Show simple item record

dc.contributor.authorGonzález-Palacio M
dc.contributor.authorSepúlveda-Cano L.M
dc.contributor.authorTobón-Vallejo D.P
dc.contributor.authorAzurdia-Meza C.
dc.date.accessioned2023-10-24T19:24:50Z
dc.date.available2023-10-24T19:24:50Z
dc.date.created2022
dc.identifier.issn1758728X
dc.identifier.urihttp://hdl.handle.net/11407/8007
dc.description.abstractPath loss models (PLMs) play a fundamental role in the deployment of wireless sensor networks (WSNs) since they support tasks related to positioning, tracking, and power control, making the end nodes coexist in complex scenarios. Such models are used on design to choose the transmission parameters, guaranteeing that the waves can be demodulated at the receiver. Different models have been proposed to quantify the path loss for general applications; however, WSNs exhibit constraints that make its application unsuitable regarding prediction accuracy. These constraints motivate proposing niche-specific PLMs that meet the WSNs' requirements. This paper identifies the trends in PLMs for WSNs. We have found the variables considered in the models, typical scenarios, reference models used to assess each approach, the most used frequency bands, specific modulations, and the research opportunities and open problems. This characterisation helps designers to establish the PLMs to achieve energy savings, accurate positioning, and reliable links. © 2022 Inderscience Enterprises Ltd.eng
dc.language.isoeng
dc.publisherInderscience Publishers
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161814922&doi=10.1504%2fIJUWBCS.2022.126795&partnerID=40&md5=05eb37b5ded327a0be9bb3d261f28db9
dc.sourceInt. J. Ultra Wideb Commun. Syst.
dc.sourceInternational Journal of Ultra Wideband Communications and Systemseng
dc.subjectEmpirical modelseng
dc.subjectInternet of thingseng
dc.subjectIoTeng
dc.subjectPath losseng
dc.subjectWireless sensor networkseng
dc.subjectWSNseng
dc.titleCharacterisation of path loss models in wireless sensor networks: scenarios, variables, and open problemseng
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.type.spaArtículo
dc.identifier.doi10.1504/IJUWBCS.2022.126795
dc.relation.citationvolume5
dc.relation.citationissue3
dc.relation.citationstartpage164
dc.relation.citationendpage188
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationGonzález-Palacio, M.
dc.affiliationSepúlveda-Cano, L.M., Department of Accountancy, Universidad EAFIT, Carrera 49 # 7 Sur - 50, Medellín, Colombia
dc.affiliationTobón-Vallejo, D.P., Department of Telecommunications Engineering, Universidad de Medellín, Carrera 87 #30-65, Medellín, Colombia
dc.affiliationAzurdia-Meza, C., Department of Electric Engineering, Universidad de Chile, Av. Libertador Bernardo O'Higgins 1058, Región Metropolitana, Santiago, Chile
dc.relation.referencesAbdel-Rahim, M., Hadi Habaebi, M., Chebil, J., Hashim, A.H.A., Ahmed, M.M., Rafiqul Islam, M., Zyoud, A., An indoor path loss model for wireless sensor networks (2018) International Journal of Ultra Wideband Communications and Systems, 30 (1), pp. 36-51
dc.relation.referencesAbo-Zahhad, M., Sabor, N., Sasaki, S., Ahmed, S.M., A centralized immune-Voronoi deployment algorithm for coverage maximization and energy conservation in mobile wireless sensor networks (2016) Information Fusion, 30 (1), pp. 36-51
dc.relation.referencesAkerberg, D., Properties of a TDMA pico cellular office communication system (1989) IEEE 39th Vehicular Technology Conference, pp. 186-191
dc.relation.referencesAkkasli, C., (2009) Methods for Path Loss Prediction, , Växjö University
dc.relation.referencesAldossari, S.M., Chen, K-C., Machine learning for wireless communication channel modeling: an overview (2019) Wireless Personal Communications, 106 (1), pp. 41-70
dc.relation.referencesAl-Samman, A.M., Rahman, T.A., Al-Hadhrami, T., Daho, A., Hindia, M.H.D.N., Azmi, M.H., Dimyati, K., Alazab, M., Comparative study of indoor propagation model below and above 6 GHZ for 5G wireless networks (2019) Electronics, 8 (1). , https://doi.org/10.3390/electronics8010044, Switzerland
dc.relation.referencesAlSayyari, A., Kostanic, I., Otero, C.E., An empirical path loss model for wireless sensor network deployment in an artificial turf environment (2014) C3 - Proceedings of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014
