Energy-Saving Method for Controlling the Formation of Transmitted Signals in a Wireless Sensor Network

Authors

  • Aleksandr V. Koskin Orel State University named after I.S. Turgenev
  • Vyacheslav I. Fedorov Belgorod State National Research University
  • Yaser Muhanad Jabar Yaser Southern Technical University
  • Salach Alghazali Kufa University

DOI:

https://doi.org/10.52575/2712-746X-2023-50-4-901-912

Keywords:

wireless sensor network, LoRaWAN, energy efficiency, signal conditioning, adaptive data rate, energy consumption

Abstract

An analysis of the adaptive data rate algorithm is presented, which is used in wireless low-power global networks with terminal sensor devices (hereinafter referred to as wireless sensor networks), operating in accordance with the LoRaWAN (Long Range Wide Area Networks) protocol. An energy-saving method for controlling the formation of transmitted signals in a wireless sensor network is proposed. To implement this method, a system for energy-saving control of the formation of signals transmitted in a wireless sensor network is proposed. A series of simulation experiments was carried out to estimate the total energy consumption of terminal devices when implementing the proposed method and when implementing the adaptive data transfer rate algorithm. The implementation of statistical processing of the results of simulation modeling is presented, which showed that the use of the proposed energy-saving method for controlling the formation of signals transmitted in a wireless sensor network can significantly reduce the energy consumption of terminal devices compared to the use of the well-known adaptive data rate algorithm.

Downloads

Download data is not yet available.

Author Biographies

Aleksandr V. Koskin, Orel State University named after I.S. Turgenev

Doctor of Technical Sciences, Professor, Director of the Department of Informatization and Perspective Development, Orel State University named after I.S. Turgenev,
Orel, Russia

Vyacheslav I. Fedorov, Belgorod State National Research University

Candidate of Technical Sciences, Associate Professor, Department of In-formation and Robotic Systems, Belgorod State National Research University,
Belgorod, Russia

Yaser Muhanad Jabar Yaser, Southern Technical University

MSc, Assistant lecturer of Southern Technical University,
Basra, Iraq

Salach Alghazali, Kufa University

Candidate of Technical Sciences, lecturer, Kufa University,
Najaf, Iraq

References

Польщиков К.А. 2014. Об управлении интенсивностью потоков данных в мобильной радиосети специального назначения. Научные ведомости БелГУ. История. Политология. Экономика. Информатика, 32(1): 196–201.

Ясир М.Д.Я., Польщиков К.А., Маматов Е.М. 2023. Имитационная модель функционирования беспроводной сети с низким энергопотреблением. Экономика. Информатика, 50(3): 645–654.

Ясир М.Д.Я., Польщиков К.А., Федоров В.И. 2023. Модель доставки сообщения в сенсорной сети с низким энергопотреблением. Экономика. Информатика, 50(2): 439–447. DOI: 10.52575/2687-0932-2023-50-2-439-447.

Casas R., Hermosa A., Marco Á. 2021. Real-time extensive livestock monitoring using lpwan smart wearable and infrastructure. Applied Sciences (Switzerland), 11(3): P. 1–18.

Hernández-Morales C.A., Luna-Rivera J.M., Perez-Jimenez R. 2022. Design and deployment of a practical IoT-based monitoring system for protected cultivations. Computer Communications, 186: 51–64.

Kang J.J., Yang W., Haskell-Dowland P. 2020. No Soldiers Left Behind: An IoT-Based Low-Power Military Mobile Health System Design. IEEE Access, 8: 201498–201515.

Konstantinov I., Polshchykov K., Lazarev S., Polshchykova O. 2017. Mathematical Model of Message Delivery in a Mobile Ad Hoc Network. Proceedings of the 11th International Conference on Application of Information and Communication Technologies (AICT): 10–13.

Konstantinov I., Polshchykov K., Lazarev S., Polshchykova O. 2017. Model of Neuro-Fuzzy Prediction of Confirmation Timeout in a Mobile Ad Hoc Network. CEUR Workshop Proceedings. Mathematical and Information Technologies, 1839: 174–186.

Koohang A., Sargent C.S., Nord J.H., Paliszkiewicz J. 2022. Internet of Things (IoT): From awareness to continued use. International Journal of Information Management, 62: 102442.

Lavric A., Petrariu A.I. 2018. LoRaWAN communication protocol: The new era of IoT. 2018 International Conference on Development and Application Systems (DAS): 74–77.

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.

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.

Pointl M., Fuchs-Hanusch D. 2021. Assessing the potential of LPWAN communication technologies for near real-time leak detection in water distribution systems. Sensors, 21(1): 1–22.

Polshchykov K.O., Zdorenko Y.M., Masesov M.O. 2014. Method of telecommunications channel throughput distribution based on linear programming and neuro fuzzy predicting. Elixir International Journal. Network Engineering, 75: 27327–27334.

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.

Qin J., Li Zh., Wang R. 2021. Industrial Internet of Learning (IIoL): IIoT based pervasive knowledge network for LPWAN-concept, framework and case studies. CCF Transactions on Pervasive Computing and Interaction, 3(1): 25–39.

Quintanar-Gomez J., Robles-Camarillo D., Trejo-Macotela F.R., Campero-Jurado I. 2021. Telemonitoring Device of Blood Pressure and Heart Rate through Multilayer Perceptrons and Pulse Rate Variability. IEEE Latin America Transactions, 19(7): 1233–1241.

Slany V., Koudelka P., Krcalova E. 2022. New Hybrid IoT LoRaWAN/IRC Sensors: SMART Water Metering System. Computers, Materials and Continua, 71(2): 5201–5217.

Taleb H., Andrieux G., Motta Cruz E. 2021. Wireless technologies, medical applications and future challenges in WBAN: a survey. Wireless Networks. DOI: 10.1007/s11276-021-02780-2.

Tu Y., Tang H., Hu W. 2022. An Application of a LPWAN for Upgrading Proximal Soil Sensing Systems. Sensors, 22(12).

Yaser M.J., Polshchykov K.A., Polshchikov I.K. 2023. Algorithm for ensuring the minimum power consumption of the end node in the LoRaWAN network. Periodicals of Engineering and Natural Sciences, 11(4): 168–174.

Zhang R., Zhao C., Cui S. 2020. Design of a data acquisition and transmission system for smart factory based on NB-IoT. Lecture Notes in Electrical Engineering, 517: 875–880.


Abstract views: 29

Share

Published

2023-12-29

How to Cite

Koskin, A. V., Fedorov, V. I., Jabar Yaser, Y. M., & Alghazali, S. (2023). Energy-Saving Method for Controlling the Formation of Transmitted Signals in a Wireless Sensor Network. Economics. Information Technologies, 50(4), 901-912. https://doi.org/10.52575/2712-746X-2023-50-4-901-912

Issue

Section

SYSTEM ANALYSIS AND PROCESSING OF KNOWLEDGE