BITS Faculty Publications
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Item Decentralized trust: NFT and blockchain-enabled evidence system using fog computing(Elsevier, 2025-06) Chamola, VinayEvidence plays a crucial role in judicial systems, and managing it securely and efficiently ensures justice. This paper introduces Decentralized Trust, a framework that combines blockchain technology, Non-Fungible Tokens (NFTs), and fog computing to address common issues like tampering, delays, and reliance on centralized systems. Traditional methods that depend on cloud computing often face high latency and slow processing, especially in remote areas. This research also builds upon the challenges identified in previous studies, such as tampering vulnerabilities, inefficiencies in evidence processing, and accessibility issues in underserved regions, providing a novel and comprehensive solution through Decentralized Trust. Fog computing handles tasks closer to where data is created, reducing delays and improving response times. Blockchain ensures that evidence records cannot be altered, while NFTs make each piece of evidence unique and tamper-proof. The framework is organized into layers: edge nodes at police stations capture evidence, fog nodes process the data and create NFTs, and cloud storage, supported by the Interplanetary File System (IPFS), provides secure long-term storage. Results demonstrate that the framework achieves average transaction delays of 24.5 seconds on low-performance devices (Node A) and 168.9 seconds on high-performance devices (Node B), with margins of error showing efficient scalability even under significant processing loads. The observed transaction delays are due to differences in system architecture and processing priorities. High-performance devices (Node B) have more complex validation processes, increased security checks, or resource contention, contributing to longer transaction times. By combining these technologies, Decentralized Trust offers a reliable, fast, and secure way to manage judicial evidence, building trust in the framework while addressing the needs of remote and underserved areas.Item DemocracyGuard: Blockchain-based secure voting framework for digital democracy(Wiley, 2024-08) Chamola, VinayOnline voting is gaining traction in contemporary society to reduce costs and boost voter turnout, allowing individuals to cast their ballots from anywhere with an internet connection. This innovation is cautiously met due to the inherent security risks, where a single vulnerability can lead to widespread vote manipulation. Blockchain technology has emerged as a promising solution to address these concerns and create a trustworthy electoral process. Blockchain offers a decentralized network of nodes that enhances transparency, security, and verifiability. Its distributed ledger and non-repudiation features make it a compelling alternative to traditional electronic voting systems, ensuring the integrity of elections. To further bolster the security of online voting, we propose DemocracyGuard platform on the Ethereum blockchain, which incorporates facial recognition technology to authenticate voters. By leveraging these advancements, DemocracyGuard aims to provide a secure and resilient platform for online voting, paving the way for its broader adoption and revolutionizing the electoral landscape.Item A Blockchain-Enabled Split Learning Framework With a Novel Client Selection Method for Collaborative Learning in Smart Healthcare(IEEE, 2024-06) Chamola, VinayDistributed machine learning in healthcare has great potential in training models to learn from the patients’ data distributed across different medical institutions. Recently, there has been a significant surge in research works applying federated learning(FL), a popular distributed machine learning technique in the healthcare sector. However, FL faces challenges like communication overhead and scalability to low-resource devices like Internet of Things(IoT) nodes. Addressing these issues, we propose a Blockchain-enabled split learning framework with a novel client selection algorithm for collaborative learning in healthcare. In the split learning model, the neural network is trained between the server and the clients, and the forward and backward propagation steps to update the weights happen in a collaborative way. The Blockchain platform serves the functions of decentralized model governance, decentralized identity and access management, incentive management, and client selection governance in the proposed framework. We proposed a comprehensive client selection algorithm incorporating several client features like deadline strictness, resource availability, data utility, model utility, etc. The experimental results show that the proposed split learning model achieves better results than the federated learning and cloud-centric machine learning models. Further, we also provide a hardware implementation for the proposed framework to gauge its real-world deployment feasibility.Item