Research Projects

LANTERN: Low-latency and private edge computing in random-access networks

We are living in a world where connected devices outnumber the human population, and this trend keeps growing: around 24.6 billion connections are forecasted in 2025—more than three times the estimated population. This gives rise to the Internet of Things (IoT) in which virtually all devices are interconnected and continuously share data. The IoT is a key enabler for a host of applications, such as intelligent transportation systems, smart cities, and smart grids. Thus it promises to transform the way we live. To realize the IoT, it is crucial and timely to develop a communication and computation infrastructure that is able to support the processing of a vast amount of time-sensitive data, for which a centralized computation is inadequate. Edge computing has emerged as a novel paradigm to guarantee very low-latency and high-bandwidth computing services. It involves moving the computation power from the cloud to where data is generated, by pooling the available resources at the network edge.

In this project, we investigate how low-latency and private edge computing protocols can be developed in wireless random-access networks. Relying on tools from information theory and coding theory, we will tackle the two following challenging objectives: i) to establish a foundation for privacy and reliability in latency-critical, multi-client and multi-server edge computing in random-access networks; and ii) to devise resilient coding schemes together with energy-efficient and scalable wireless random-access protocols to achieve low latency and preserve privacy in distributed edge computing. The results of this project will help to pave the way to the full realization of the IoT in the near future.

Other information
PI: Dr. Ngo Khac Hoang

SAME CATEGORY

EEG signal processing from the computer vision perspective

In this work, we study the processing of EEG signals in the image domain, i.e., visualizing the signal in the binary image and applying computer vision techniques for signal smoothing and classification. Some research questions are being investigated: 1. How well can we smooth the signal in the image domain? Here, we plot the interested […]

Vision-based crack detection with crack propagation modeling

In this work, the propagation of cracks on some materials, such as concrete, is analyzed, the best-fit model of which is employed for vision-based crack detection. Some research questions are being investigated: 1. What is the correlation between simulated crack propagation and real-world crack? Here, we measure the fitting errors between best-fit regression models of […]

Machine learning based beacon placement optimization for indoor robots localization

Indoor localization systems usually consist of transceiver with fixed positions, which are called beacons. These beacons act as landmarks for localizing objects that need to be positioned. One can easily see that the positions of beacons have a great impact on the performance of the localization system. The problem of beacon placement optimization is to […]