DETECTION RANGE CALIBRATION IN BINARY SENSOR NETWORKS USING EFFICIENT ALGORITHMS

The Internet of Things (IoT) relies on networks of physical objects equipped with sensors and actuators to collect and share data for analysis and decision-making. Wireless sensor networks are fundamental to IoT infrastructure. Binary Sensor Networks, which use simple and cost-effective devices with limited processing capabilities, are increasingly prevalent in IoT applications. They offer advantages over traditional wireless sensor networks by reducing reliance on noisy Received Signal Strength values influenced by environmental factors. Many sophisticated sensors can also function as binary-detection devices by producing binary reports based on predefined thresholds. This binary information is used to determine the presence or absence of a device within a specified area, impacting network coverage, deployment, and accuracy in localization and tracking. This research addresses various problems using binary information, with a focus on IoT applications. It explores the potential of Binary Sensor Networks to improve the efficiency and reliability of IoT systems, especially in situations where complex sensors are not required. The study investigates methods for optimizing coverage, deployment, and positioning accuracy by leveraging binary data, which has practical implications for a wide range of IoT scenarios