dc.contributor.author | González-Palacio M | |
dc.contributor.author | Tobón-Vallejo D | |
dc.contributor.author | Sepúlveda-Cano L.M | |
dc.contributor.author | Rúa S | |
dc.contributor.author | Pau G | |
dc.contributor.author | Le L.B. | |
dc.date.accessioned | 2023-10-24T19:24:22Z | |
dc.date.available | 2023-10-24T19:24:22Z | |
dc.date.created | 2023 | |
dc.identifier.issn | 23065729 | |
dc.identifier.uri | http://hdl.handle.net/11407/7936 | |
dc.description.abstract | LoRaWAN is a widespread protocol by which Internet of things end nodes (ENs) can exchange information over long distances via their gateways. To deploy the ENs, it is mandatory to perform a link budget analysis, which allows for determining adequate radio parameters like path loss (PL). Thus, designers use PL models developed based on theoretical approaches or empirical data. Some previous measurement campaigns have been performed to characterize this phenomenon, primarily based on distance and frequency. However, previous works have shown that weather variations also impact PL, so using the conventional approaches and available datasets without capturing important environmental effects can lead to inaccurate predictions. Therefore, this paper delivers a data descriptor that includes a set of LoRaWAN measurements performed in Medellín, Colombia, including PL, distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and energy, among other things. This dataset can be used by designers who need to fit highly accurate PL models. As an example of the dataset usage, we provide some model fittings including log-distance, and multiple linear regression models with environmental effects. This analysis shows that including such variables improves path loss predictions with an RMSE of 1.84 dB and an R2 of 0.917. Dataset: https://github.com/magonzalezudem/MDPI_LoRaWAN_Dataset_With_Environmental_Variables Dataset License: CC-BY 4.0. © 2022 by the authors. | eng |
dc.language.iso | eng | |
dc.publisher | MDPI | |
dc.relation.isversionof | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146828989&doi=10.3390%2fdata8010004&partnerID=40&md5=8960e450e806f419b35c1edc69c297f3 | |
dc.source | Data | |
dc.source | Data | eng |
dc.subject | Environmental variables | eng |
dc.subject | LoRaWAN | eng |
dc.subject | Path loss | eng |
dc.title | LoRaWAN Path Loss Measurements in an Urban Scenario including Environmental Effects | eng |
dc.type | Data Paper | |
dc.rights.accessrights | info:eu-repo/semantics/restrictedAccess | |
dc.publisher.program | Ingeniería de Telecomunicaciones | spa |
dc.type.spa | Artículo de datos | |
dc.identifier.doi | 10.3390/data8010004 | |
dc.relation.citationvolume | 8 | |
dc.relation.citationissue | 1 | |
dc.publisher.faculty | Facultad de Ingenierías | spa |
dc.affiliation | González-Palacio, M., Telecommunications Department, Universidad de Medellín, Carrera 87 #30-65, Medellín, 050026, Colombia | |
dc.affiliation | Tobón-Vallejo, D., Telecommunications Department, Universidad de Medellín, Carrera 87 #30-65, Medellín, 050026, Colombia | |
dc.affiliation | Sepúlveda-Cano, L.M., Accountancy Department, Universidad EAFIT, Carrera 49 # 7 Sur-50, Medellín, 050022, Colombia | |
dc.affiliation | Rúa, S., Electronics Department, Universidad Nacional Abierta y a Distancia, Medellín, 050012, Colombia | |
dc.affiliation | Pau, G., Informatics Department, Università Kore di Enna, Enna94100, Italy | |
dc.affiliation | Le, L.B., Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada | |
dc.relation.references | Casaccia, S., Romeo, L., Calvaresi, A., Morresi, N., Monteriu, A., Frontoni, E., Scalise, L., Revel, G.M., Measurement of users’ well-being through domotic sensors and machine learning algorithms (2020) IEEE Sens. J, 20, pp. 8029-8038 | |
dc.relation.references | Rajesh, P., Shajin, F.H., Kannayeram, G., A novel intelligent technique for energy management in smart home using internet of things (2022) Appl. Soft Comput, 128, p. 109442 | |
dc.relation.references | Caruso, A., Chessa, S., Escolar, S., Barba, J., López, J.C., Collection of data with drones in precision agriculture: Analytical model and LoRa case study (2021) IEEE Internet Things J, 8, pp. 16692-16704 | |
dc.relation.references | Mahdavinejad, M.S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., Sheth, A.P., Machine learning for Internet of Things data analysis: A survey (2018) Digit. Commun. Netw, 4, pp. 161-175 | |
