Time-Critical Data Transmission Scheme in Wireless Sensor Networks Using Machine Learning Approach

3. Problem Statement

Wireless sensor networks for real time data transmission like industrial control and monitoring demands extremely trustworthy and well-timed delivery of critical or sensitive data. The important issues in assembling the information in wireless sensor network is energy consumption, especially in remote areas where battery replacement is not physically possible. On the one hand, improving the network strength for sensors data gathering is again a significant problem in maximum wireless sensor networks (Arampatzis et al., 2005; Chen & Varshney, 2004; Li, 2007). In many emergency applications, the collected sensor data are usually useful only if it collected at sink node within some stipulated time period, so that based on criticality of collected data, appropriate actions can be taken at the earliest to reduce losses (Vijayakumar et al., 2019). If the data arrived late after the targeted deadline at sink node, the information may not be useful.