Energy Consumption Reduction By Integrating Wireless Sensors And Actuators Networks Supervisory Controller With The Cloud Computing

In this paper we present a new framework for energy consumption conservation in green smart building. We propose an energy efficient smart wireless control system that integrates Wireless Sensors Actuators Networks (WSAN), Machine Learning and Cloud Computing. Moving WSANs Supervisory controlled by Petri nets system to the cloud have many advantages such as scalability, and availability. The first component of our proposed framework is Petri Nets supervisory controller which is implemented to monitor and control the entire system. Moreover, fault-tolerant algorithms could be developed and deployed with the supervisory system. The second component of our framework is the machine learning scheme that is responsible of processing and manipulating users preferences based on Spiking Neural Networks. This is very crucial as it considers behavioral preferences of tenants of building called user preferences. Finally integrating all components of the framework and deploy it on the cloud environment gives WSAN the benefits of using cloud resources and make the system highly available, scalable and fault-tolerant.