A machine learning-based optimization algorithm for wearable wireless sensor networks

Abstract

In an Internet of Things (IoT) setting, a Wireless Sensor Network (WSN) effectively collects and transmits data. Using the distributed characteristics of the network, machine learning techniques may reduce data transmission speeds. This paper offers a unique cluster-based data-gathering approach using the Machine Learning-based Optimization Algorithm for WSN (MLOA-WSN) designed in this article for assessing networks depending on power, latency, height, and length. Using the cluster head, the data-gathering technique is put into action, with the data collected from comparable groups transmitted to the mobile sink, where machine learning methods are then applied for routing and data optimization. As a result of the time-distributed transmission period, each node across the cluster can begin sensing and sending data again to the cluster head. The cluster-head node performs data fusion, aggregation, and compression, which sends the generated statistics to the base station. Consequently, the suggested strategy yields promising outcomes as it considerably improves network performance and minimizes packet loss due to a reduced number of aggregating procedures. The existing method for findings of the MLOA-WSN system is a value of 2.43, a packet loss rate analysis of 7.6 and an Average delay analysis of the optimizers for 224. The method was evaluated under various settings, and the outcomes indicated that the suggested algorithm outperformed previous techniques in terms of decreased delay and solution precision.