In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m². The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m² from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell.

In recent years, the Internet of Things (IoT) become a promising topic of technical social and economic significance, especially with the high number of developed sensors and technologies. Logistic applications are a perfect domain of IoT as it adds new functionalities in identification, traceability, storage and real-time tracking of good in the supply chain. Handling the huge quantities of heterogeneous IoT components and logistic items is an important challenge. We present in this paper a new Internet of Things middleware architecture for logistic transport applications. This architecture focuses mainly on a semantic model that uses ontologies for sensor data representation by describing the main entities involved in the logistic scenario.