Light Intensity Based IoT Device Positioning for Indoor Monitoring.

With the prosperous deployment of IoT devices in recent years, more data are collected by ubiquitous sensors. Hence, there are two main challenges needed to be solved. Firstly, how to accommodate the communication links among these devices and collect their data effectively? Secondly, how to track or localize these devices and well organize the IoT network? The common way to track these devices is achieved by registering locations of these devices manually or RSSI measurement. However, these measures suffer from high complexity and inaccuracy, and cause boring reconfiguration process with shift of devices. In this framework, we proposed a novel and low cost IoT monitoring system with self-location awareness. The collected data by sensors, such as the light intensity, can be used for device localizations. By using the proposed algorithm, we derived the critical parameters for light curves, and interpolated for the predicted light intensity inside the whole room. The experiment results indicated the proposed algorithm is useful for inferring device location with low cost, which are suitable for device management without privacy information.