An Energy Efficient WSN Assisted IoT Platform for Home/ Office Automation to enable Green IoT

Abstract

Internet of Things (IoT) comes out as a popular buzzword that can bring the next generation industrial revolution. The smart devices used in IoT's environment consume a tremendous amount of energy. Energy consumption is the futuristic approach to achieve the goals of Green IoT for smart city initiatives. However, we also need an intelligent and safe environment in our daily life. Here, the smart home concept comes into the picture, controlling home devices from a remote place. Many companies reached our house to assist us with popular smart controlling devices such as Amazon Alexa and Google Nest. These companies apply machine learning (ML) to predict usage patterns and enable the device to fine-tune the user experience. These ML systems were developed and processed in the cloud due to computation limitations on the client site. These cloud-based models introduce some processing delays in the system as ML takes its own processing time. It adds some delay between the training data generation and the system's actual prediction. Our proposed model bridges this gap in the system and performs ML computation on the fog layer.  Fog computing brings the processing power closer to the network's edge node along with the advantages of the cloud. This fog layer is continuously running and integrating the predictions into the system. It helps in bringing intelligence and optimizes energy consumption in the automation of the smart home solution.