dc.relation.references | Athanasaki, D.E., Mastorakis, G., Mavromoustakis, C.X., Markakis, E.K., Pallis, E., Panagiotakis, S., IoT Detection Techniques for Modeling Post-Fire Landscape Alteration Using Multitemporal Spectral Indices (2020) Convergence of Artificial Intelligence and the Internet of Things, pp. 347-367. , Springer, Berlin/Heidelberg, Germany | |
dc.relation.references | Hadipour, M., Derakhshandeh, J.F., Shiran, M.A., An experimental setup of multi-intelligent control system (MICS) of water management using the Internet of Things (IoT) (2020) ISA Trans, 96, pp. 309-326. , 31285060 | |
dc.relation.references | Adeel, A., Gogate, M., Farooq, S., Ieracitano, C., Dashtipour, K., Larijani, H., Hussain, A., A survey on the role of wireless sensor networks and IoT in disaster management (2019) Geological Disaster Monitoring Based on Sensor Networks, pp. 57-66. , Springer, Berlin/Heidelberg, Germany | |
dc.relation.references | Ikpehai, A., Adebisi, B., Rabie, K.M., Anoh, K., Ande, R.E., Hammoudeh, M., Gacanin, H., Mbanaso, U.M., Low-power wide area network technologies for Internet-of-Things: A comparative review (2018) IEEE Internet Things J, 6, pp. 2225-2240 | |
dc.relation.references | Sornin, N., Luis, M., Eirich, T., Kramp, T., Hersent, O., (2015) Lorawan Specification, pp. 6-77. , LoRa Alliance, Fremont, CA, USA | |
dc.relation.references | Mekki, K., Bajic, E., Chaxel, F., Meyer, F., A comparative study of LPWAN technologies for large-scale IoT deployment (2019) ICT Express, 5, pp. 1-7 | |
dc.relation.references | Wixted, A.J., Kinnaird, P., Larijani, H., Tait, A., Ahmadinia, A., Strachan, N., Evaluation of LoRa and LoRaWAN for wireless sensor networks Proceedings of the 2016 IEEE SENSORS, pp. 1-3. , Orlando, FL, USA, 30 October–3 November 2016 | |
dc.relation.references | Goldsmith, A., (2005) Wireless Communications, , Cambridge University Press, Cambridge, UK | |
dc.relation.references | Friis, H.T., A note on a simple transmission formula (1946) Proc. Ire, 34, pp. 254-256 | |
dc.relation.references | Okumura, Y., Field strength and its variability in VHF and UHF land-mobile radio service (1968) Rev. Electr. Commun. Lab, 16, pp. 825-873 | |
dc.relation.references | Rappaport, T.S., (1996) Wireless Communications: Principles and Practice, 2. , Prentice Hall PTR, Englewood Cliffs, NJ, USA | |
dc.relation.references | Jawad, H.M., Jawad, A.M., Nordin, R., Gharghan, S.K., Abdullah, N.F., Ismail, M., Abu-AlShaeer, M.J., Accurate empirical path-loss model based on particle swarm optimization for wireless sensor networks in smart agriculture (2019) IEEE Sens. J, 20, pp. 552-561 | |
dc.relation.references | Faraway, J.J., (2002) Practical Regression and ANOVA Using R, 168. , University of Bath, Bath, UK | |
dc.relation.references | Xu, W., Kim, J.Y., Huang, W., Kanhere, S.S., Jha, S.K., Hu, W., Measurement, characterization, and modeling of lora technology in multifloor buildings (2019) IEEE Internet Things J, 7, pp. 298-310 | |
dc.relation.references | Kim, D.H., Lee, E.K., Kim, J., Experiencing LoRa network establishment on a smart energy campus testbed (2019) Sustainability, 11 | |
dc.relation.references | El Chall, R., Lahoud, S., El Helou, M., LoRaWAN network: Radio propagation models and performance evaluation in various environments in Lebanon (2019) IEEE Internet Things J, 6, pp. 2366-2378 | |
dc.relation.references | Masek, P., Stusek, M., Svertoka, E., Pospisil, J., Burget, R., Lohan, E.S., Marghescu, I., Ometov, A., Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor (2021) Data, 6 | |
dc.relation.references | Bhatia, L., Breza, M., Marfievici, R., McCann, J.A., Dataset: Loed: The lorawan at the edge dataset (2020) arXiv, , 2010.14211 | |
dc.relation.references | Aernouts, M., Berkvens, R., Van Vlaenderen, K., Weyn, M., Sigfox and LoRaWAN datasets for fingerprint localization in large urban and rural areas (2018) Data, 3 | |
dc.relation.references | Li, Y., Barthelemy, J., Sun, S., Perez, P., Moran, B., Urban vehicle localization in public LoRaWan network (2021) IEEE Internet Things J, 9, pp. 10284-10293 | |
dc.relation.references | Anzum, R., Habaebi, M.H., Islam, M.R., Hakim, G.P., Khandaker, M.U., Osman, H., Alamri, S., AbdElrahim, E., A Multiwall Path-Loss Prediction Model Using 433 MHz LoRa-WAN Frequency to Characterize Foliage’s Influence in a Malaysian Palm Oil Plantation Environment (2022) Sensors, 22 | |
