Sensing and Actuation
DETAILS
IoT applications often need to interface to the real world to collect data central to informing decisions, which in turn result in physical interactions. On a single embedded device, this is more generally called physical computing, where embedded microcontroller-based interactive systems can sense the world and/or control outputs such as displays and motors. In a typical IoT system, a sensor may collect information and route it to a smart control center, where defined business logic makes a decision, which in turn controls distributed actuators. Thus, sensors and actuators in IoT can work together in a distributed framework. Entwine researchers work with state-of-art sensing techniques and emerging sensors to investigate how data from modern sensors can be aggregated into useful information at edge nodes, fog nodes and the IoT core. Current sensors and actuators under investigation range from mmWave radar, LiDAR, industry 4.0 sensors, optical sensors and bio sensors for wearable applications, and motors for smart control applications.
Selected Publications
• C. Briciu-Burghina, J. Zhou, M. Ali and F. Regan, "Demonstrating the potential of a low-cost soil moisture sensor network", Sensors, vol. 22, pp. 987, no. 3, 2022.• M. Liu, L. Cheng, Y. Gu, Y. Wang, Q. Liu and N. O'Connor, "MPC-CSAS: Multi-party computation for real-time privacy-preserving speed advisory systems", IEEE Transactions on Intelligent Transportation Systems, pp. 1-7, 2021.
• Y. Gu, A. Zalkikar, M. Liu, L. Kelly, A. Hall, K. Daly and T. Ward, "Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data", Scientific Reports, vol. 11, pp. 1-13, no. 1, 2021.
• M. Liu, J. Naoum-Sawaya, Y. Gu, F. Lecue and R. Shorten, "A distributed markovian parking assist system", IEEE Transactions on Intelligent Transportation Systems, vol. 20, pp. 2230-2240, no. 6, 2018.
• M. Liu, R. Ordóñez-Hurtado, F. Wirth, Y. Gu, E. Crisostomi and R. Shorten, "A distributed and privacy-aware speed advisory system for optimizing conventional and electric vehicle networks", IEEE Transactions on Intelligent Transportation Systems, vol. 17, pp. 1308-1318, no. 5, 2015.