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.

Abstract: The present invention provides a method for performing analysis on crowdsource data using mobile devices. The method comprises the steps of receiving a query specific function, pushing one or more instances of the query specific function for execution to a set of mobile devices, triggering one or more instances of the query specific function to generate one or more crowdsource data sets associated with the set of mobile devices receiving the one or more crowdsource data sets and analyzing the one or more crowdsource data sets according to the query specific function.