (2023)
dc.relation.referencesZorbas D., Sabyrbek A., Supporting critical downlink traffic in LoRaWAN, Comput. Commun., 228, (2024)
dc.relation.referencesKhiadani N., Hendessi F., An energy efficient prediction based protocol for target tracking in wireless sensor networks, Ad Hoc Netw., 167, (2025)
dc.relation.referencesOssongo E., Esseghir M., Merghem-Boulahia L., A multi-agent federated reinforcement learning-based optimization of quality of service in various LoRa network slices, Comput. Commun., 213, pp. 320-330, (2024)
dc.relation.referencesZhang L., Zhou X., Li D., Yang Z., HCCNet: Hybrid coupled cooperative network for robust indoor localization, ACM Trans. Sens. Netw., 20, 4, pp. 1-22, (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.referencesWu P., Yu L., Yi X., Xu L., Liu L., Yi Y., Jiang T., Tao C., Research on WSN reliable ranging and positioning algorithm for forest environment, Sci. Rep., 14, 1, (2024)
dc.relation.referencesLi J., Li P., Li P., Tang L., Zhang X., Wu Q., Self-position awareness based on cascade direct localization over multiple source data, IEEE Trans. Intell. Transp. Syst., 25, 1, pp. 796-804, (2022)
dc.relation.referencesKaur G., Jyoti K., Mittal N., Mittal V., Salgotra R., Optimized approach for localization of sensor nodes in 2D wireless sensor networks using modified learning enthusiasm-based teaching–learning-based optimization algorithm, Algorithms, 16, 1, (2022)
dc.relation.referencesChen H., Yang J., Hao Z., Ga M., Han X., Zhang X., Chen Z., Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm, Sci. Rep., 13, 1, (2023)
dc.relation.referencesLyu S., Li Q., Li Z., Liang H., Chen J., Liu Y., Huang H., Precision location-aware and intelligent scheduling system for monorail transporters in mountain orchards, Agriculture, 13, 11, (2023)
dc.relation.referencesLiu J., Wei L., Du X., Jin L., A localization algorithm based on coevolutionary noise-suppressing Newton method for wireless sensor network, IEEE Sens. J., (2024)
dc.relation.referencesIslam M., Jamil H.M.M., Pranto S.A., Das R.K., Amin A., Khan A., Future industrial applications: Exploring LPWAN-driven IoT protocols, Sensors, 24, 8, (2024)
dc.relation.referencesChaudhari B.S., Zennaro M., Borkar S., LPWAN technologies: Emerging application characteristics, requirements, and design considerations, Futur. Internet, 12, 3, (2020)
dc.relation.referencesGoldsmith A., Wireless Communications, (2005)
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, 12, pp. 54466-54487, (2024)
dc.relation.referencesRecommendation ITU-R P.833-10 - Attenuation in Vegetation: Tech. Rep. P.833-10, (2021)
dc.relation.referencesLima W.G., Lopes A.V., Cardoso C.M., Araujo J.P., Neto M.C., Tostes M.E., Nascimento A.A., Rodriguez M., Barros F.J., LoRa technology propagation models for IoT network planning in the amazon regions, Sensors, 24, 5, (2024)
dc.relation.referencesDias M.H.C., de Assis M.S., An empirical model for propagation loss through tropical woodland in urban areas at UHF, IEEE Trans. Antennas and Propagation, 59, 1, pp. 333-335, (2010)
dc.relation.referencesLyu Y., Liang C., Chen J., Chen P., Guo L., Zhao J., Wang W., Channel measurements and analysis for fixed-wing UAV-to-vehicle communications at 2.7 GHz in rural area, IEEE Antennas Wirel. Propag. Lett., (2024)
dc.relation.referencesGreenstein L.J., Erceg V., Yeh Y.S., Clark M.V., A new path-gain/delay-spread propagation model for digital cellular channels, IEEE Trans. Veh. Technol., 46, 2, pp. 477-485, (1997)
dc.relation.referencesAnderson H.R., Fixed Broadband Wireless System Design, (2003)
dc.relation.referencesFei Z., Li B., Yang S., Xing C., Chen H., Hanzo L., A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems, IEEE Commun. Surv. Tutor., 19, 1, pp. 550-586, (2016)
dc.relation.referencesGunjan Z., A review on multi-objective optimization in wireless sensor networks using nature inspired meta-heuristic algorithms, Neural Process. Lett., 55, 3, pp. 2587-2611, (2023)
dc.relation.referencesCheng S., Li B.-W., Garber P.A., Xia D.-P., Li J.-H., Wild tibetan macaques use a route-based mental map to navigate in large-scale space, Am. J. Primatol., 87, 1, (2025)