dc.relation.referencesAlSayyari, A., Kostanic, I., Otero, C., Almeer, M., Rukieh, K., An empirical path loss model for wireless sensor network deployment in a sand terrain environment (2014) C3 -2014 IEEE World Forum on Internet of Things, , WF-IoT 2014
dc.relation.referencesAlSayyari, A., Kostanic, I., Otero, C.E., An empirical path loss model for wireless sensor network deployment in a concrete surface environment (2015) C3 - 2015 IEEE 16th Annual Wireless and Microwave Technology Conference, WAMICON 2015
dc.relation.referencesAlsayyari, A., Kostanic, I., Otero, C.E., Aldosary, A., An empirical path loss model for wireless sensor network deployment in a dense tree environment (2017) C3 - SAS 2017 -2017 IEEE Sensors Applications Symposium, Proceedings
dc.relation.referencesAl-Turjman, F., Radwan, A., Mumtaz, S., Rodriguez, J., Mobile traffic modelling for wireless multimedia sensor networks in IoT (2017) Computer Communications, 112, pp. 109-115. , https://doi.org/10.1016/j.comcom.2017.08.017
dc.relation.referencesAl-Zahrani, A.Y., Optimal 3-d placement of an aerial base station in a heterogeneous wireless IoT with Nakagami-m fading channels (2020) Ad-Hoc and Sensor Wireless Networks
dc.relation.referencesAmmari, H.M., Das, S.K., Trade-off between energy savings and source-to-sink delay in data dissemination for wireless sensor networks (2005) Proceedings of the 8th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 126-133
dc.relation.referencesAndres-Maldonado, P., Lauridsen, M., Ameigeiras, P., Lopez-Soler, J.M., Analytical modeling and experimental validation of NB-IoT device energy consumption (2019) IEEE Internet of Things Journal, 6 (3), pp. 5691-5701. , https://doi.org/10.1109/jiot.2019.2904802
dc.relation.referencesArya, R., Sharma, S.C., Optimization approach for energy minimization and bandwidth estimation of WSN for data centric protocols (2018) International Journal of System Assurance Engineering and Management, 9 (1), pp. 2-11
dc.relation.referencesBalachander, D., Rao, T.R., Mahesh, G., RF propagation investigations in agricultural fields and gardens for wireless sensor communications (2013) 2013 IEEE Conference on Information & Communication Technologies, pp. 755-759
dc.relation.referencesBoano, C.A., Brown, J., He, Z., Roedig, U., Voigt, T., Low-power radio communication in industrial outdoor deployments: the impact of weather conditions and ATEX-compliance (2009) International Conference on Sensor Applications, Experimentation and Logistics, pp. 159-176
dc.relation.referencesBoano, C.A., Brown, J., He, Z., Roedig, U., Voigt, T., Low-power radio communication in industrial outdoor deployments: the impact of weather conditions and ATEX-compliance (2010) C3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering
dc.relation.referencesBotchkarev, A., (2018) Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology, , ArXiv Preprint ArXiv:1809.03006
dc.relation.referencesBousquet, N., Diagnostics of prior-data agreement in applied Bayesian analysis (2008) Journal of Applied Statistics, 35 (9), pp. 1011-1029
dc.relation.referencesCaso, G., Alay, Ö., De Nardis, L., Brunstrom, A., Neri, M., Di Benedetto, M-G., Empirical models for NB-IoT path loss in an urban scenario (2021) IEEE Internet of Things Journal, 8 (17), pp. 13774-13788
dc.relation.referencesCattani, M., Boano, C.A., Römer, K., An experimental evaluation of the reliability of lora long-range low-power wireless communication (2017) Journal of Sensor and Actuator Networks, 6 (2), p. 7
dc.relation.referencesCheffena, M., Mohamed, M., Empirical path loss models for wireless sensor network deployment in snowy environments (2017) IEEE Antennas and Wireless Propagation Letters, 16 (1), pp. 2877-2880
dc.relation.referencesChen, J., He, S., Sun, Y., Thulasiraman, P., Shen, X.S., Optimal flow control for utility-lifetime tradeoff in wireless sensor networks (2009) Computer Networks, 53 (18), pp. 3031-3041
dc.relation.referencesChoi, W, Chang, Y.S., Jung, Y., Song, J., Low-power LORa signal-based outdoor positioning using fingerprint algorithm (2018) ISPRS International Journal of Geo-Information, 7 (11). , https://doi.org/10.3390/ijgi7110440
