Designing an energy-efficient MQTT SN protocol for wireless IoT sensors on campus in university-type buildings
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Abstract
Modern construction and operation of university campuses require innovative approaches to energy management. Wireless sensor networks based on IoT technologies provide significant opportunities for monitoring and optimizing energy consumption in various structural elements of university-type buildings. However, existing data transmission protocols demonstrate insufficient energy efficiency in a heterogeneous construction environment. The study presents a modification of the MQTT-SN protocol, optimized for the specific conditions of university campus building structures, taking into account the peculiarities of radio signal propagation. The experimental testing was carried out on the basis of five academic buildings with different types of building structures and materials. The results demonstrate a 37.8% reduction in sensor power consumption while maintaining 99.3% data transmission reliability compared to the basic protocol. An adaptive handshake algorithm with a dynamic change in the survey period has increased the service life of autonomous sensors by 43.2% when integrated into the existing infrastructure. The proposed protocol has shown high scalability when deployed in multi-storey buildings with reinforced concrete structures, which makes it promising for upgrading existing university campuses and introducing it into new construction of educational institutions.
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References
Alotaibi, N.S., Ahmed, H.I.S., Kamel, S.O.M., Elkabbany, G.F. Secure еnhancement for MQTT рrotocol using distributed machine learning framework // Sensors. 2024. № 24(5). pp. 16-38.
Jara Ochoa H.J., Pena R., Ledo Y.M., Gonzalez E. Comparative analysis of power consumption between MQTT and HTTP protocols in an IoT platform designed and implemented for remote real-time monitoring of long-term cold chain transport operations // Sensors. 2023. № 23(10. pp. 48-96.
Jia K., Xiao J., Fan S., He G. A MQTT/MQTT-SN-based user energy management system for automated residential demand response: formal verification and cyber-physical performance evaluation // Applied sciences. 2018. № 8(7). pp. 10-35.
Khan M., Khan M., Jan S., Ahmad J., Jamal S., Shah A., Pitropakis N., Buchanan W. A Deep Learning-Based Intrusion Detection System for MQTT-Enabled IoT // Sensors. 2021. № 21(21). pp. 16-70.
Kurdi H., Thayananthan V. A multi-tier MQTT architecture with multiple brokers based on fog computing for securing industrial IoT // Applied sciences. 2022. № 12(14). pp. 71-73.
Lee S., Kim H., Hong D.K., Ju H. Correlation analysis of MQTT loss and delay according to QoS level // Mat. of the Inter. conf. on Information networking (ICOIN). 2013.
Manowska A., Wycisk A., Nowrot A., Pielot J. The Use of the MQTT Protocol in Measurement, Monitoring and Control Systems as Part of the Implementation of Energy Management Systems. Electronics, 2023. № 12(1). P. 17.
Marti M., Garcia-Rubio C., Campo C. Performance evaluation of CoAP and MQTT_SN in an IoT environment // Proceedings. 2019. 31(1). P. 49.
Naik N. Choice of effective messaging protocols for iot systems: MQTT, CoAP, AMQP and HTTP // IEEE International Systems Engineering Symposium (ISSE). 2017.
Ramelan, A., Wahyono, I.D., & Wibawanto, H. (2021). IoT Based Building Energy Monitoring and Controlling System Using LoRa Modulation and MQTT Protocol // IOP conf. Series: Materials science and engineering. 2019. Vol. 1096.pp. 12-69.
Schutz S., Eggert L., Schmitt S., Steinbach T. Improving energy efficiency of MQTT-SN in lossy environments using seed-based network coding // Mat. of the Inter. conf. on wireless and mobile computing, networking and communications (WiMob). 2017.
Shahri E., Pedreiras P., Almeida L. Extending MQTT with Real-Time Communication Services Based on SDN // Sensors. 2022. № 22(9). pp. 31-62.
Toldinas J., Lozinskis B., Baranauskas E., Dobrovolskis A. (2019). MQTT Quality of Service versus Energy Consumption // International conference electronics. 2019.
Vaccari I., Giovanni C., Aiello M., Mongelli M., Cambiaso M. MQTTset, a new dataset for machine learning techniques on MQTT // Sensors. 2020. № 20(22). pp. 65-78.
Zhang J., Zhang X., Gao J. Signal propagation analysis for iot deployment in building construction sites // Automation in construction. 2023. № 155. рр. 104-827.