dc.relation.references | Alobaidy, H.A., Nordin, R., Singh, M.J., Abdullah, N.F., Haniz, A., Ishizu, K., Matsumura, T., Ramli, N., Low-Altitude-Platform-Based Airborne IoT Network (LAP-AIN) for Water Quality Monitoring in Harsh Tropical Environment (2022) IEEE Internet Things J, 9, pp. 20034-20054 | |
dc.relation.references | Batalha, I.S., Lopes, A.V., Lima, W.G., Barbosa, Y.H., Neto, M.C., Barros, F.J., Cavalcante, G.P., Large-Scale Modeling and Analysis of Uplink and Downlink Channels for LoRa Technology in Suburban Environments (2022) IEEE Internet Things J, 9, pp. 24477-24490 | |
dc.relation.references | Callebaut, G., Van der Perre, L., Characterization of LoRa point-to-point path loss: Measurement campaigns and modeling considering censored data (2019) IEEE Internet Things J, 7, pp. 1910-1918 | |
dc.relation.references | Bianco, G.M., Giuliano, R., Marrocco, G., Mazzenga, F., Mejia-Aguilar, A., LoRa system for search and rescue: Path-loss models and procedures in mountain scenarios (2020) IEEE Internet Things J, 8, pp. 1985-1999 | |
dc.relation.references | Deese, A.S., Jesson, J., Brennan, T., Hollain, S., Stefanacci, P., Driscoll, E., Dick, C., Rentsch, B., Long-term monitoring of smart city assets via Internet of Things and low-power wide-area networks (2020) IEEE Internet Things J, 8, pp. 222-231 | |
dc.relation.references | Fang, Z., Guerboukha, H., Shrestha, R., Hornbuckle, M., Amarasinghe, Y., Mittleman, D.M., Secure Communication Channels Using Atmosphere-limited Line-of-sight Terahertz Links (2022) IEEE Trans. Terahertz Sci. Technol, 12, pp. 363-369 | |
dc.relation.references | Union, I.T., Attenuation by atmospheric gases and related effects, ITUR P.676-13 (2022) Recommendation ITU-R, pp. 676-688. , International Telecommunication Union, Geneva, Switzerland | |
dc.relation.references | Min, N., Ren, J., Yang, G., Zhang, M.-L., Pei, C.-X., Influences of PM2. 5 atmospheric pollution on the performance of free space quantum communication (2015) Acta Phys. Sin, 64, p. 150301 | |
dc.relation.references | (2020) RP002-1.0.1 LoRaWAN® Regional Parameters, , LoRa Alliance, Inc., Fremont, CA, USA | |
dc.relation.references | Reynders, B., Pollin, S., Chirp spread spectrum as a modulation technique for long range communication Proceedings of the 2016 Symposium on Communications and Vehicular Technologies (SCVT), pp. 1-5. , Mons, Belgium, 22–23 November 2016 | |
dc.relation.references | Heusse, M., Attia, T., Caillouet, C., Rousseau, F., Duda, A., Capacity of a lorawan cell Proceedings of the 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 131-140. , Alicante, Spain, 16–20 November 2020 | |
dc.relation.references | Adaptative Data Rate, , https://www.thethingsnetwork.org/docs/lorawan/adaptive-data-rate/, Available online | |
dc.relation.references | Firouzi, F., Chakrabarty, K., Nassif, S., (2020) Intelligent Internet of Things: From Device to Fog and Cloud, , Springer, Berlin/Heidelberg, Germany | |
dc.relation.references | Abdelghany, A., Uguen, B., Moy, C., Lemur, D., On Superior Reliability of Effective Signal Power versus RSSI in LoRaWAN Proceedings of the 2021 28th International Conference on Telecommunications (ICT), pp. 1-5. , London, UK, 1–3 June 2021 | |
dc.relation.references | Yang, G., Zhang, Y., He, Z., Wen, J., Ji, Z., Li, Y., Machine-learning-based prediction methods for path loss and delay spread in air-to-ground millimetre-wave channels (2019) IET Microwaves Antennas Propag, 13, pp. 1113-1121 | |
dc.relation.references | De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L., The mahalanobis distance (2000) Chemom. Intell. Lab. Syst, 50, pp. 1-18 | |
dc.relation.references | Thanaki, J., (2018) Machine Learning Solutions: Expert Techniques to Tackle Complex Machine Learning Problems Using Python, , Packt Publishing Ltd., Birmingham, UK | |
dc.relation.references | Ohshima, K., Hara, H., Hagiwara, Y., Terada, M., Field experiments for developing transmission control based on weather estimation in an environmental wireless sensor network Proceedings of the 2010 Australasian Telecommunication Networks and Applications Conference, pp. 19-24. , Auckland, New Zealand, 31 October–3 November 2010 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | |
dc.identifier.reponame | reponame:Repositorio Institucional Universidad de Medellín | |
dc.identifier.repourl | repourl:https://repository.udem.edu.co/ | |
dc.identifier.instname | instname:Universidad de Medellín | |