BITS Faculty Publications

Permanent URI for this communityhttp://localhost:4000/handle/123456789/1867

Browse

Author: search.filters.author.Chamola, VinaySubject: search.filters.subject.EEE

Search Results

Now showing 1 - 10 of 205
  • No Thumbnail Available
    Item
    Blockchain enabled traceability in the jewel supply chain
    (Springer Nature, 2025-01) Chamola, Vinay
    This article examines the potential of blockchain technology to revolutionize the jewelry supply chain by enhancing trust, transparency, and efficiency. Utilizing Ethereum, we developed a blockchain network tailored to the industry’s needs. Blockchain operates as a secure, immutable ledger, ensuring data integrity and transparency while preventing fraud and tampering due to its decentralized nature. Ethereum’s key features, including nodes, addresses, and smart contracts, make it an ideal platform for this application. The system incorporates robust security measures, addressing vulnerabilities such as reentrancy attacks and unauthorized access. Performance tests on networks demonstrated the solution’s viability, with Layer 2 optimizations reducing transaction costs. The system also uses IPFS (InterPlanetary File System) to store certificate templates in order to improve scalability and data accessibility. Six primary participants in the supply chain, from miners to customers, engage with the blockchain, ensuring full traceability and transparency. Certificates are dynamically generated by retrieving transaction hashes from the blockchain. The certificate template is stored on the InterPlanetary File System (IPFS), and when needed, the relevant data is populated into the template in real-time to produce the certificate. While challenges remain in terms of industry-wide adoption and regulatory compliance, the solution’s potential to enhance transparency and efficiency positions it as a significant advancement for the jewelry supply chain within the Industry 4.0 framework.
  • No Thumbnail Available
    Item
    Unleashing the power of generative AI in agriculture 4.0 for smart and sustainable farming
    (Springer, 2025-02) Chamola, Vinay
    Generative artificial intelligence (GAI) represents a pioneering class of artificial intelligence systems renowned for producing diverse media, such as text and images. Agriculture 4.0 (AG-4.0) is a concept that integrates advanced technologies such as the Internet of Things (IoT), data analytics, artificial intelligence, and precision agriculture into the agricultural sector. The integration of GAI and AG-4.0 can generate new and valuable agricultural insights and solutions through pattern recognition and data analysis. This integration enhances farming practices by generating predictive models, simulating optimal growth conditions, diagnosing plant diseases, and optimizing genetic traits. In spite of the tremendous scope of GAI in agriculture, there has been no detailed study concerning the applications and scope of GAI in AG-4.0. Addressing this research gap, we explore various applications, real-world products, and limitations of GAI in agriculture. We explore how GAI models such as ChatGPT and Dall-E can be personalized advisors for farmers, help increase awareness about farmer relief programs, design farm layouts, and many other such applications. Additionally, we cover four real-world GAI products deployed to assist farmers. Since GAI is a growing technology, it poses challenges such as scarcity of data, data privacy, and interpretability. We elaborately discuss these limitations and suggest multiple directions for future research in GAI for agriculture.
  • No Thumbnail Available
    Item
    Generative AI for finance: applications, case studies and challenges
    (Wiley, 2025-02) Chamola, Vinay
    Generative AI (GAI), which has become increasingly popular nowadays, can be considered a brilliant computational machine that can not only assist with simple searching and organising tasks but also possesses the capability to propose new ideas, make decisions on its own and derive better conclusions from complex inputs. Finance comprises various difficult and time-consuming tasks that require significant human effort and are highly prone to errors, such as creating and managing financial documents and reports. Hence, incorporating GAI to simplify processes and make them hassle-free will be consequential. Integrating GAI with finance can open new doors of possibility. With its capacity to enhance decision-making and provide more effective personalised insights, it has the power to optimise financial procedures. In this paper, we address the research gap of the lack of a detailed study exploring the possibilities and advancements of the integration of GAI with finance. We discuss applications that include providing financial consultations to customers, making predictions about the stock market, identifying and addressing fraudulent activities, evaluating risks, and organising unstructured data. We explore real-world examples of GAI, including Finance generative pre-trained transformer (GPT), Bloomberg GPT, and so forth. We look closer at how finance professionals work with AI-integrated systems and tools and how this affects the overall process. We address the challenges presented by comprehensibility, bias, resource demands, and security issues while at the same time emphasising solutions such as GPTs specialised in financial contexts. To the best of our knowledge, this is the first comprehensive paper dealing with GAI for finance.
  • No Thumbnail Available
    Item
    Resource allocation in unmanned aerial vehicle networks: A review
    (Elsevier, 2025-04) Chamola, Vinay
