Increasing device energy efficiency in LoRaWAN networks via a learning-automata-based approach
by Konstantina P. Spathi; Georgia A. Beletsioti; Konstantinos F. Kantelis; Anastasios Valkanis; Petros Nicopolitidis; Georgios I. Papadimitriou
International Journal of Sensor Networks (IJSNET), Vol. 42, No. 2, 2023

Abstract: The internet of things (IoT) has emerged as one of the most important developments of the 21st century. Consumer, commercial, industrial, and infrastructural environments are among the many uses for IoT devices. IoT may also be used for environmental objectives, such as monitoring forest regions and avoiding forest fires, which is important for the protection of flora and fauna. Efficient energy management is critical for this purpose, since it allows for continual monitoring of forest areas. A strategy for increasing the lifetime of devices that monitor such sites is provided in this research. The developed algorithm makes use of the long-range wide area network protocol-based technology, which is an innovative technology for wide-area networks and sensor applications. An energy-efficient method based on machine learning is presented to extend the lifespan of the entire network by extending the lifetime of devices. Extensive simulation results demonstrate that the proposed energy-efficient approach, using a learning-automata mechanism, enhances device lifespan up to 6.7 times. The learning automaton determines which end node will serve as a cluster head. It is also proven that the learning automaton converges on the proper selections, as the automaton selects the appropriate node to act as cluster head, based on the energy consumed.

Online publication date: Wed, 21-Jun-2023

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