Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-06T04:21:37.509Z Has data issue: false hasContentIssue false
  • English
  • Français

Empirical path loss models for 5G wireless sensor network in coastal pebble/sand environments

Published online by Cambridge University Press:  24 November 2021

Ibrahim Bahadir Basyigit Open the ORCID record for Ibrahim Bahadir Basyigit [Opens in a new window]
Ibrahim Bahadir Basyigit*
Affiliation:
Department of Electrical and Electronics Engineering, Isparta University of Applied Sciences, Isparta 32260, Turkey
*
Author for correspondence: Ibrahim Bahadir Basyigit, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Propagation modeling of small/big pebbles and air-dry/wet sand environments for wireless sensor networks has not been extensively studied in the 5G frequency band. This study is necessary for the proper coverage planning and efficient operation of wireless sensors in various applications such as monitoring summer sporting activities, and environmental/ground surveillance on coastal pebble/sand environments, or tracking pebble mobility and including the rescue of the flood-type avalanche in Gravel-Bed Rivers. In this study, empirical path loss models are proposed for wireless sensor networks in pebble/sand environments at two discrete frequencies, namely 3.5 and 4.2 GHz. The theoretical models and proposed models are compared to indicate the accuracy of proposed models in predicting the path loss in these environments. Additionally, R-squared and RMSE values of eight different generated models are calculated in the range of 0.931–0.877 and 2.284–2.837, respectively. These comparisons indicate that empirical model parameters have a significant effect on the path loss model.

