Device Free WiFi Sensing (DFWS):

Wi-Fi and wireless technology are rapidly evolving, leading to new methods for interpreting Wi-Fi signals. Device-Free WiFi Sensing (DFWS) has recently gained attention due to its cost-effectiveness, deployment, and penetration capabilities. 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. Our 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 to identify, monitor, track, and classify activities, other parameters, and applications.

Intelligent Sensing:

With the rapid advancement of artificial intelligence, cognitive technology, and big data, intelligent sensing systems have become a significant focus for researchers. These sophisticated sensors can adapt their internal behavior to optimize data collection and utilize advanced signal processing, data fusion techniques, intelligent algorithms, and AI to enhance sensor data interpretation. This improved data understanding leads to better sensor integration and feature extraction, making these systems highly effective for smart sensing applications. Recently, our research has concentrated on developing intelligent and soft sensors for monitoring various water quality and quantity parameters. Additionally, we are exploring the use of ultrasonic sensors as general-purpose intelligent sensors for environmental monitoring. These sensors can non-invasively measure temperature, humidity, breathing rate, and body temperature, offering versatile solutions for comprehensive environmental and health monitoring. Our work aims to harness the potential of these intelligent systems to create more efficient and accurate sensing applications, thereby contributing to advancements in smart technology and data-driven decision-making.

Internet of Things Edge Computing:

In a traditional IoT architecture, smart devices transmit collected data to the cloud or a remote data center for analysis. This model, while effective for some applications, often faces challenges due to the high volume of data traveling between devices and the cloud, leading to potential bottlenecks. These bottlenecks can make the system ineffective for latency-sensitive applications, where real-time processing is crucial. IoT edge computing offers a solution to this problem by moving data processing closer to the IoT devices themselves. By shortening the data route, edge computing significantly reduces latency and allows for near-instantaneous on-site data analysis. This means that instead of sending all the collected data to a distant server for processing, the data is processed at a nearby edge server. This approach not only speeds up the system’s performance but also reduces the load on network bandwidth, making it more efficient and responsive. The integration of Wi-Fi sensing technology into IoT and smart-home devices exemplifies the benefits of edge computing. Wi-Fi sensing can detect human presence and movements, providing valuable data for automation and smart home applications. By processing this data at the edge, devices can react in real-time, enhancing the user experience with more responsive and intelligent automation systems. This is particularly useful for applications such as security monitoring, energy management, and personalized home automation, where immediate response is critical. Overall, IoT edge computing enhances the capabilities of IoT systems, making them faster, more efficient, and better suited for real-time applications, while also leveraging the broad potential of Wi-Fi sensing technology for smarter automation.