dc.relation.referencesChoi, W., Das, S.K., A novel framework for energy-conserving data gathering in wireless sensor networks (2005) Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, 3, pp. 1985-1996
dc.relation.referencesChong, P.K., Kim, D., Surface-level path loss modeling for sensor networks in flat and irregular terrain (2013) ACM Transactions on Sensor Networks, 9 (2), pp. 15:1-15:32
dc.relation.referencesChong, P.K., Yoo, S.E., Kim, S.H., Kim, D., Wind-blown foliage and human-induced fading in ground-surface narrowband communications at 400 MHz (2011) IEEE Transactions on Vehicular Technology, 60 (4), pp. 1326-1336
dc.relation.referencesCost, E.C., (1996) Radiowave Propagation Effects on Next Generation Fixed-Services Terrestrial Telecommunications Systems, pp. 159-168. , 235. Final Report EUR 16992 EN
dc.relation.referencesCzerwinski, D., Przylucki, S., Wojcicki, P., Sitkiewicz, J., Path loss model for a wireless sensor network in different weather conditions (2017) C3 - Communications in Computer and Information Science, 718 (1), pp. 106-117
dc.relation.referencesDahnil, D.P., Selamat, S., Abu Bakar, K.A., Hassan, R., Ismail, A.G., A new method for battery lifetime estimation using experimental testbed for Zigbee wireless technology (2018) International Journal on Advanced Science, Engineering and Information Technology, 8 (6), pp. 2654-2662
dc.relation.referencesDamosso, E., Correia, L.M., (1991) Urban transmission loss models for mobile radio in the 900 and 1800 Mhz bands, , The Hague, September
dc.relation.referencesDargie, W., Poellabauer, C., (2010) Fundamentals of Wireless Sensor Networks: Theory and Practice, , John Wiley & Sons, Dresden, Germany
dc.relation.referencesDeese, 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 of Things Journal, 8 (1), pp. 222-231
dc.relation.referencesDevarajan, N., Gupta, S.H., Implementation and analysis of different path loss models for cooperative communication in a wireless sensor network (2019) C3 - Advances in Intelligent Systems and Computing, 851 (1), pp. 227-236
dc.relation.referencesEl Chall, R., Lahoud, S., El Helou, M., LoRaWAN network: radio propagation models and performance evaluation in various environments in Lebanon (2019) IEEE Internet of Things Journal, 6 (2), pp. 2366-2378. , https://doi.org/10.1109/JIOT.2019.2906838
dc.relation.referencesFaruk, N., Abdulrasheed, I.Y., Surajudeen-Bakinde, N.T., Adetiba, E., Oloyede, A.A., Abdulkarim, A., Sowande, O., Atayero, A.A., Large-scale radio propagation path loss measurements and predictions in the VHF and UHF bands (2021) Heliyon, 7 (6), pp. 1-15
dc.relation.referencesFriis, H.T., A note on a simple transmission formula (1946) Proceedings of the IRE, 34 (5), pp. 254-256
dc.relation.referencesGhani, A., Naqvi, S.H.A., Ilyas, M.U., Khan, M.K., Hassan, A., Energy efficiency in multipath Rayleigh faded wireless sensor networks using collaborative communication (2019) IEEE Access, 7, pp. 26558-26570. , https://doi.org/10.1109/access.2019.2898565
dc.relation.referencesGharghan, S.K., Nordin, R., Ismail, M., Energy efficiency of ultra-low-power bicycle wireless sensor networks based on a combination of power reduction techniques (2016) Journal of Sensors, , https://doi.org/10.1155/2016/7314207
dc.relation.referencesGoldsmith, A., (2005) Wireless Communications, , Cambridge University Press, Cambridge
dc.relation.referencesGong, X., Plets, D., Tanghe, E., De Pessemier, T., Martens, L., Joseph, W., An efficient genetic algorithm for large-scale planning of dense and robust industrial wireless networks (2018) Expert Systems with Applications, 96, pp. 311-329. , https://doi.org/10.1016/j.eswa.2017.12.011
dc.relation.referencesGong, X., Plets, D., Tanghe, E., De Pessemier, T., Martens, L., Joseph, W., An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments (2018) Applied Soft Computing Journal, 65, pp. 243-259. , https://doi.org/10.1016/j.asoc.2018.01.016
dc.relation.referencesGonzález-Palacio, M., Sepúlveda-Cano, L., Montoya, R., Rocha, Á., Ferrás, C., López-López, P.C., Guarda, T., Simplified path loss lognormal shadow fading model versus a support vector machine-based regressor comparison for determining reception powers in WLAN networks (2021) Information Technology and Systems. ICITS, 2021, pp. 431-441
