ML-Based Adaptive MAC Protocol for Real Time Data Transmission in Wireless Sensor Networks

Abstract Wireless Sensor Networks (WSNs) are expressively utilized in various real-time control and monitoring applications. WSNs have been expanded considering the necessities in industrial time-bounded applications to support the dependable, time-bound delivery of data. Recently, Machine Learning (ML) algorithms have been used to address various WSN-related issues. The use of machine learning techniques supports dynamically modifying MAC settings based on traffic patterns and network conditions. In WSNs to control the communication between a large number of tiny, low-power sensor nodes while preserving energy and reducing latency, effective MAC protocols are essential. This paper addresses the ML-based Adaptive MAC (ML-MAC) protocol to provide a priority-based transmission system. In this research, depending upon the predictions of the machine learning model, the MAC parameters are dynamically adjusted to find priority-based channel access and the optimal routing path to meet the deadline of critical data packets. From the result analysis, the average throughput and delay of the proposed ML-MAC algorithm is improved as compared to the existing I-MAC protocol. © 2017 Elsevier Inc. All rights reserved.