Optimal Environment Control Mechanism Based on OCF Connectivity for Efficient Energy Consumption in Greenhouse

Abstract

Greenhouses are a productive system that allows us to respond to the growing global demand for fresh and healthy food throughout the year, but the greenhouse environment is not easily controlled because its climate parameters are interrelated. However, the numbers of the actuator are operated parallelly to maintain the greenhouse environment; as a result, the energy consumption of greenhouses is high. In this study, we presented the optimization module by considering the outdoor environment with the aim of minimum energy consumption. Metaheuristic-based differential evaluation (DE) is used to optimize the climate parameters by considering indoor and outdoor environmental constraints. Furthermore, the long short-term memory (LSTM)-based inference model is offloaded on the Internet of Things (IoT) device to predict the next environmental situation. The objective function selects the optimal parameters within user preferences with minimum energy consumption based on the inferred parameter value. The open-source software framework IoTivity, implementing open connectivity foundation (OCF) technical standards, is used for the real-time connection between IoT devices and the IoT platform. Greenhouse owners can set the preferences based on the requirements of plants in the greenhouse by using a smart and remotely accessible Android-based interface. A fuzzy logic-based control module operates on an IoT device that maps the optimized parameters with the actuator and operates accordingly. The proposed model is analyzed, and the performance is evaluated in terms of energy consumption for each climate parameter and actuator in the greenhouse. The results show that the proposed mechanism saves 36% of energy.