dc.relation.referencesGonzález-Palacio, M., Sepúlveda-Cano, L., Valencia, J., D'Amato, J., Quiza-Montealegre, J., Palacio, L.G., System dynamics baseline model for determining a multivariable objective function optimization in wireless sensor networks (2020) 2020 15th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1-6
dc.relation.referencesGuidara, A., Fersi, G., Derbel, F., Jemaa, M.B., Impacts of temperature and humidity variations on RSSI in indoor wireless sensor networks (2018) C3 - Procedia Computer Science, Procedia Computer Science. International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2018, pp. 1072-1081
dc.relation.referencesHabib, S.J., Marimuthu, P.N., Development of trustworthy self-adaptive framework for wireless sensor networks (2020) C3 - Advances in Intelligent Systems and Computing, 8th World Conference on Information Systems and Technologies, WorldCIST 2020, pp. 368-378
dc.relation.referencesHao, X., Liu, W., Yao, N., Geng, D., Li, X., Distributed topology construction algorithm to improve link quality and energy efficiency for wireless sensor networks (2016) Journal of Network and Computer Applications, 72, pp. 162-170. , https://doi.org/10.1016/j.jnca.2016.04.017
dc.relation.referencesHarun, A., Ndzi, D.L., Ramli, M.F., Shakaff, A.Y.M., Ahmad, M.N., Kamarudin, L.M., Zakaria, A., Yang, Y., Signal propagation in aquaculture environment for wireless sensor network applications (2012) Progress in Electromagnetics Research, 131 (1), pp. 477-494
dc.relation.referencesHasan, M.Z., Al-Turjman, F., Al-Rizzo, H., Analysis of cross-layer design of quality-of-service forward geographic wireless sensor network routing strategies in green internet of things (2018) IEEE Access, 6, pp. 20371-20389. , https://doi.org/10.1109/ACCESS.2018.2822551
dc.relation.referencesHata, M., Empirical formula for propagation loss in land mobile radio services (1980) IEEE Transactions on Vehicular Technology, 29 (3), pp. 317-325
dc.relation.referencesHattab, G., El-Tarhuni, M., Al-Ali, M., Joudeh, T., Qaddoumi, N., An underwater wireless sensor network with realistic radio frequency path loss model (2013) International Journal of Distributed Sensor Networks, 2013 (1), pp. 1-9
dc.relation.referencesHebel, M.A., Tate, R., Watson, D.G., Results of wireless sensor network transceiver testing for agricultural applications (2007) C3 - 2007 ASABE Annual International Meeting, , Technical Papers
dc.relation.referencesHeinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H., An application-specific protocol architecture for wireless microsensor networks (2002) IEEE Transactions on Wireless Communications, 1 (4), pp. 660-670
dc.relation.referencesHejselbaek, J., Nielsen, J.O., Fan, W., Pedersen, G.F., Empirical study of near ground propagation in forest terrain for internet-of-things type device-to-device communication (2018) IEEE Access, 6 (1), pp. 54052-54063
dc.relation.referencesHejselbaek, J., Nielsen, J.O., Fan, W., Pedersen, G.F., Empirical study of near ground propagation in forest terrain for internet-of-things type device-to-device communication (2018) IEEE Access, 6, pp. 54052-54063. , https://doi.org/10.1109/ACCESS.2018.2871368
dc.relation.referencesHernández, E., Sanchez-Anguix, V., Julian, V., Palanca, J., Duque, N., Rainfall prediction: a deep learning approach (2016) International Conference on Hybrid Artificial Intelligence Systems, pp. 151-162
dc.relation.referencesHosseinzadeh, S., Almoathen, M., Larijani, H., Curtis, K., A neural network propagation model for LoRaWAN and critical analysis with real-world measurements (2017) Big Data and Cognitive Computing, 1 (1), p. 7
dc.relation.referencesIkpehai, 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 of Things Journal, 6 (2), pp. 2225-2240
dc.relation.referencesInfrastructure, L-E.C., Layer, M.L.P., IEEE standard for low-rate wireless networks (2015) IEEE Standards, 2015 (1), pp. 1-708
dc.relation.referencesJafari, H., Nazari, M., Shamshirband, S., Optimization of energy consumption in wireless sensor networks using density-based clustering algorithm (2018) International Journal of Computers and Applications, pp. 1-10
