Abstract: An energy-efficient wireless sensor network (WSN) is disclosed. The WSN comprises one or more base stations, a plurality of branch nodes communicatively coupled to each of the one or more base stations, and a plurality of leaf nodes communicatively coupled to each of the branch nodes. The WSN is configured for optimising energy consumption associated with each of the following: transmitting sensor data from the leaf node to the branch node; encryption of data transmitted between the leaf node and the branch node; updating firmware of the branch node or of the leaf node, through over-the-air updates from a remote server; sending data packets through the WSN based on a plurality of predefined levels of a Quality-of-Service (QoS); and configuring radio duty cycle protocols at the base station or the branch node or both during transmission and reception of data packets from the leaf nodes.

Abstract: An energy-efficient wireless sensor network (WSN) is disclosed. The WSN comprises one or more base stations, a plurality of branch nodes communicatively coupled to each of the one or more base stations, and a plurality of leaf nodes communicatively coupled to each of the branch nodes. The WSN is configured for optimising energy consumption associated with each of the following: transmitting sensor data from the leaf node to the branch node; encryption of data transmitted between the leaf node and the branch node; updating firmware of the branch node or of the leaf node, through over-the-air updates from a remote server; sending data packets through the WSN based on a plurality of predefined levels of a Quality-of-Service (QoS); and configuring radio duty cycle protocols at the base station or the branch node or both during transmission and reception of data packets from the leaf nodes.

Abstract: The present disclosure relates to development of a self-service artificial intelligence platform by integrating data-based model with physics-based model and vice-versa to generate real-time recommendations and control actions. Further, the present disclosure provides the system and method for at least one of data collection and preparation, developing a hybrid system/control model, and developing a physics-based model driven by data-based model and vice versa to generate real-time recommendations and control actions.

Abstract: Disclosed herein is a system and a method for improving the energy management of HVAC equipment. The system comprising: a plurality of sensors distributed in a building for sensing a set of parameters including environmental information, thermal zone information, energy consumption information, operational parameter information and field information from the building; a network for connecting the plurality of sensors; a server includes a hybrid platform with physics based simulation model and machine learning model for processing and controlling the parameters of the HVAC equipment.