V-track: blockchain-enabled IoT system for reliable vehicle location verification(Elsevier, 2024-08) Chamola, VinayLocation-Based Services (LBS) have greatly improved efficiency and functionality in various domains, but privacy and security concerns remain due to the centralized nature of many existing systems. To address these issues, this paper introduces the V-Track system, a decentralized architecture using blockchain technology for reliable vehicle location verification. By integrating GPS devices (SparkFun GPS NEO-M9), IoT-enabled sensors, and a Cosmos blockchain-based ledger (network of interconnected blockchains), V-Track aims to solve centralized LBS problems. Through rigorous simulation experiments, this paper evaluates the performance and security of the V-Track system and demonstrates its potential to provide reliable location verification while preserving user privacy. This paper makes significant contributions by presenting V-Track as a decentralized solution to centralized LBS privacy and security problems, enhancing reliability and trustworthiness through blockchain integration, improving tracking mechanisms with GPS devices and IoT sensors for improved accuracy, and providing a privacy-preserving alternative to centralized LBS through its decentralized design and use of blockchain technology. These advancements hold promise for applications across multiple sectors, including logistics, supply chain management, urban planning, and emerging fields such as autonomous vehicles and augmented reality.Item Secure cognitive health monitoring using a directed acyclic graph-based and AI-enhanced IoMT framework(Elsevier, 2024-09) Chamola, VinayMillions of people throughout the world struggle with mental health disorders, but the widespread stigma associated with these issues often prevents them from seeking treatment. We propose a novel strategy that integrates Internet of Medical Things (IoMT), DAG-based hedera technology, and Artificial Intelligence (AI) to overcome these challenges. We also consider the costs of chronic diseases such as Parkinson's and Alzheimer's, which often require 24-hour care. Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration, our system aims to provide low-cost, continuous support. Since IoMT data is large in volume, we need a blockchain network with high transaction throughput without compromising the privacy of patient data. To address this concern, we propose to use Hedera technology to ensure the privacy, and security of personal mental health information, scalability and a faster transaction confirmation rate. Overall, this research paper outlines a holistic approach to mental health monitoring that respects privacy, promotes accessibility, and harnesses the potential of emerging technologies. By combining IoMT, Hedera, and AI, we offer a solution that can help break down the barriers that prevent individuals from seeking the support they need for their mental well-being. Furthermore, comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%, which is more than 30% better than traditional models such as logistic regression.Item Blockchain-Enabled Vehicle Lifecycle Management With Predictive Maintenance using Federated Learning(IEEE, 2024-11) Chamola, VinayThe traditional landscape of vehicle lifecycle management systems has several issues, including widespread fraud, opaque processes, and limited accessibility. As a result, there is a need for a paradigm change toward modernized vehicle management techniques, which is connected with the emergence of Intelligent Transport Systems (ITS). This work is a novel solution in the shape of a Blockchain-Assisted Vehicle State Tracking System that is claimed to transform how automobiles are identified, registered, tracked, and controlled inside an Intelligent Transport System. The proposed model offers a secure, auditable ledger for tracking vehicle states. Incorporating federated learning-based predictive maintenance ensures timely servicing while protecting the privacy of user data. This paper explores the intricate architecture and promising capabilities to not only address the shortcomings of existing frameworks but also promote the evolution towards a seamlessly integrated, technologically driven ecosystem for vehicle management and Intelligent Transport Systems.Item Enhancing Security Using Quantum Blockchain in Consumer IoT Networks(IEEE, 2024-12) Chamola, VinayBlockchain technology, renowned for its ability to securely store data, hashes, and signatures permanently, faces unprecedented challenges in secure Consumer IoT (CIoT) networks with the advent of quantum computing. This paper proposes a set of robust quantum-based protocols and techniques to address these challenges by enhancing the security, scalability, and reliability of CIoT systems in the face of quantum threats. Updating the blockchain infrastructure is imperative to ensure ongoing security, which involves forks or protocol adjustments to establish new post-quantum chains and addresses, requiring rapid