dc.relation.referencesJawad, 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 (2020) IEEE Sensors Journal, 20 (1), pp. 552-561
dc.relation.referencesJeftenic, N., Simic, M., Stamenkovic, Z., Impact of environmental parameters on SNR and RSS in LoRaWAN (2020) C3 - 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020
dc.relation.referencesJia, C., Cai, Y., Yu, Y.T., Tse, T.H., 5W+ 1H pattern: a perspective of systematic mapping studies and a case study on cloud software testing (2016) Journal of Systems and Software, 116 (1), pp. 206-219
dc.relation.referencesJian, X., Wei, Y., Liu, Y., Song, J., Zeng, X., Tan, X., Power consumption modeling and optimization for NB-IoT eDRX (2019) Tongxin Xuebao/Journal on Communications, 40 (4), pp. 107-116. , https://doi.org/10.11959/j.issn.1000-436x.2019094
dc.relation.referencesJiang, A., Zheng, L., An effective hybrid routing algorithm in WSN: ant colony optimization in combination with hop count minimization (2018) Sensors, 18 (4), p. 1020
dc.relation.referencesJiang, X., Yang, Y., Wang, X., Zhang, H., Experimental research of path loss models for zigbee wireless sensor networks (2010) C3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA), pp. 1429-1433
dc.relation.referencesKamarudin, L.M., Ahmad, R.B., Ong, B.L., Malek, F., Zakaria, A., Arif, M.A.M., Review and modeling of vegetation propagation model for wireless sensor networks using omnet++ (2010) 2010 Second International Conference on Network Applications, Protocols and Services, pp. 78-83
dc.relation.referencesKamga, G.N., Aissa, S., Wireless power transfer in mmWave massive MIMO systems with/without rain attenuation (2019) IEEE Transactions on Communications, 67 (1), pp. 176-189
dc.relation.referencesKarl, H., Willig, A., (2007) Protocols and Architectures for Wireless Sensor Networks, , John Wiley & Sons, Berlin
dc.relation.referencesKhalid, N., Abbasi, N.A., Akan, O.B., Statistical characterization and analysis of low-THz communication channel for 5G internet of things (2019) Nano Communication Networks, 22. , https://doi.org/10.1016/j.nancom.2019.100258
dc.relation.referencesKim, D.H., Lee, E.K., Kim, J., Experiencing LoRa network establishment on a smart energy campus testbed (2019) Sustainability, 11 (7). , https://doi.org/10.3390/su11071917, Switzerland
dc.relation.referencesKim, J., Choi, Y., Jeong, J., Lee, S., Kim, S., The v2. 0+ EDR Bluetooth SOC architecture for multimedia (2006) IEEE Transactions on Consumer Electronics, 52 (2), pp. 436-444
dc.relation.referencesKlaina, H., Alejos, A.V, Aghzout, O., Falcone, F., Narrowband characterization of near-ground radio channel for wireless sensors networks at 5G-IoT bands (2018) Sensors, 18 (8). , https://doi.org/10.3390/s18082428, Switzerland
dc.relation.referencesKonstantinidis, A., Yang, K., Zhang, Q., Zeinalipour-Yazti, D., A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks (2010) Computer Networks, 54 (6), pp. 960-976
dc.relation.referencesKullback, S., Leibler, R.A., On information and sufficiency (1951) The Annals of Mathematical Statistics, 22 (1), pp. 79-86
dc.relation.referencesKumar, S., Gautam, P.R., Verma, A., Rashid, T., Kumar, A., An energy-efficient transmission in WSNs for different climatic conditions (2020) Wireless Personal Communications, 110 (1), pp. 423-444
dc.relation.referencesLee, J., Prediction-based energy saving mechanism in 3GPP NB-IoT networks (2017) Sensors, 17 (9). , https://doi.org/10.3390/s17092008, Switzerland
dc.relation.referencesLi, S., Gao, H., Propagation characteristics of 2.4GHz wireless channel in cornfields (2011) C3 - International Conference on Communication Technology Proceedings, ICCT, pp. 136-140
dc.relation.referencesLi, Z., Wang, N., Hong, T., Franzen, A., Experimental path-loss models for 2.4 GHz in-field wireless sensor network (2010) American Society of Agricultural and Biological Engineers Annual International Meeting 2010, ASABE 2010
dc.relation.references(2020) RP002-1.0.1 LoRaWAN® Regional Parameters, , https://lora-alliance.org/sites/default/files/2020-06/rp_2-1.0.1.pdf, [online] (accessed 12 January 2021)
dc.relation.referencesLu, J., Wang, X., Zhang, L., Zhao, X., Fuzzy random multi-objective optimization based routing for wireless sensor networks (2014) Soft Computing, 18 (5), pp. 981-994
