Browsing by Author "Shankar Kumar, Valli Sanghami"

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    Intersection Collision Avoidance For Autonomous Vehicles Using Petri Nets
    (2019-08) Shankar Kumar, Valli Sanghami; Li, Lingxi; Chen, Yaobin; King, Brian
    Autonomous vehicles currently dominate the automobile field for their impact on humanity and society. Connected and Automated Vehicles (CAV’s) are vehicles that use different communication technologies to communicate with other vehicles, infrastructure, the cloud, etc. With the information received from the sensors present, the vehicles analyze and take necessary steps for smooth, collision-free driving. This the sis talks about the cruise control system along with the intersection collision avoidance system based on Petri net models. It consists of two internal controllers for velocity and distance control, respectively, and three external ones for collision avoidance. Fault-tolerant redundant controllers are designed to keep these three controllers in check. The model is built using a PN toolbox and tested for various scenarios. The model is also validated, and its distinct properties are analyzed.
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    Simulating Modern CPU Vulnerabilities on a 5-stage MIPS Pipeline Using Node-RED
    (Springer, 2022-03) Miles, Samuel; McDonough, Corey; Michael, Emmanuel Obichukwu; Shankar Kumar, Valli Sanghami; Lee, John J.; Electrical and Computer Engineering, School of Engineering and Technology
    This paper proposes a simulation of the 5-stage pipelined MIPS processor using Node-RED and illustrates the basic effects of modern CPU vulnerabilities. Demonstrated in this study are Spectre vulnerability attack and load value injection (LVI) transient-execution attack. The storing of secret data within the cache is shown for Spectre, and through the use of an attacker’s injected page number after a page fault has occurred, we demonstrate LVI’s ability to access the host secrets via simulated memory hierarchy. The persistence of the secret data in the cache can also be observed in the case of both attacks. The characteristics of such security vulnerabilities are successfully simulated with the proposed Node-RED-based processor simulator.
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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.