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Smart IoT for energy sustainable environments
February 6 @ 1:30 pm - 3:00 pm
Smart environments in wide-ranging applications are expected to have a large number of static/mobile IoT devices operating via machine-type communications for sensing/monitoring, actuation, and control purposes. For scalability and widespread adoption of such autonomous environments the IoT devices and the network need to be low-cost, light in terms of energy and data footprints, and potentially self-adaptive, so as to minimize human intervention. In this context, in this talk, we will present a data-driven approach to the performance optimization of IoT network systems. By application of context-aware learning of temporal and spatial dynamic stochasticity and cross-correlation of the sensing parameters of interest, the multi-sensing and communication activities of the IoT nodes are optimized. Further, data communication priority is dynamically adapted, thereby further minimizing sensing and communication-related energy and communication bandwidth overheads. A few examples, include smart power grid monitoring and smart sensing IoT systems, we will demonstrate how data-driven, context-aware light-weight
machine learning and edge computing-aided approaches can be utilized for more accurate system performance characterization and optimizations, leading to significant processing, communication, and storage resource efficiency and energy sustainability of the IoT network. We will share our experiences from field experiments, proof-of-concept implementations, and campus-wide deployments, and will highlight the possibilities of learning-aided networking optimizations for various multidisciplinary-disciplinary applications.
Time: Feb 6, 2023, 06:30 Eastern Time (US and Canada)
Join Zoom Meeting
Meeting ID: 951 454 7039
Co-sponsored by: IEEE North Jersey Section
Speaker(s): Dr. Swades De,