arXiv subjects

Baek-Young Choi

Publications and source records attributed to Baek-Young Choi.

3 recordsLinked to original sources

arXiv2021

PiChu: Accelerating Block Broadcasting in Blockchain Networks with Pipelining and Chunking

Blockchain technologies have been rapidly enhanced in recent years. However, its scalability still has limitations in terms of throughput and broadcast delay as the network and the amount of transaction data increase. To improve scalability of blockchain networks, we propose a novel approach named PiChu that accelerates block propagation in blockchain networks by pipelining and verifying chunks of a block in parallel. Accelerating block propagation reduces the mining interval and chance of fork occurring, which in turn increases throughput. Our approach can be applied to the blockchain networks either directly or with a minor modification to the consensus. Through an extensive and large scale simulations, we validate that the proposed PiChu scheme significantly enhances the scalability of blockchain networks. For instance, a 64 MB block can be broadcasted in just 80 seconds in a blockchain network with a million nodes. The efficiency of PiChu broadcasting increases with bigger block sizes and a larger number of nodes in the network.

cs.DC↗

arXiv2018

Smart Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight CNN

Edge computing efficiently extends the realm of information technology beyond the boundary defined by cloud computing paradigm. Performing computation near the source and destination, edge computing is promising to address the challenges in many delay-sensitive applications, like real-time human surveillance. Leveraging the ubiquitously connected cameras and smart mobile devices, it enables video analytics at the edge. In recent years, many smart video surveillance approaches are proposed for object detection and tracking by using Artificial Intelligence (AI) and Machine Learning (ML) algorithms. This work explores the feasibility of two popular human-objects detection schemes, Harr-Cascade and HOG feature extraction and SVM classifier, at the edge and introduces a lightweight Convolutional Neural Network (L-CNN) leveraging the depthwise separable convolution for less computation, for human detection. Single Board computers (SBC) are used as edge devices for tests and algorithms are validated using real-world campus surveillance video streams and open data sets. The experimental results are promising that the final algorithm is able to track humans with a decent accuracy at a resource consumption affordable by edge devices in real-time manner.

cs.CV↗

arXiv2018

Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN

Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. Today many delay sensitive, mission-critical applications can leverage these edge devices to reduce the time delay or even to enable real time, online decision making thanks to their onsite presence. Human objects detection, behavior recognition and prediction in smart surveillance fall into that category, where a transition of a huge volume of video streaming data can take valuable time and place heavy pressure on communication networks. It is widely recognized that video processing and object detection are computing intensive and too expensive to be handled by resource limited edge devices. Inspired by the depthwise separable convolution and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural Network (LCNN) is introduced in this paper. By narrowing down the classifier's searching space to focus on human objects in surveillance video frames, the proposed LCNN algorithm is able to detect pedestrians with an affordable computation workload to an edge device. A prototype has been implemented on an edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance is achieved using real world surveillance video streams. The experimental study has validated the design of LCNN and shown it is a promising approach to computing intensive applications at the edge.

cs.CV↗