Keywords

path loss modelRF propagation5Gpebble/sand terrainscoastal environmentWireless Sensor Network (WSN)
Type
Microwave Measurements
Information
International Journal of Microwave and Wireless Technologies , Volume 14 , Issue 9 , November 2022 , pp. 1222 - 1231
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press in association with the European Microwave Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Lynggaard, P and Skouby, KE (2015) Deploying 5G-technologies in smart city and smart home wireless sensor networks with interferences. Wireless Personal Communications 81, 1399–1413.CrossRefGoogle Scholar
Martínez, ISH, Salcedo, IPOJ and Daza, IBSR (2017) IoT application of WSN on 5G infrastructure. International Symposium on Networks, Computers and Communications (ISNCC), Marrakech, Morocco, 16–18 May 2017, pp. 1–6.CrossRefGoogle Scholar
Christin, D, Reinhardt, A, Mogre, PS and Steinmetz, R (2009) Wireless sensor networks and the internet of things: selected challenges. Proc. of the 8th GI/ITG KuVS Fachgespräch Drahtlose sensornetze, Berlin, Germany, pp. 31–34.Google Scholar
Iskandar, WV, Hendrawan, T and Arifianto, MS (2018) Wireless sensor network on 5G network. 4th International Conference on Wireless and Telematics (ICWT), Nusa Dua, Indonesia, 12–13 July 2018, pp. 1–5.Google Scholar
Li, Q, Zhang, H, Lu, Y, Zheng, T and Lv, Y (2019) A new method for path-loss modeling. International Journal of Microwave and Wireless Technologies 11, 739–746.CrossRefGoogle Scholar
Kurnaz, O and Helhel, S (2014) Near ground propagation model for pine tree forest environment. AEU – International Journal of Electronics and Communications 68, 944–950.CrossRefGoogle Scholar
Leonor, NR, Caldeirinha, RFS, Fernandes, TR, Ferreira, D and Sánchez, MG (2014) A 2D ray-tracing based model for micro- and millimetre-wave propagation through vegetation. IEEE Transactions on Antennas and Propagation 62, 6443–6453.CrossRefGoogle Scholar
Gay-Fernández, JA and Cuiñas, I (2014) Short-term modeling in vegetation media at wireless network frequency bands. IEEE Transactions on Antennas and Propagation 62, 3330–3337.CrossRefGoogle Scholar
Gay-Fernández, AJA and Cuiñas, I (2013) Peer to peer wireless propagation measurements and path-loss modeling in vegetated environments. IEEE Transactions on Antennas and Propagation 61, 3302–3311.CrossRefGoogle Scholar
Picallo, I, Klaina, H, Lopez-Iturri, P, Aguirre, E, Celaya-Echarri, M, Azpilicueta, L, Eguizábal, A, Falcone, F and Alejos, A (2019) A radio channel model for D2D communications blocked by single trees in forest environments. Sensors 19, 4606–4611.CrossRefGoogle ScholarPubMed
Gay-Fernández, JA, Cuiñas, I, Sánchez, MG and Alejos, AV (2011) Radio-electric validation of an electronic cowbell based on ZigBee technology. IEEE Antennas and Propagation Magazine 53, 40–44.CrossRefGoogle Scholar
Saunders, SR and Aragón-Zavala, A (2008) Antennas and Propagation for Wireless Communication Systems. 2nd edn. Delhi, India: Pashupai Printing, pp. 89–102.Google Scholar
He, R, Zhong, Z, Ai, B, Ding, J and Guan, K (2012) Analysis of the relation between Fresnel zone and path loss exponent based on two-ray model. IEEE Antennas and Wireless Propagation Letters 11, 208–211.Google Scholar
Jarndal, A and Alnajjar, K (2018) MM-wave wideband propagation model for wireless communications in built-up environments. Physics Communications 28, 97–107.CrossRefGoogle Scholar
Zang, J and Wang, X (2017) Measurements and modeling of path loss over irregular terrain for near-ground and short-range communications. Progress in Electromagnetics Research M 57, 55–62.CrossRefGoogle Scholar
Akyildiz, IF and Vuran, MC (2010) Wireless Sensor Networks. New York, NY, USA: Wiley, pp. 123–156. ISBN-13: 978–0470036013.CrossRefGoogle Scholar
Sawant, RP, Liang, Q, Popa, DO and Lewis, FL (2007) Experimental path loss models for wireless sensor networks. Proc. Military Commun. Conf., Orlando, FL, USA, 29–31 October 2007, pp. 1–7.CrossRefGoogle Scholar
Olasupo, T, Otero, CE, Kostanic, I and Shaikh, S (2015) Effects of terrain variations in wireless sensor network deployments. IEEE International RF and Microwave Conference (RFM), Kuching, Malaysia, 14–16 December 2015, pp. 83–88.CrossRefGoogle Scholar
Cassel, M, Dépret, T and Piégay, H (2017) Assessment of a new solution for tracking pebbles in rivers based on active RFID. Earth Surface Processes and Landforms 42, 1938–1951.CrossRefGoogle Scholar
Papini, M, Ivanov, VI, Brambilla, D, Arosio, D and Longoni, L (2017) Monitoring bedload sediment transport in a pre-alpine river: an experimental method. Rendiconti Online Societa Geologica Italiana 43, 57–63.CrossRefGoogle Scholar
