Browsing by Author "Kim, Dongsoo"

Now showing 1 - 5 of 5
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    Building a Private LoRaWAN Platform
    (BEIESP, 2019) Lee, John J.; Souryal, Youssef; Tam, Darren; Kim, Dongsoo; Kang, Kyubyung; Koo, Dan D.; Electrical and Computer Engineering, School of Engineering and Technology
    LoRaWAN technology has been here for several years as one of LPWAN technologies. It consists of various components such as end nodes, a gateway, a network server, and an application server at the minimum. The servers have been exclusive products of commercial companies, and not many experimental or academic ones are available. Recently one such software has been developed. However, few fully functional academic ones have been reported. In this study, we implement a fully functional private independent LoRaWAN platform for the academic research of LPWAN Internet of Things (IoT) and demonstrate that our platform can support not only end-to-end LoRaWAN communication but also graphical user interface on an embedded and limited computing power system.
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    Emergency Evacuation Assistance
    (IEEE, 2020-01) Lee, John J.; Koo, Dan; Tadesse, Dinaol; Jain, Atharv; Shettar, Sushmitha; Kim, Dongsoo; Electrical and Computer Engineering, School of Engineering and Technology
    There have been more than necessary casualties due to a lack of intelligence in emergency evacuation mechanisms such as exit signs. Although large or complex buildings and facilities have many exit doors, in case of emergency, people may not be able to escape quickly enough due to sudden loss of directions and difficulty in finding safe routes to exit doors. If you were ever in such a situation, you would wish that if there were ever smart escape route assistance mechanisms available or at least smart exit signs available that safely and quickly guide you to a safe haven. It is what we try to make such a wish come true. In this paper, we propose a graph mapping scheme and a new safe evacuation route algorithm for safe emergency evacuation assistance, with the aid of recent technology called Internet of Things (IoT). The gist of our approach is that people are not allowed to pass through or even go towards any area where fire or toxic gas is detected by controlling the direction signals installed on exit signs. The experiments performed with our methodology shows that the proposed technology may be able to save more lives.
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    Multi-spectral Fusion for Semantic Segmentation Networks
    (2023-05) Edwards, Justin; El-Sharkawy, Mohamed; King, Brian; Kim, Dongsoo
    Semantic segmentation is a machine learning task that is seeing increased utilization in multiples fields, from medical imagery, to land demarcation, and autonomous vehicles. Semantic segmentation performs the pixel-wise classification of images, creating a new, seg- mented representation of the input that can be useful for detected various terrain and objects within and image. Recently, convolutional neural networks have been heavily utilized when creating neural networks tackling the semantic segmentation task. This is particularly true in the field of autonomous driving systems. The requirements of automated driver assistance systems (ADAS) drive semantic seg- mentation models targeted for deployment on ADAS to be lightweight while maintaining accuracy. A commonly used method to increase accuracy in the autonomous vehicle field is to fuse multiple sensory modalities. This research focuses on leveraging the fusion of long wave infrared (LWIR) imagery with visual spectrum imagery to fill in the inherent perfor- mance gaps when using visual imagery alone. This comes with a host of benefits, such as increase performance in various lighting conditions and adverse environmental conditions. Utilizing this fusion technique is an effective method of increasing the accuracy of a semantic segmentation model. Being a lightweight architecture is key for successful deployment on ADAS, as these systems often have resource constraints and need to operate in real-time. Multi-Spectral Fusion Network (MFNet) [1] accomplishes these parameters by leveraging a sensory fusion approach, and as such was selected as the baseline architecture for this research. Many improvements were made upon the baseline architecture by leveraging a variety of techniques. Such improvements include the proposal of a novel loss function categori- cal cross-entropy dice loss, introduction of squeeze and excitation (SE) blocks, addition of pyramid pooling, a new fusion technique, and drop input data augmentation. These improve- ments culminated in the creation of the Fast Thermal Fusion Network (FTFNet). Further improvements were made by introducing depthwise separable convolutional layers leading to lightweight FTFNet variants, FTFNet Lite 1 & 2. 13 The FTFNet family was trained on the Multi-Spectral Road Scenarios (MSRS) and MIL- Coaxials visual/LWIR datasets. The proposed modifications lead to an improvement over the baseline in mean intersection over union (mIoU) of 2.92% and 2.03% for FTFNet and FTFNet Lite 2 respectively when trained on the MSRS dataset. Additionally, when trained on the MIL-Coaxials dataset, the FTFNet family showed improvements in mIoU of 8.69%, 4.4%, and 5.0% for FTFNet, FTFNet Lite 1, and FTFNet Lite 2.
