Estimating the probability of messages delivery in an IoT system
https://doi.org/10.21822/2073-6185-2025-52-1-113-121
Abstract
Objective. The aim of the study is to develop a model for estimating the probability of message delivery in a medical Internet of Things (IoMT) system and to study the dependence of this value on the number of retransmissions used to compensate for distorted or lost telemetry data.
Method. The transmission of telemetry messages is carried out in accordance with the MQTT-SN protocol. Message delivery is performed using the server as an intermediary device to which client devices connect, i.e. sensor devices that transmit measured data, and wireless devices for healthcare workers that receive this data. To develop a message delivery model, it is proposed to use the apparatus of probabilistic graphs. The adequacy of the model was verified based on computational experiments.
Result. A mathematical model of the process of delivering telemetry messages in an IoT system has been developed, which adequately reflects the dependence of the probability of message delivery on the characteristics of wireless channels, parameters of transmitted packets and acknowledgments, as well as the allowed number of retransmissions.
Conclusion. The model can be used to estimate the probability of message delivery in the medical Internet of Things system. The use of the model makes it possible to select theoretically justified values of the permissible number of retransmissions, established to achieve the required values of message delivery probability under the current level of bit error rate in wireless channels.
About the Authors
K. A. Polshchykov
Belgorod National Research University
Russian Federation
Russian Federation
Konstantin A. Polshchykov, Dr. Sci. (Eng), Prof., Assoc. Prof., Prof., Department of Information and Robotic Systems,
85 Pobeda St., Belgorod 308015
T. N. Mahdi
Belgorod National Research University
Russian Federation
Russian Federation
Mahdi Tarek Nasser, Graduate student, Department of Applied Informatics and Information Technologies,
85 Pobeda St., Belgorod 308015
References
1. Wang N., He J., Xiang S., Yang J. Transmission reliability evaluation of the wireless mobile ad hoc network considering the routing protocol and randomness of channel capacity. Quality and Reliability Engineering International. 2024; 40: 664-680. DOI: 10.1002/qre.3432.
2. Konstantinov I.S., Polshchikov K.A., Lazarev S.A. Simulation model for transmitting information flows in a special-purpose mobile radio network. Scientific bulletins of Belgorod State University. Series: Economics. Computer science. 2015; 13(210): 156-163. URL: https://elibrary.ru/item.asp?id=24313935. (In Russ).
3. Elias E.M., Baharuddin M.N., Zaki M., Nur A., Roshartini O., Santoso B. Analyzing Data Transmission Reliability in Mobile Ad-Hoc Networks under Dynamic Scenarios. International Journal of Interactive Mobile Technologies. 2024; 18(11): 41. DOI: 10.3991/ijim.v18i11.49061.
4. Konstantinov I., Polshchykov K., Lazarev S., Polshchykova O. Model of Neuro-Fuzzy Prediction of Confirmation Timeout in a Mobile Ad Hoc Network. CEUR Workshop Proceedings. Mathematical and Information Technologies. 2017; 1839: 174-186. URL:https://elibrary.ru/item.asp?id=31032898.
5. Polshchykov K.O., Lazarev S.A., Zdorovtsov A.D. Neuro-Fuzzy Control of Data Sending in a Mobile Ad Hoc Network. Journal of Fundamental and Applied Sciences. 2017; 9(2S): 1494-1501. DOI: 10.4314/jfas.v9i2s.856.
6. Bhatti D.S., Saleem S., Imran A. et al. Detection and isolation of wormhole nodes in wireless ad hoc networks based on post-wormhole actions. Scientific Reports. 2024; 14. DOI: 10.1038/s41598-024-53938-9.
7. Polshchykov K.O., Lazarev S.A., Kiseleva E.D. Mathematical Model of Multimedia Information Exchange in Real Time Within а Mobile Ad Hoc Network. International Journal of Computer Science and Network Security. 2018; 18(6): 20-24. URL:http://paper.ijcsns.org/07_book/201806/20180603.pdf.
8. Soran A.H., Marwan A.M., Sazan K.S. Flying Ad-Hoc Networks (FANETs): Review of Communications, Challenges, Applications, Future direction and Open Research Topics. ITM Web Conferences. 2024; 64: 01002. DOI: 10.1051/itmconf/20246401002.
9. Kundu J., Alam S., Das J. C., Dey A., De D. Trust based Flying ad-hoc Network: A Survey. IEEE Access. 2024. DOI: 10.1109/ACCESS.2024.3419904.
10. Jameel K.J.Q., Likhosherstov R.V., Polshchikov K.A. Model of Video Streams Transmission in a Flying Ad Hoc Network. Economics. Information technologies. 2022; 49(2): 403-415 DOI: 10.52575/2687-0932-2022-49-2-403-415. (In Russ).
11. Bhatia T.K., Gilhotra S., Bhandari S.S., Suden R. Flying Ad-Hoc Networks (FANETs): A Review. EAI Endorsed Transactions on Energy Web. 2024; 11. URL: https://publications.eai.eu/index.php/ew/article/view/5489.