data and asset migration by users. Blockchain guarantees data integrity through an immutable ledger of transactions distributed via cryptographic hashes. The proposed quantum protocols and techniques enhance scalability and reliability and address the unique security needs of both commercial and governmental applications in secure CIoT networks through immersive embedded cyber-physical systems. These include Quantum Currency Security Protocols, Distributed Ledger Data Blocks, Quantum Ledger Verification, Quantum Solutions for Middleman Attacks, and the integration of Elliptic Curve Cryptography (ECC)-based security measures. By integrating these methods, the proposed approach ensures robust protection against emerging quantum threats, thereby securing sensitive information and transactions.Item CellularBlockB5G: A Blockchain-based Multi Operator Spectrum Sharing Simulator for 5G and Beyond Networks(IEEE, 2023) Chamola, VinayThe advancement of Fifth-generation networks has enabled service-specific resource provisioning through Network slicing. Moving forward, Beyond 5G (B5G) is the key enabling factor for the next generation of computing networks catering to the needs of seamless connectivity with ultra-reliable performance and security. But the deployment of such systems to provide various services through dynamic network slicing needs network densification, leading to increased operational cost. This requirement has bid to enable infrastructure sharing between multiple operators and HetNets through Blockchain as a promising solution with secure and distributed ledger-based operations. This work presents a comprehensive simulation environment providing blockchain integration with B5G networks. In particular, this work identifies key challenges to creating such a simulation environment and handles several operational details, including spectrum sharing, network slicing and dealing with orphan blocks. In the end, we have presented the evaluation of the simulator on 5G blockchain-based spectrum sharing. Furthermore, this work can facilitate further research on blockchain in B5G networks and help in providing a common framework for operators in analyzing such operations on a large scale.Item Next generation stock exchange: Recurrent neural learning model for distributed ledger transactions(Elsevier, 2021-07) Chamola, VinayA distributed stock exchange system encompasses multiple network hosts that participate in the sharing and exchange of resources. In such exchanges, the mediator or stock exchange must manage and delineate all operations in a cohesive manner. Stock exchange (SE) also acts as the transaction manager to provide consistent, isolated, durable, and atomic transactions for participating entities. However, the work for the stock exchange is not so straightforward as it may sound. With multiple transactions happening per second, the global serializability and concurrency control becomes an issue resulting in multiple threats and vulnerabilities. We propose a novel stock exchange that integrates time series prediction to distributed transactions and understanding the rapid global transactions and limitations of resources at the stock exchange. We use distributed acyclic graph (DAG) based distributed ledger technology IOTA to provide security and consensus for independent users. The paper proposes a time-variant model that adjusts its predictions based on transactions, moments of observations, participating entities, and history. We show that our model outcasts other state-of-art schemes in terms of prediction accuracy. Also, the model is fair, fast, and scalable to handle millions of transactions per second.Item A Blockchain and ML-Based Framework for Fast and Cost-Effective Health Insurance Industry Operations(IEEE, 2022) Chamola, VinayHealth insurance is crucial for each person, bearing in mind the increasing medical costs. COVID-19 has been an eye-opener as to how important it is to have health insurance. Medical emergencies can have a severe emotional and financial impact. Thus, a health insurance policy can help mitigate financial risks in unpredictable circumstances. However, the current insurance system is very expensive, as thousands of people pay the premiums, and very few take the claims. Furthermore, the claim settlement process is excruciatingly long and tiresome. In this article, we focus on establishing a rapid and cost-effective framework for the health insurance market, based on machine learning and blockchain technology. By developing a smart contract, blockchain may eliminate any third-party organizations and make the complete process safer, easier, and more efficient. The contract pays the claim based on the claimant’s documentation. We optimized the premiums using a regression model based on the net amount claimed during the current policy tenure and various other criteria. For anticipating risk, a random forest classifier is used, which aids in the risk-rated premium rebate computation for policyholders for their next term of insurance.