Message Delivery Model in a Low-Power Sensor Network
DOI:
https://doi.org/10.52575/2687-0932-2023-50-2-439-447Keywords:
IoT, sensor network, LoRaWAN, message delivery model, power consumption of network nodes, sensors, frame retransmissionsAbstract
The article is devoted to the development of a mathematical model for message delivery in a sensor network with low power consumption. The relevance of the study of the processes of functioning of wireless networks for collecting data transmitted by numerous sensor nodes, as well as the need to create theoretically justified means of reducing power consumption by end transceivers, is substantiated. Based on the use of the mathematical apparatus of probabilistic graphs, analytical expressions are obtained that allow estimating the probability of message delivery in the sensor network and the average number of frames that will need to be transmitted for this. The results of computational experiments carried out using the developed model are presented. Quantitative data have been obtained showing that increasing the allowed number of retransmissions makes it possible to increase the probability of message delivery in the sensor network, but this requires the transmission of more frames, which leads to an undesirable increase in the power consumption of end nodes. Based on the results obtained, further research is planned to develop an algorithm that minimizes the energy consumption of the end nodes of the sensor network.
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Джамил К.Дж.К., Лихошерстов Р.В., Польщиков К.А. 2022. Модель передачи видеопотоков в летающей беспроводной самоорганизующейся сети. Экономика. Информатика, 49(2): 403–415. DOI 10.52575/2687-0932-2022-49-2-403-415.
Константинов И.С., Пилипенко О.В., Польщиков К.А., Иващук О.Д. 2016. К вопросу обеспечения связи в процессе предупреждения и ликвидации чрезвычайных ситуаций на объектах строительства. Строительство и реконструкция, 1(63): 40-46.
Константинов И.С., Польщиков К.А., Лазарев С.А. 2015. Имитационная модель передачи информационных потоков в мобильной радиосети специального назначения. Научные ведомости БелГУ. Сер. Экономика. Информатика, 13 (210): 156–163.
Cheikh I., Sabir E., Sadik M. 2022. Multi-Layered Energy Efficiency in LoRa-WAN Networks: A Tutorial. IEEE Access, 10: 9198-9231.
Haque K.F., Abdelgawad A., Yanambaka V.P., Yelamarthi K. 2020. Lora architecture for v2x communication: An experimental evaluation with vehicles on the move. Sensors, 20(23): 1-26.
Jameel J.Q., Mahdi T.N., Polshchykov K.A., Lazarev S.А., Likhosherstov R.V., Kiselev V.E. 2022. Development of a mathematical model of video monitoring based on a self-organizing network of unmanned aerial vehicles // Periodicals of Engineering and Natural Sciences, 10(6): 84-95.
Luntovskyy A., Shubyn B., Maksymyuk T., Klymash M. 2021. 5G Slicing and Handover Scenarios: Compulsoriness and Machine Learning. Lecture Notes in Networks and Systems, 212: 223-255.
Mahdi T.N., Jameel J.Q., Polshchykov K.A., Lazarev S.A., Polshchykov I.K., Kiselev V.E. 2021. Clusters partition algorithm for a self-organizing map for detecting resource-intensive database inquiries in a geo-ecological monitoring system. Periodicals of Engineering and Natural Sciences, 9(4): 1138-1145.
Moysiadis V., Lagkas T., Argyriou V., Sarigiannidis A., Moscholios I.D., Sarigiannidis P. 2021. Extending ADR mechanism for LoRa enabled mobile end-devices. Simulation Modelling Practice and Theory, 113: 102388.
Park G., Lee W., Joe I. 2020. Network resource optimization with reinforcement learning for low power wide area networks. EURASIP Journal onWireless Communications and Networking, 2020: 176.
Polshchykov K., Lazarev S., Zdorovtsov A. 2017. Multimedia Messages Transmission Modeling in a Mobile Ad Hoc Network. Proceedings of the 11th International Conference on Application of Information and Communication Technologies (AICT): 24–27.
Polshchykov K.O., Lazarev S.A., Kiseleva E.D. 2018. Mathematical Model of Multimedia Information Exchange in Real Time Within а Mobile Ad Hoc Network. International Journal of Computer Science and Network Security, 18(6): 20–24.
Polshchykov K., Shabeeb A.H.T., Lazarev S. 2020. Algorithm for receiving the recommended bandwidth of a wireless self-organizing network channel. Periodicals of Engineering and Natural Sciences, 8(3): 1873–1879.
Polshchykov K., Shabeeb A.H.T., Lazarev S., Kiselev V. 2021. Justification for the decision on loading channels of the network of geoecological monitoring of resources of the agroindustrial complex. Periodicals of Engineering and Natural Sciences, 9(3): 781–787.
Radeta M., Ribeiro M., Vasconcelos D., Nunes N.J. 2019. LoRattle – An Exploratory Game with a Purpose Using LoRa and IoT, 11863: 263-277.
Rvachova N., Sokol G., Polschykov K., Davies J.N. 2015. Selecting the intersegment interval for TCP in telecomms networks using fuzzy inference system. Proceedings of the 6th International Conference “Internet Technologies and Applications” (ITA): 256-260.
Sahir S., Abbina Y., Krishna P.G. 2020. Implementation of environment gases monitoring system using lora gateway in smart cities with IoT technology, 12(2): 1109-1118.
Umer M.A., Stepanov S.N., Ndayikunda J., Kanishcheva M.G. 2020. Cellular network resource distribution methods for the joint servicing of real-time multiservice traffic and grouped IoT traffic. T-Comm, 14(10): 61-69.
Zhang X., Zhang M., Meng F. 2019. A Low-Power Wide-Area Network Information Monitoring System by Combining NB-IoT and LoRa. IEEE Internet of Things Journal, 6(1): 590-598.8
Zinonos Z., Chatzichristofis S.A., Gkelios S. 2022. Grape Leaf Diseases Identification System Using Convolutional Neural Networks and LoRa Technology, 10: 122-133.
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