Machine Learning-Based Node Localization in IoT-Assisted WSN: An Initial Framework for Real-Time Applications

In the age of the Internet of Things (IoT), the utilization of intelligent devices has surged. These devices are sensor-equipped, heterogeneous devices, making wireless sensor networks (WSNs) the underlying technology of IoT, which can be referred to as IoT-assisted WSNs, to monitor physical environments. These networks are designed to collect data and perform specific tasks, with location information being a highly desirable task for achieving efficiency in achieving goals. However, traditional localization techniques are inadequate in dealing with the dynamic nature of sensor nodes. Machine learning (ML) has emerged as a promising solution to address these challenges in recent years. This paper presents a literature survey of various machine learning and localization techniques for IoT-assisted WSN. The need for localization and machine learning techniques in IoT is also discussed in detail. Furthermore, an initial framework for implementing machine learning techniques in an IoT-assisted WSN environment for node localization is proposed.