Device Free WiFi Sensing (DFWS)

Wi-Fi and wireless technology are rapidly evolving, and scientists have developed new ways to make sense of Wi-Fi signals. Recently DFWS has gained attention due to its low cost and penetration power. It is unobtrusive, ubiquitous, and privacy protected. It works on the principle of observed variations of WiFi signals, called Channel State Information (CSI) to perform the sensing. While the CSI is designed primarily for data communication, in recent years we discovered that to overcome the interference due to the surrounding object movements and changes, the CSI actually captures a huge amount of information of the environment. So, in a nutshell, Wi-Fi sensing, we perform by decoding the information hidden in the CSI. Some sensing capabilities include basic motion detection, motion localization, presence detection, speed/velocity measurement, breathing detection, sleep monitoring, and daily activity monitoring. Wi-Fi Sensing technology is most prominent when applied in the health-at-home space, with specific use in elder care. Without the use of wearables, Wi-Fi Sensing allows caregivers to monitor elderly loved ones without infringing on their privacy or sense of independence. Machine learning and AI techniques are used to recognize the change in signal patterns in CSI in different environments. We focus on the study of Wi-Fi sensing, which includes channel estimation to extract CSI values corresponding to all sub-carriers, use of advanced filtering techniques, feature extraction, pattern recognition and use of machine learning, deep learning and AI techniques for various sensing applications.

Intelligent Sensing

With rapid development of artificial intelligence, cognitive technology and big data technology, intelligent sensing system has attracted extensive attention of researchers. Intelligent sensors are capable of modifying its internal behaviour to optimize the collection of data and utilize advanced signal processing techniques, data fusion techniques, intelligent algorithms, and artificial intelligence concepts to better understand sensor data, for better integration of sensors and feature extraction, leading to measures that can be used in smart sensing applications. In the recent past we are focused on development of intelligent and soft sensors for different water quality and quantity monitoring parameters. We also conducting experiments to use ultrasonic sensors as a general purpose intelligent sensor to monitor the environment temperature, humidity and breathing rate and body temperature monitoring in an non-invasive manner.

Internet of Things Edge Computing

In a classic IoT architecture, smart devices send collected data to the cloud or a remote data center for analysis. High amounts of data traveling from and to a device can cause bottlenecks that make this approach ineffective in any latency-sensitive use case. IoT edge computing solves this issue by bringing data processing closer to IoT devices. This strategy shortens the data route and enables the system to perform near-instant on-site data analysis. IoT edge computing is the practice of using data processing at the network's edge to speed up the performance of an IoT system. Instead of sending data to a remote server, edge computing enables a smart device to process raw IoT data at a close-by edge server. Wi-Fi Sensing adopted by more IoT and smart-home devices, since it has broad application potential in automation triggered by human presence.