Markov-based algorithms for wireless sensor network: Theoretical insights and python implementation

Abstract

The study concentrates on improving the dependability and operational efficacy of LoRa-based Wireless Sensor Networks (WSNs), which are extensively utilized in IoT applications, especially for long-range private networks. It seeks to deal with the problems that arise when a single node or communication line fails, which can have a big effect on network performance. The research utilizes a Markovian matrix theoretical framework to examine and simulate the behavior of LoRa-based Wireless Sensor Networks (WSNs), incorporating states such as Sleep (S), Idle (I), Transmit (T), and Receive (R) mode. A Python software program was created to put this model into action, allowing for testing and simulation with 50 fake data sets. The method stresses that the network should always be running, that sensor nodes should be replaced quickly, and that the network should be able to handle failures of individual nodes. The simulations indicate that using the Markov chain model in conjunction with detailed step-by-step math computation may yield a more accurate analysis of the data sets. The methodology also helps you evaluate protocols, change control, look at scalability, and make informed choices about how to build a network. This work offers practical benefits for the design, deployment, and maintenance of LoRa-based WSNs in real-world IoT scenarios. It supports network administrators and engineers in predicting power consumption, designing resilient protocols, scaling networks efficiently, and implementing adaptive control measures to ensure continuous and dependable operation. The integration of Markov chain mathematical modeling with Python-based simulation provides a robust solution for ensuring reliable operation of LoRa-based WSNs. The approach mitigates the impact of node failures, supports rapid recovery, and maintains network integrity.