dc.relation.referencesLuomala, J., Hakala, I., Effects of temperature and humidity on radio signal strength in outdoor wireless sensor networks (2015) 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1247-1255
dc.relation.referencesManfredi, S., Di Tucci, E., Decentralized control algorithm for fast monitoring and efficient energy consumption in energy harvesting wireless sensor networks (2017) IEEE Transactions on Industrial Informatics, 13 (4), pp. 1513-1520. , https://doi.org/10.1109/tii.2016.2627478
dc.relation.referencesMarkham, A., Trigoni, N., Ellwood, S., Effect of rainfall on link quality in an outdoor forest deployment', C3 -WINSYS 2010 - Proceedings of the International Conference on Wireless Information Networks and Systems, pp.148-153 Massey Jr., F.J. (1951) 'The Kolmogorov-Smirnov test for goodness of fit (2010) Journal of the American Statistical Association, 46 (253), pp. 68-78
dc.relation.referencesMcCune, E., Feher, K., Closed-form propagation model combining one or more propagation constant segments (1997) 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion, 2, pp. 1108-1112
dc.relation.referencesMolina, G., Alba, E., Talbi, E-G., Optimal sensor network layout using multi-objective metaheuristics (2008) J. UCS, 14 (15), pp. 2549-2565
dc.relation.referencesMysorewala, M., Time and energy savings in leak detection in WSN-based water pipelines: a novel parametric optimization-based approach (2019) Water Resources Management, 33 (6), pp. 2057-2071
dc.relation.referencesNekrasov, M., Allen, R., Belding, E., Performance analysis of aerial data collection from outdoor IoT sensor networks using 2.4GHz 802.15.4 (2019) C3 - DroNet 2019 -Proceedings of the 5th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, co-located with MobiSys 2019, pp. 33-38
dc.relation.referencesNekrasov, M., Ginier, M., Allen, R., Artamonova, I., Belding, E., Impact of 802.15.4 radio antenna orientation on UAS aerial data collection (2020) C3 - Proceedings - International Conference on Computer Communications and Networks, ICCCN
dc.relation.referencesNie, M., 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 Physica Sinica, 64 (15), p. 150301
dc.relation.referencesOhshima, K., Hara, H., Hagiwara, Y., Terada, M., Field experiments for developing transmission control based on weather estimation in an environmental wireless sensor network (2010) C3 - 2010 Australasian Telecommunication Networks and Applications Conference, ATNAC 2010, pp. 19-24
dc.relation.referencesOhshima, K., Hara, H., Hagiwara, Y., Terada, M., Field investigation of the radio transmission performance and distance in a environmental wireless sensor network', International Conference on Information Networking, pp.132-137 Okumura, Y. (1968) 'Field strength and its variability in VHF and UHF land-mobile radio service (2012) Rev. Electr. Commun. Lab, 16 (1), pp. 825-873
dc.relation.referencesOlasupo, T.O., Otero, C.E., Olasupo, K.O., Kostanic, I., Empirical path loss models for wireless sensor network deployments in short and tall natural grass environments (2016) IEEE Transactions on Antennas and Propagation, 64 (9), pp. 4012-4021
dc.relation.referencesOraibi, I., Otero, C.E., Olasupo, T.O., Empirical path loss model for vehicle-to-vehicle IoT device communication in fleet management (2017) C3 - 2017 16th Annual Mediterranean Ad Hoc Networking Workshop, Med-Hoc-Net 2017
dc.relation.referencesOrlóci, L., An agglomerative method for classification of plant communities (1967) The Journal of Ecology, 55 (1), pp. 193-206
dc.relation.referencesPerez-Vega, C., Garcia, J.L.G., A simple approach to a statistical path loss model for indoor communications (1997) 1997 27th European Microwave Conference, 1, pp. 617-623
dc.relation.referencesPinky, Pandey, A., Kumar, S., Smart device localization using femtocell and macro base station based path loss models in IoT networks (2018) C3 - International Symposium on Advanced Networks and Telecommunication Systems, ANTS, , https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066020107&doi=10.1109%2FANTS.2018.8710150&partnerID=40&md5=348a5d49ebb2c07240401479d93a6138, [online]
