Sushmita Ghosh

Learning-based optimization strategies for sustainable IoT communications

In this project, we intend to develop the innovative data-driven techniques for application context aware smart computing and communication strategies at the edge nodes for 5th generation Internet of Things (5G IoT) and beyond communications. The use cases are important from both indoor as well as outdoor perspective, though the operating conditions, available resources, and objectives could be significantly different. Usually the smart devices/robots/sensors used in such use cases are of heterogeneous types and in large scale. Mobile communication and energy agents (e.g., unmanned aerial vehicles (UAVs)) are also deployed to undertake certain important tasks such as sensing, controlling, recharging, managing, remote diagnosing, etc. To perform the desired tasks by the appropriate smart device and/or mobile agents and at the right time requires coordination among the devices/ mobile agents and the central control system. For this, data collected by sensors and mobile agents are to be communicated either to a central system or to set of distributed agents or nodes depending upon the use case and the architecture. Primary objective of this project is to study context-aware, sustainable communication and networking issues in deployment of smart sensors and mobile agents and to provide optimal solutions in terms of algorithms, protocols, and deployable proof-of-concepts on application aware network platform.