    Currently, resource allocation in Unmanned Aerial Vehicles (UAVs) is a major topic of discussion among industrialists and researchers. Considering the different emerging applications of UAVs, if the resource allocation problem is not addressed effectively, the upcoming UAV applications will not serve their proposed purpose. Although there are numerous and diverse research works addressing the resource allocation in UAVs, there is an evident lack of a comprehensive survey describing and analyzing the existing methods. Addressing this research gap, we present an extensive review of the resource allocation in UAVs. In this work, we classify the existing research works based on four criteria - optimization goal-based classification, mathematical model-based classification, game theory framework-based classification, and machine learning model-based classification. Our findings revealed that the mathematical models are relatively more explored to solve the resource allocation problem in UAVs. Researchers have explored a variety of game theory techniques, like the Stackelberg model, mean-field game theory, cooperative games, etc., for optimized resource allocation in UAVs. The optimization of energy and throughput factors is more seen in the literature compared to the other optimization goals. We also observed that the reinforcement learning technique is a heavily exploited technique for resource allocation in UAVs compared to all other machine learning-based methods. We have also presented several challenges and future works in the field of resource allocation in UAVs.
  • No Thumbnail Available
    Item
    HardSecUAV: A hardware-based mutual authentication protocol for network of drones
    (Elsevier, 2025-04) Chamola, Vinay
    Unmanned Aerial Vehicle (UAV) networks are increasingly utilized in various applications, yet they face significant security challenges due to their open operational environments and resource constraints. Existing authentication protocols often rely on stored secret keys, making them vulnerable to physical attacks and key compromise. To address this research gap, we propose HardSecUAV, a lightweight and secure authentication protocol that leverages hardware-based security features of the DS28S60 cryptographic coprocessor, including Physically Unclonable Functions (PUFs). Our protocol eliminates the need for secret key storage by generating device-specific secrets on-demand, enhancing resistance to physical attacks. Implementation results demonstrate that HardSecUAV achieves a total computation time of approximately 28.775 during the authentication phase, suitable for real-time operations. The storage requirement is 1024 bits, and the communication overhead is 1664 bits, comparable to existing schemes. Compared to existing protocols, HardSecUAV provides enhanced security features, including mutual authentication, replay attack resistance, forward secrecy, and UAV-to-UAV authentication without dependence on Ground Station(GS), making it a robust solution for securing UAV networks.
  • No Thumbnail Available
    Item
    Catalysing assistive solutions by deploying light-weight deep learning model on edge devices
    (Taylor & Francis, 2023-06) Chamola, Vinay
    Nowadays, real-time object detection, which is a crucial task, is being performed through image processing and deep learning techniques. As there are several high-performance computing edge devices available, selecting the best-fit device for a particular problem is a tough task and keeping in mind the cost, performance, and weight of the device in mind. One faces several challenges while performing this task in real-time such as a lack of resources in terms of power and mobility. We have provided an insight into the computation power of devices in terms of Frames per Second (FPS) by deploying object detection models on them. This paper will provide insight into selecting the appropriate combination of device and object detection models for real-time applications. Raspberry Pi 3 (RPi3), Raspberry Pi 4 (RPi4), Intel Neural Compute Stick 2 (NCS2), and Nvidia Jetson NANO are popular devices with high computation power used for real-time applications. The memory constraints of devices along with the deployment of different You Only Look Once (YOLO) and Single-Shot Detector (SSD) are the two object detection models that have been explained in this paper. A deep learning inference optimiser, TensorRT, has been used in NANO to achieve high throughput in the performance of object detection. The precision, recall, and F1 score achieved on deploying each tested model have been presented. After observing the devices during experimentation, RPi4+NCS2 showed the best execution with the blend of factors i.e. speed, portability, and user-friendliness
  • No Thumbnail Available
    Item
    FPGA-accelerated yolox with enhanced attention mechanisms for real-time wildfire detection on AAVS
    (IEEE, 2025-04) Chamola, Vinay
    Real-time wildfire detection is crucial for enabling prompt intervention and minimizing environmental and economic damages; however, deploying high-accuracy detection models on resource-constrained platforms such as autonomous aerial vehicles (AAVs) presents significant challenges due to limitations in computational capacity and power availability. In this article, we propose layerwise channel attention module (LCAM)-YOLOX, an enhanced object detection framework that integrates an LCAM into the YOLOX architecture to improve detection accuracy while maintaining computational efficiency. The model is optimized for deployment on FPGA platforms through 8-bit integer quantization, facilitating efficient inference on devices with limited resources. We implement and evaluate the LCAM-YOLOX model on the Xilinx Kria KV260 FPGA platform, demonstrating that it achieves a quantized mean average precision (mAP) of 78.11%, outperforming other state-of-the-art models such as YOLOv3, YOLOv5, and YOLOX-m. Moreover, the LCAM-YOLOX model processes at 195 frames per second (FPS) using a single DPU core on the KV260, exceeding real-time processing requirements while consuming only 10.45 W of power, which translates to the highest performance per watt ratio among the tested platforms. These results highlight the suitability of the KV260 FPGA as an optimal choice for deploying high-performance, energy-efficient wildfire detection models on AAVs, enabling real-time monitoring in resource-constrained environments.
