Development of Libelium-based Reconfigurable Solutions For Smart City Applications

As the number of internet-connected devices, Internet of Things (IoT) devices are overgrowing, and thus, the collection and the process of enormous volumes of data generated from these devices is becoming increasingly difficult. The evolving edge computing models help process data stored near the sources to ensure the lowest latency and combine with cloud computing models to compute and handle many complex new data parameters. In this research, we focus on studying the application of the reconfiguration methods to remotely reconfigure the Libelium sensors by referring to related studies on the reconfiguration solutions. The reconfiguration solutions will be applied with the User-driven Adaptive Sampling Algorithm (UDASA) to optimize the device's power consumption time to meet the demand for both precision and latency of real-time data, solving the problem of sensors located in remote locations and hard-to-reach terrain. The feasibility of the research is studied by deploying Libelium devices - a modern IoT solution applied to today's large IoT models, to be able to read and send sensor data to the gateway over WiFi protocol. The sensor data is then stored in the Cloud to serve the following sampling time calculation to reconfigure the device, significantly reducing the energy consumption of the sensor device and opening a new development direction for a reconfiguration solution that applies algorithms to the fields of energy research and new reconfiguration for IoT devices.