dc.relation.referencesPriya, R.K., Venkatanarayanan, S., Implementation of thermal aware wireless sensor network clustering algorithm based on fuzzy and spider optimized cluster head selection (2020) Journal of Ambient Intelligence and Humanized Computing, 12 (5), pp. 5245-5255
dc.relation.referencesQamar, F., Hindia, N., Dimyati, K., Noordin, K.A., Majed, M.B., Rahman, T.A., Amiri, I.S., Investigation of future 5g-iot millimeter-wave network performance at 38 GHz for urban microcell outdoor environment (2019) Electronics, 8 (5). , https://doi.org/10.3390/electronics8050495, Switzerland
dc.relation.referencesRaheemah, A., Sabri, N., Salim, M.S., Ehkan, P., Ahmad, R.B., New empirical path loss model for wireless sensor networks in mango greenhouses (2016) Computers and Electronics in Agriculture, 127 (1), pp. 553-560
dc.relation.referencesRameesh, M.V, Rajan, P., Divya, P., Augmenting QoS in outdoor wireless sensor networks through frequency optimization (2015) Proceedings - 7th International Conference on Computational Intelligence, Communication Systems and Networks, CICSyN 2015, pp. 39-44
dc.relation.referencesRamesh, M.V, Rajan, P., Divya, P., Augmenting packet delivery rate in outdoor wireless sensor networks through frequency optimization (2014) C3 - 5th International Conference on Computing Communication and Networking Technologies, ICCCNT 2014
dc.relation.referencesRani, K.S.S., Devarajan, N., Multiobjective sensor node deployment in wireless sensor networks (2012) International Journal of Engineering Science and Technology, 4 (4), pp. 1262-1266
dc.relation.referencesRappaport, T.S., (1996) Wireless Communications: Principles and Practice, 2. , Prentice Hall PTR, New Jersey
dc.relation.referencesRappaport, T.S., Mobile radio propagation: large-scale path loss (2002) Wireless Communications: Principles and Practice, , New York
dc.relation.referencesRasool, I., Salman, N., Kemp, A.H., RSSI-based positioning in unknown path-loss model for WSN (2012) IET Seminar Digest
dc.relation.referencesRatasuk, R., Vejlgaard, B., Mangalvedhe, N., Ghosh, A., NB-IoT system for M2M communication (2016) 2016 IEEE Wireless Communications and Networking Conference, pp. 1-5
dc.relation.referencesRave, J.P., (2013) Revisión sistemática de literatura en Ingeniería como apoyo a la Consultoría basada en Investigación, 17 (66). , Universidad Ciencia y Tecnología
dc.relation.referencesRecommendation, I-R., Guidelines for evaluation of radio transmission technologies for IMT-2000 (1997) Rec. ITU-R M, p. 1225
dc.relation.referencesRoyston, P., Approximating the Shapiro-Wilk W-test for non-normality (1992) Statistics and Computing, 2 (3), pp. 117-119
dc.relation.referencesRuiz-Zea, C.A., Osorno, C.A., Vallejo, M., Path loss model for indoor parking environments in a wireless sensor network (2016) 2016 IEEE Colombian Conference on Communications and Computing, COLCOM 2016
dc.relation.referencesSandoval, R.M., Garcia-Sanchez, A.J., Garcia-Haro, J., Improving RSSI-based path-loss models accuracy for critical infrastructures: a smart grid substation case-study (2018) IEEE Transactions on Industrial Informatics, 14 (5), pp. 2230-2240. , https://doi.org/10.1109/TII.2017.2774838
dc.relation.referencesSandoval, R.M., Rodenas-Herraiz, D., Garcia-Sanchez, A-J., Garcia-Haro, J., Deriving and updating optimal transmission configurations for Lora networks (2020) IEEE Access, 8, pp. 38586-38595
dc.relation.referencesSawant, R.P., Liang, Q., Popa, D.O., Lewis, F.L., Experimental path loss models for wireless sensor networks (2007) C3 - Proceedings - IEEE Military Communications Conference MILCOM
dc.relation.referencesSegun, A.A., Olusope, A.M., Kofoworola, A.H., Influence of air temperature, relative humidity and atmospheric moisture on UHF radio propagation in South Western Nigeria (2015) Int. J. of Sci. and Research, 4 (8), pp. 588-592
dc.relation.referencesSeybold, J.S., (2005) Introduction to RF propagation, , John Wiley & Sons, New Jersey
dc.relation.referencesShekh, N.A., Dviwedi, V., Pabari, J.P., Effect of sandstorm on radio propagation model of Mars (2020) International Conference on Mobile Computing and Sustainable Informatics, pp. 441-447
dc.relation.referencesSingh, K., Singh, K., Aziz, A., Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm (2018) Computer Networks, 138 (1), pp. 90-107