  • No Thumbnail Available
    Item
    Future of connectivity: a comprehensive review of innovations and challenges in 7G smart networks
    (IEEE, 2025-04) Chamola, Vinay
    The evolution from 1G to 6G networks has transformed global communication, progressing from basic voice calls in 1G to the immersive, AI-enabled experiences of 6G. As emerging AI-driven applications like autonomous systems, the Internet of Everything (IoE), and immersive technologies demand unprecedented capabilities, 7G networks are set to redefine connectivity by overcoming the limitations of earlier generations. This paper comprehensively reviews the innovations and challenges in 7G networks, focusing on integrating advanced AI and machine learning paradigms such as meta-learning, incremental learning, distributed intelligence, and reinforcement learning to enhance adaptability, resource allocation, and edge performance. The review also examines the role of Large Language Models (LLMs) in enabling real-time actionable intelligence and optimizing edge devices within 7G. The paper highlights the use of technologies, including blockchain for decentralized security, quantum computing for robust encryption, terahertz communication for ultra-fast data transfer, zero-energy solutions for sustainability, and generative AI for intelligent network optimization and automation. By addressing these challenges and exploring cutting-edge strategies, this paper envisions 7G networks as the foundation for a secure, intelligent, and sustainable digital future, equipped to combat emerging cyber warfare threats, enhance resilience against technological disruptions, and support innovations across smart cities, autonomous systems, healthcare, and industrial IoT.
  • No Thumbnail Available
    Item
    Metaverse for education: developments, challenges, and future direction
    (Wiley, 2025-04) Chamola, Vinay; Sangwan, Devika
    The rapid advancements in digital technologies such as artificial intelligence (AI), virtual reality (VR), augmented reality (AR), mixed reality (MR), extended reality (XR), and the internet of things (IoT) have revolutionized various sectors, including education. Metaverse, a convergence of these transformative technologies, offers immersive, personalized, and interactive experiences, making it a powerful tool in modern education. This paper explores the Metaverse's role in enhancing education by examining its architecture, types, and components while addressing practical implementation challenges, and follows a structured review protocol to ensure a comprehensive analysis, including systematic research, paper selection, and a critical examination of relevant studies from reputable databases such as Google Scholar, IEEE Xplore, ACM, and Springer. The research objectives focus on evaluating the Metaverse's applications in education, ethical challenges, technological limitations, and potential strategies for sustainable integration. Key research questions address the need for Metaverse adoption in education, its benefits, challenges, and future directions. The Metaverse cultivates essential skills such as empathy, ethical reasoning, and effective communication by providing students with customized, immersive learning environments. However, ethical concerns, technical barriers, and infrastructural costs pose significant obstacles to its widespread adoption. It discusses strategies to solve these barriers, explores applications in distance learning, and proposes future research directions to create scalable and sustainable educational models in the Metaverse. Through this structured inquiry, the paper establishes the Metaverse as a transformative force in education, blending technological innovation with instructional advancement.
  • No Thumbnail Available
    Item
    Balancing consistency and performance in edge-cloud transaction management
    (Elsevier, 2025-06) Chamola, Vinay
    The proliferation of Internet of Things (IoT) devices has led to edge-cloud computing paradigms where resource-constrained edge devices connect to cloud servers. However, traditional concurrency control methods like two-phase locking (2 PL) and optimistic concurrency control (OCC) are inefficient in these heterogeneous environments. This paper presents adaptive transaction management protocols for edge-cloud systems. We propose EC-Lock which transitions between non-blocking and blocking phases, and EC-OCC which distinguishes edge and cloud transactions during timestamp validation. These hybrid techniques reduce unnecessary blocking and restarts. Simulation studies demonstrate that EC-Lock and EC-OCC provide substantial performance gains over traditional protocols under diverse workloads. By balancing consistency and efficiency, the proposed protocols enable scalable edge-cloud transaction processing. Our results show EC-Lock and EC-OCC better utilize scarce edge resources while minimizing cloud transaction impact. This work delivers innovative adaptive concurrency control optimized for emerging IoT-based edge-cloud computing architectures.