12. Hemmati A., Zarei M., Rahmani A.M. A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues. International Journal of Communication Systems. 2024; 37(1): e5625. DOI: 10.1002/dac.5625.
13. Wu C.-M., Tsai C.-T., Hou C.-C., Yang J.-J., Lin G.-D., Kuang M.-Y. Emergency Message Broadcast Mechanism in Vehicular Ad-Hoc Networks Based on Reinforcement Learning With Contention Estimation. IEEE Transactions on Intelligent Vehicles. 2024. DOI: 10.1109/TIV.2024.3418778.
14. Alaya B., Sellami L., Lorenz P. An ontological approach to the detection of anomalies in vehicular ad hoc networks. Ad Hoc Networks. 2024; 156: 103417. DOI: 10.1016/j.adhoc.2024.103417.
15. Yaser M.J.Y., Polshchikov K.A., Fedorov V.I. Message Delivery Model in a LowPower Sensor Network. Economics. Information technologies. 2023; 50(2): 439-447 DOI: 10.52575/2687-0932-2023-50-2-439-447. (In Russ).
16. Gulati K., Boddu R.S.K., Kapila D., Bangare S.L., Chandnani N., Saravanan G. A review paper on wireless sensor network techniques in Internet of Things (IoT). Materials Today: Proceedings. 2022; 51(1): 161-165. DOI: 10.1016/j.matpr.2021.05.067.
17. Yaser M.J., Polshchykov K.A., Polshchikov I.K. Algorithm for ensuring the minimum power consumption of the end node in the LoRaWAN network. Periodicals of Engineering and Natural Sciences. 2023; 11(4): 168-174. URL: http://dx.doi.org/10.21533/pen.v11i4.3779.
18. Munirathinam S. Industry 4.0: Industrial Internet of Things (IIOT). Advances in Computers. 2020; 117(1): 129-164. DOI: 10.1016/bs.adcom.2019.10.01.
19. Tabaa M., Monteiro F., Bensag H., Dandache A. Green Industrial Internet of Things from a smart industry perspectives. Energy Reports. 2020; 6(6): 430-446. DOI: 10.1016/j.egyr.2020.09.022.
20. Jiang D. The construction of smart city information system based on the Internet of Things and cloud computing. Computer Communications, 2020; 150: 158-166. DOI: 10.1016/j.comcom.2019.10.035.
21. Ghazal T.M., Hasan M.K., Alzoubi H.M., Alshurideh M., Ahmad M., Akbar S.S. Internet of Things Connected Wireless Sensor Networks for Smart Cities. Studies in Computational Intelligence. 2023; 1056. DOI: 10.1007/978-3-031-12382-5_107.
22. Sanjeevi P., Prasanna S., Siva Kumar B., Gunasekaran G., Alagiri I., Vijay Anand R. Precision agriculture and farming using Internet of Things based on wireless sensor network. Transactions on Emerging Telecommunications Technologies. 2020; 31:e3978. DOI: 10.1002/ett.3978.
23. Polshchykov K., Shabeeb A. H. T., Lazarev S., Kiselev V. 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. 2021; 9(3): 781-787. URL: http://dx.doi.org/10.21533/pen.v9i3.2281.
24. Abu N. S., Bukhari W. M., Ong C. H., Kassim A. M., Izzuddin T. A., Sukhaimie M.N., Norasikin M.A., Rasid A.F.A. Internet of Things Applications in Precision Agriculture: A Review. Journal of Robotics and Control. 2022; 3(3). URL: https://journal.umy.ac.id/index.php/jrc/article/view/14159.
25. Razdan S., Sharma S. Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies. IETE Technical Review. 2021; 39(4): 775-788. DOI: 10.1080/02564602.2021.1927863.
26. Arora S. IoMT (Internet of Medical Things): Reducing Cost While Improving Patient Care. IEEE Pulse, 2020; 11(5): 24-27. DOI: 10.1109/MPULS.2020.3022143.
27. Shanmugapriya D., Patel A., Srivastava G., Lin J.CW. MQTT Protocol Use Cases in the Internet of Things. Lecture Notes in Computer Science. 2021; 13147. DOI: 10.1007/978-3-030-93620-4_12.
28. Jameel J.Q., Mahdi T.N., Polshchykov K.A., Lazarev S.А., Likhosherstov R.V., Kiselev V.E. Development of a mathematical model of video monitoring based on a self-organizing network of unmanned aerial vehicles. Periodicals of Engineering and Natural Sciences. 2022; 10(6): 84-95. URL: http://dx.doi.org/10.21533/pen.v10i6.3381.
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For citations:
Polshchykov K.A., Mahdi T.N. Estimating the probability of messages delivery in an IoT system. Herald of Dagestan State Technical University. Technical Sciences. 2025;52(1):113-121. (In Russ.) https://doi.org/10.21822/2073-6185-2025-52-1-113-121
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