Malon, K, Skokowski, P and Lopatka, J (2018) Optimization of wireless sensor network deployment for electromagnetic situation monitoring. International Journal of Microwave and Wireless Technologies 10, 746–753.CrossRefGoogle Scholar
Van Khoa, V and Takayama, S (2018) Wireless sensor network in landslide monitoring system with remote data management. Measurement 118, 214–229.CrossRefGoogle Scholar
Balaji, S, Anitha, M, Rekha, D and Arivudainambi, D (2020) Energy-efficient target coverage for a wireless sensor network. Measurement 165, 1–12.CrossRefGoogle Scholar
Rao, TR, Balachander, D, Tiwari, N and Mvsn, P (2013) Ultra-high frequency near-ground short-range propagation measurements in forest and plantation environments for wireless sensor networks. IET Wireless Sensor Systems 3, 80–84.CrossRefGoogle Scholar
Tadesse, AD, Acharya, OP and Sahu, S (2021) A wideband four-port multiple-input-multiple-output slot antenna for WLAN/WiFi/5G below 6 GHz applications. International Journal of RF and Microwave Computer-Aided Engineering 31, 1–12.CrossRefGoogle Scholar
Recommendation, ITURM (2020) Detailed specifications of the terrestrial radio interfaces of International Mobile Telecommunications-2020 (IMT-2020). ITU-R M, 2150–2150, Tech. Rep. M.Google Scholar
Dogan, H (2021) A new empirical propagation model depending on volumetric density in citrus orchards for wireless sensor network applications at sub-6 GHz frequency region. International Journal of RF and Microwave Computer-Aided Engineering, Early Access 31, 1–10.Google Scholar
Genc, A (2021) A new path loss model based on the volumetric occupancy rate for the pine forests at 5G frequency band. International Journal of Microwave and Wireless Technologies 13, 144–153.CrossRefGoogle Scholar
Olasupo, TO, Otero, CE, Olasupo, KO and Kostanic, I (2016) Empirical path loss models for wireless sensor network deployments in short and tall natural grass environments. IEEE Transactions on Antennas and Propagation 64, 4012–4021.Google Scholar
Olasupo, TO, Alsayyari, A, Otero, CE, Olasupo, KO and Kostanic, I (2017) Empirical path loss models for low power wireless sensor nodes deployed on the ground in different terrains. IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Aqaba, Jordan, 11–13 October 2017, pp. 1–8.CrossRefGoogle Scholar
Cheffena, M and Mohamed, M (2017) Empirical path loss models for wireless sensor network deployment in snowy environments. IEEE Antennas and Wireless Propagation Letters 16, 2877–2880.Google Scholar
Szajna, A, Athi, M, Rubeck, A and Zekavat, S (2015) 2.45 GHz near ground path loss and spatial correlation for open indoor and Snowy Terrain. IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), Boston, MA, USA, 6–9 September 2015, pp. 1–5.Google Scholar
Chong, PK and Kim, D (2013) Surface-level path loss modeling for sensor networks in flat and irregular terrain. ACM Transactions on Sensor Networks 9, 1–32.CrossRefGoogle Scholar
Alsayyari, A, Kostanic, I, Otero, C, Almeer, M and Rukieh, K (2014) An empirical path loss model for wireless sensor network deployment in a sand terrain environment. IEEE World Forum on Internet of Things (WF-IoT), Seoul, South Korea, 6–8 March 2014, pp. 218–223.CrossRefGoogle Scholar
Alsayyari, A and Aldosary, A (2019) Path loss results for wireless sensor network deployment in a Sparse tree environment. International Symposium on Networks, Computers and Communications (ISNCC), Istanbul, Turkey, 18–20 June 2019, pp. 1–6.CrossRefGoogle Scholar
Pozzebon, A, Andreadis, A, Bertoni, D and Bove, C (2018) A wireless sensor network framework for real-time monitoring of height and volume variations on sandy beaches and dunes. ISPRS International Journal of Geo-Information 7, 141–146.CrossRefGoogle Scholar
Pozzebon, A, Cappelli, I, Mecocci, A, Bertoni, D, Sarti, G and Alquini, F (2018) A wireless sensor network for the real-time remote measurement of aeolian sand transport on sandy beaches and dunes. Sensors 18, 820–825.CrossRefGoogle Scholar
Güneş, F, Sharipov, Z, Belen, MA and Mahouti, P (2017) GSM filtering of horn antennas using modified double square frequency selective surface. International Journal of RF and Microwave Computer-Aided Engineering 27, 1–8.CrossRefGoogle Scholar
Genc, A, Dogan, H and Basyigit, IB (2020) A new semiempirical model determining the dielectric characteristics of citrus leaves for the remote sensing at C band. Turkish Journal of Electrical Engineering Computer Sciences 28, 1644–1655.CrossRefGoogle Scholar
ITU-R P.527-4 Standard (2017) Electrical characteristics of the surface of the Earth. P series. Radio Wave Propagation.Google Scholar
ASTM D2216-19 Standard. Standard Test Methods for Laboratory Determination of Water (Moisture) Content of Soil and Rock by Mass, Developed by subcommittee: D18.03, book of standards volume: 04.08.Google Scholar