Reducing the Impact of Unstable Connections Among Nodes of Wireless IIoT Clusters Using Machine Learning Methods

Distributed systems have become increasingly popular in recent years due to their ability to handle massive amounts of data and provide high level of availability and scalability. However, designing and implementing distributed algorithms is a complex task that poses significant challenges. For example, an IoT cluster of sensors for environmental monitoring can utilize a consensus algorithm. In this scenario, multiple sensor nodes are deployed in various locations to collect environmental data such as temperature, humidity, air quality, and other parameters. Consensus algorithms are crucial in distributed systems as they enable all nodes in the system to agree on a single value, even in the presence of faults. To ensure data consistency and reliability in such a distributed IoT cluster, a consensus algorithm can be employed. However, existing consensus algorithms suffer from several issues that can impact their performance and efficiency. One such issue is when the cluster contains an unstable connection with the master node, leading to the algorithm’s inability to perform. In such a situation, the follower node thinks the master is not working and starts the leader’s reelection process. This can cause the cluster to become stuck, with the leader’s reelection process constantly repeated. To overcome this challenge, we propose to utilize temporal analysis with a Seasonal Autoregressive Integrated Moving Average (SARIMA) machine learning model. Temporal analysis involves analyzing the response time from each node in the system. This approach can help predict delays and failures in distributed systems and enable the development of more efficient consensus algorithms. The proposed technique and model can help address one of the problems associated with existing consensus algorithms. By leveraging temporal analysis, the technique can mitigate the impact of unstable connections in the cluster. The model can help predict the response time from a functioning node that is cut off by a network link failure, enabling better management of the system’s performance.