dc.relation.referencesCorporation I., Application performance profile for intel xeon processors, (2025)
dc.relation.referencesCentre U.W.H., Los katios national park — whc.unesco.org, (2024)
dc.relation.referencesArnon E., Cain S., Uzan A., Nathan R., Spiegel O., Toledo S., Robust time-of-arrival location estimation algorithms for wildlife tracking, Sensors, 23, 23, pp. 1-23, (2023)
dc.relation.referencesGonzalez-Palacio M., Sepulveda-Cano L.M., Quiza-Montealegre J., D'amato J.P., Mejoramiento del algoritmo ADR en una red de internet de las cosas LoRaWAN usando aprendizaje de Máquina, Rev. Ibérica Sist. Tecnol. Inf., 2020, 5, pp. 164-188, (2020)
dc.relation.referencesGonzalez-Palacio M., Sepulveda-Cano L.M., Tobon-Vallejo D.P., Azurdia-Meza C., Characterisation of path loss models in wireless sensor networks: scenarios, variables, and open problems, Int. J. Ultra Wideband Commun. Syst., 5, 3, pp. 164-188, (2022)
dc.relation.referencesGonzalez-Palacio M., Tobon-Vallejo D., Sepulveda-Cano L.M., Rua S., Pau G., Le L.B., Lorawan path loss measurements in an urban scenario including environmental effects, Data, 8, 1, (2022)
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.referencesCuozzo G., Marini R., Buratti C., Mikhaylov K., On the support of the 2.4 GHz band in the LoRaWAN standard, IEEE Internet Things Mag., pp. 66-71, (2024)
dc.relation.referencesPan H., Qi X., Liu M., Liu L., An UWB-based indoor coplanar localization and anchor placement optimization method, Neural Comput. Appl., 34, 19, pp. 16845-16860, (2022)
dc.relation.referencesShilpi K., Sensor node localization using nature-inspired algorithms with fuzzy logic in WSNs, J. Supercomput., 80, 19, pp. 26776-26804, (2024)
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.referencesLi B., Xu Y., Liu Y., Shi Z., LoRaWAPS: A wide-area positioning system based on LoRa mesh, Appl. Sci., 13, 17, (2023)
dc.relation.referencesVo H., Tran V.L., Ferrero F., Lee F.-Y., Tsai M.-H., Advance path loss model for distance estimation using LoRaWAN network's received signal strength indicator (RSSI), IEEE Access, 12, 12, pp. 83205-83216, (2024)
dc.relation.referencesWei Z., Zhou Z., A combined filtering method for ZigBee indoor distance measurement, Sensors, 24, 10, (2024)
dc.relation.referencesBouse L., King S.A., Chu T., Simplified indoor localization using bluetooth beacons and received signal strength fingerprinting with smartwatch, Sensors, 24, 7, (2024)
dc.relation.referencesWu Z., Li Y., Zhang X., Meng X., Lv X., Wu Y., Multiple anchors and RIS-aided localization method in complex NLOS environments, IEEE Internet Things J., 24, pp. 13578-13588, (2024)
dc.relation.referencesJudd K.L., The law of large numbers with a continuum of iid random variables, J. Econom. Theory, 35, 1, pp. 19-25, (1985)
dc.relation.referencesPettorru G., Pilloni V., Martalo M., Trustworthy localization in IoT networks: A survey of localization techniques, threats, and mitigation, Sensors, 24, 7, (2024)
dc.relation.referencesCorporation S., SX1276/77/78/79 datasheet: Long range, low power RF transceiver, (2021)
dc.relation.referencesSornin N., Luis M., Eirich T., Kramp T., Hersent O., Lorawan specification, LoRa Alliance, 1, (2015)
dc.relation.referencesNetwork T.T., LoRaWAN frequency plans, (2025)
dc.rights.accesoRestricted access
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceAd Hoc Networks
dc.sourceAd Hoc Netw.
dc.sourceScopus
dc.subjectAnchor Node Placement
dc.subjectDistance optimization
dc.subjectResidual error-based localization
dc.subjectWireless Sensor Networks
dc.subjectChannel estimation
dc.subjectDeforestation
dc.subjectGlobal positioning system
dc.subjectMacroinvertebrates
dc.subjectRandom errors
dc.subjectAnchor node placement
dc.subjectAnchor nodes
dc.subjectDistance optimization
dc.subjectLocalisation
dc.subjectNode placement
dc.subjectOptimisations
dc.subjectResidual error
dc.subjectResidual error-based localization
dc.subjectSensors network
dc.subjectWireless sensor
dc.subjectSensor nodes
dc.titleWSN-based wildlife localization framework in dense forests through optimization techniques
dc.typeArticle
dc.type.localArtículospa
dc.type.versioninfo:eu-repo/semantics/publishedVersion

Archivos

Colecciones

Indexados Scopus