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    Network AIS-based DDoS attack detection in SDN environments with NS-3
    (2017-07-21) Jevtic, Stefan G.; Kim, Dongsoo; King, Brian; Luo, Xiao
    With the ever increasing connectivity of and dependency on modern computing systems, our civilization is becoming ever more susceptible to cyberattack. To combat this, identifying and disrupting malicious traffic without human intervention becomes essential to protecting our most important systems. To accomplish this, three main tasks for an effective intrusion detection system have been identified: monitor network traffic, categorize and identify anomalous behavior in near real time, and take appropriate action against the identified threat. This system leverages distributed SDN architecture and the principles of Artificial Immune Systems and Self-Organizing Maps to build a network-based intrusion detection system capable of detecting and terminating DDoS attacks in progress.
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    Trustworthy and Efficient Blockchain-based E-commerce Model
    (2024-08) Shankar Kumar, Valli Sanghami; Lee, John; King, Brian; Kim, Dongsoo; Hu, Qin
    Amidst the rising popularity of digital marketplaces, addressing issues such as non- payment/non-delivery crimes, centralization risks, hacking threats, and the complexity of ownership transfers has become imperative. Many existing studies exploring blockchain technology in digital marketplaces and asset management merely touch upon various application scenarios without establishing a unified platform that ensures trustworthiness and efficiency across the product life cycle. In this thesis, we focus on designing a reliable and efficient e-commerce model to trade various assets. To enhance customer engagement through consensus, we utilize the XGBoost algorithm to identify loyal nodes from the platform entities pool. Alongside appointed nodes, these loyal nodes actively participate in the consensus process. The consensus algorithm guarantees that all involved nodes reach an agreement on the blockchain’s current state. We introduce a novel consensus mechanism named Modified- Practical Byzantine Fault Tolerance (M-PBFT), derived from the Practical Byzantine Fault Tolerance (PBFT) protocol to minimize communication overhead and improve overall efficiency. The modifications primarily target the leader election process and the communication protocols between leader and follower nodes within the PBFT consensus framework. In the domain of tangible assets, our primary objective is to elevate trust among various stakeholders and bolster the reputation of sellers. As a result, we aim to validate secondhand products and their descriptions provided by the sellers before the secondhand products are exchanged. This validation process also holds various entities accountable for their actions. We employ validators based on their location and qualifications to validate the products’ descriptions and generate validation certificates for the products, which are then securely recorded on the blockchain. To incentivize the participation of validator nodes and up- hold honest validation of product quality, we introduce an incentive mechanism leveraging Stackelberg game theory. On the other hand, for optimizing intangible assets management, we employ Non-Fungible Tokens (NFT) technology to tokenize these assets. This approach enhances traceability of ownership, transactions, and historical data, while also automating processes like dividend distributions, royalty payments, and ownership transfers through smart contracts. Initially, sellers mint NFTs and utilize the InterPlanetary File System (IPFS) to store the files related to NFTs, NFT metadata, or both since IPFS provides resilience and decentralized storage solutions to our network. The data stored in IPFS is encrypted for security purposes. Further, to aid sellers in pricing their NFTs efficiently, we employ the Stackelberg mechanism. Furthermore, to achieve finer access control in NFTs containing sensitive data and increase sellers’ profits, we propose a Popularity-based Adaptive NFT Management Scheme (PANMS) utilizing Reinforcement Learning (RL). To facilitate prompt and effective asset sales, we design a smart contract-powered auction mechanism. Also, to enhance data recording and event response efficiency, we introduce a weighted L-H index algorithm and transaction prioritization features in the network. The weighted L-H index algorithm determines efficient nodes to broadcast transactions. Transaction prioritization prioritizes certain transactions such as payments, verdicts during conflicts between sellers and validators, and validation reports to improve the efficiency of the platform. Simulation experiments are conducted to demonstrate the accuracy and efficiency of our proposed schemes.