dc.relation.referencesSingh, Y., Comparison of Okumura, Hata and cost-231 models on the basis of path loss and signal strength (2012) International Journal of Computer Applications, 59 (11), pp. 37-41
dc.relation.referencesSornin, N., Luis, M., Eirich, T., Kramp, T., Hersent, O., (2015) Lorawan Specification, , LoRa Alliance New York
dc.relation.referencesSun, G., Liu, Y., Liang, S., Chen, Z., Wang, A., Ju, Q., Zhang, Y., A sidelobe and energy optimization array node selection algorithm for collaborative beamforming in wireless sensor networks (2017) IEEE Access, 6, pp. 2515-2530. , https://doi.org/10.1109/access.2017.2783969
dc.relation.referencesTahir, M., Javaid, N., Iqbal, A., Khan, Z.A., Alrajeh, N., On adaptive energy-efficient transmission in WSNs (2013) International Journal of Distributed Sensor Networks, 9 (5), p. 923714
dc.relation.referencesTaib Miskon, M., Ismael Rizman, Z., Mohd Fauzi, F.D.H., Shahran Ibrahim, A., Mat Zain, M.Y., Uyun Ahmad, N., Rabi'ah Husin, N.H., Test bed implementation of IEEE 802.15.4 WSN for outdoor environment (2013) World Applied Sciences Journal, 23 (23), pp. 109-114
dc.relation.referencesTang, W., Ma, X., Wei, J., Wang, Z., Measurement and analysis of near-ground propagation models under different terrains for wireless sensor networks (2019) Sensors, 19 (8), p. 1901
dc.relation.references(2020) LoRaWAN Frequency Plans and Regulations by Country, , https://www.thethingsnetwork.org/docs/lorawan/frequenciesby-country.html, The-Things-Network [online] (accessed 1 December 2021)
dc.relation.referencesTorabi, A., Zekavat, S.A., Near-ground channel modeling for distributed cooperative communications (2016) IEEE Transactions on Antennas and Propagation, 64 (6), pp. 2494-2502
dc.relation.referencesWaldman, D.M., A note on algebraic equivalence of White's test and a variation of the Godfrey/Breusch-Pagan test for heteroscedasticity (1983) Economics Letters, 13 (2-3), pp. 197-200. , Nos
dc.relation.referencesWang, H., Yu, F.R., Jiang, H., Modeling of radio channels with leaky coaxial cable for LTE-M based CBTC systems (2016) IEEE Communications Letters, 20 (5), pp. 1038-1041
dc.relation.referencesWang, Y., Lu, W.J., Zhu, H.B., An empirical path-loss model for wireless channels in indoor short-range office environment (2012) International Journal of Antennas and Propagation, , https://doi.org/10.1155/2012/636349
dc.relation.referencesWhite, K.J., The Durbin-Watson test for autocorrelation in nonlinear models (1992) The Review of Economics and Statistics, 74 (2), pp. 370-373
dc.relation.referencesWu, W., Xiong, N., Wu, C., Improved clustering algorithm based on energy consumption in wireless sensor networks (2017) IET Networks, 6 (3), pp. 1-7. , https://doi.org/10.1049/iet-net.2016.0115
dc.relation.referencesXiuling, W., Wenjing, F., Wensi, W., Ligang, H., Yuanpu, L., Binglong, L., Technical analysis on the node chip of low-power consumption wide area internet of things based on NB-IoT (2017) Boletin Tecnico/Technical Bulletin, 55 (17), pp. 231-237
dc.relation.referencesXu, B., Xu, S.Z., Wang, Q., Chen, Z.H., Attenuation model of antenna signal with barriers in wireless sensor network (2013) International Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT 2013, 380, pp. 3908-3911
dc.relation.referencesXu, X., Zhang, Z., Xu, Y., Yang, Z., Chen, Y., Liang, Z., Zhou, J., Zheng, J., Measurement and analysis of wireless propagative model of 433MHz and 2.4GHz frequency in Southern China Orchards (2018) IFAC-PapersOnLine, 51 (17), pp. 695-699
dc.relation.referencesYun, Z., Iskander, M.F., Ray tracing for radio propagation modeling: principles and applications (2015) IEEE Access, 3 (1), pp. 1089-1100
dc.relation.referencesZhang, R.B., Guo, J.G., Chu, F.H., Zhang, Y.C., Environmental-adaptive indoor radio path loss model for wireless sensor networks localization (2011) AEU - International Journal of Electronics and Communications, 65 (12), pp. 1023-1031
dc.relation.referencesZhu, J., Hung, K-L., Bensaou, B., Nait-Abdesselam, F., Rate-lifetime tradeoff for reliable communication in wireless sensor networks (2008) Computer Networks, 52 (1), pp. 25-43
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record