Yingying Chen's group won the Best Paper Runner-Up Award for their paper entitled "Towards Efficient Privacy-Preserving Top-k Trajectory Similarity Query" at IEEE MASS 2023. The 20th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS 2023) is the premier conference for mobile ad-hoc networks and smart systems, defined broadly.  As wireless ad-hoc networks continue to evolve and specialize into a number of application scenarios and environments, and sensor-based systems and technologies increasingly permeate our everyday life and become the inner fabric of the Internet of Things and cyber-physical systems, the unfolding of smart environments such as smart cities, smart farming, smart healthcare, and smart manufacturing, to name a few, demand integrated solutions that can make intelligent use of both cloud and edge systems, while applying machine learning and artificial intelligence tools to handle their growing complexity and to leverage the vast amount of available data created.

Sponsored by the IEEE Computer Society and IEEE Computer Society's TCDP, the 20th edition of the IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS) was held in Toronto, Canada in September 2023, and brought together researchers, developers, and practitioners to address recent advances in mobile ad-hoc and smart systems, covering algorithms, theory, protocols, systems & applications, experimental evaluations and testbeds, security/privacy, as well as AI/ML-based smart design.

Congratulations on this achievment Yingying !

The ECE Department is proud to announce that the paper authored by Spilios Evmorfos and Zhaoyi Xu (under the supervision of Prof. Athina Petropulu) has received the Best Student Paper Award at the 2023 IEEE Workshop on Machine Learning for Signal Processing (IEEE MLSP), Rome Italy. The paper was presented at the MLSP workshop by Spilios.   The abstract of the paper is given below. 

Congratulations to Spilios, Zhaoyi, and Athina !

Super sensitive technique could revolutionize treatment during pandemics like COVID-19

The test, described in Science Advances, is an electronic sensor contained within a computer chip. It employs nanoballs – microscopic spherical clumps made of tinier particles of genetic material, each of those with diameters 1,000 times smaller than the width of a human hair – and combines that technology with advanced electronics.

“During the COVID pandemic, one of the things that didn’t exist but could have stemmed the spread of the virus was a low-cost diagnostic that could flag people known as the ‘quiet infected’ – patients who don’t know they are infected because they are not exhibiting symptoms,” said Mehdi Javanmard, a professor in the Department of Electrical and Computer Engineering in the Rutgers School of Engineering and an author of the study. “In a pandemic, pinpointing an infection early with accuracy is the Holy Grail. Because once a person is showing symptoms – sneezing and coughing – it’s too late. That person has probably infected 20 people.”

For the past 20 years, Javanmard has been developing biosensors – devices that monitor and transmit information about a life process. During the COVID-19 pandemic, he became disheartened about the extent of infections and the extreme loss of life. He believed there had to be a way of using biosensors as a test to detect illness earlier.

Working with Muhammad Tayyab, a Rutgers doctoral student and co-author of the study, Javanmard and research colleagues at the Karolinska Institute in Sweden and Stanford and Yale universities started brainstorming.

“We thought: How is there a way where we can leverage our individual expertise to build something new?” Javanmard said.

The biosensor developed by the team works through a series of steps. First, it zeroes in on a virus’ characteristic sequence of nucleic acids – naturally occurring chemical compounds that serve as the primary information-carrying molecules in a cell. Next, because it amplifies any nucleic acid sequence found in the sample, it makes many more copies, as many as 10,000. Then, it clumps those thousands of specks of nucleic acids into nanoballs that are “large” enough to be detected.

The nanoballs are identified electrically when they are directed individually through minute channels containing electrodes on opposite sides. The process is akin to people walking single file through an airport security gate and being X-rayed one by one.

“Our method involves taking the viral nucleic acid material and rolling it up into a ball of DNA that is large enough to be detected by a cell measurement device known as an electronic cytometer,” Javanmard said. “As a result, we can flag the infection at its earliest stages when the concentration is still very low.”

ECE Professor Yingying Chen received a new NSF grant under the Division of Information and Intelligent Systems (IIS) for the project “Efficient and Robust Multi-model Data Analytics for Edge Computing.” This is a three-year $600,000 research project collaborating with Temple University and New York Institute of Technology. Rutgers’ share in this project is $240,000.

Many advanced edge-computing applications rely on large-scale data analysis for high-level decision-making. Edge computing makes computing faster and more efficient because it takes place near the physical location of either the user or the data rather than sending all the information to the cloud. For example, augmented reality/virtual reality (AR/VR) applications process data from high-definition sensors (such as cameras, motion sensors, and microphones) to enable accurate and robust human-computer interactions. Drones and electric vehicles perform tracking, adjustments and obstacle recognition and avoidance via analyzing data at the level of the vehicle. However, the current ability to understand and manage various high-dimensional sensing data is obscured by significant knowledge and data gaps due to the heterogeneous edge device and environments, hindering the building of precise models for emerging edge computing applications using data analytics. One important trend in edge computing is utilizing artificial intelligence (AI) to extract complex knowledge from various sensor measurements for precise modeling. Most edge devices have limited computing and memory resources, making it challenging to perform sophisticated data analytics using AI while satisfying the time requirements of most applications. Therefore, a heuristic data analytic framework is needed to enable efficient and robust edge event prediction using multi-model learning on resource-constrained edge devices. The goal of this project is developing transformative machine-learning and data analytics technologies for enabling AI-based applications on resource-constrained edge computing devices (such as IoT devices, AR/VR headsets, and drones). The outcome of this project will advance data analytics and machine-learning research by deriving and integrating various high-dimensional sensing data from diverse data sources and building robust predictive models for generic edge-computing applications.