Department of Computer Science and Information Systems

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Author: search.filters.author.Bhatia, AshutoshSubject: search.filters.subject.Internet of Things (IoT)

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    Enabling AI in Agriculture 4.0: A Blockchain-Based Mobile CrowdSensing Architecture
    (Springer, 2024-04) Bhatia, Ashutosh; Tiwari, Kamlesh
    Agriculture 4.0 relies on extensive data for predictive services, necessitating effective data collection. Mobile CrowdSensing (MCS), with its cost-effectiveness and scalability, addresses this need but faces centralization limitations. Blockchain-based frameworks have been proposed to mitigate these issues but often focus solely on data collection, lacking a comprehensive end-to-end architecture for smart agriculture. Recent literature has explored the integration of the Internet of Things (IoT), edge computing, fog computing, and cloud computing capabilities to establish centralized end-to-end architectures. Nonetheless, these architectures come with their own set of centralized limitations. In the context of contemporary technologies, the integration of blockchain and digital twin (DT) holds the potential to revolutionize the field of smart agriculture. This paper introduces a holistic end-to-end, layered, and service-oriented architecture for Agriculture 4.0, integrating mobile crowdsensing, blockchain, and DT. Unlike existing architectures, this approach aims to overcome centralization limitations, leveraging the strengths of emerging technologies. The proposed architecture extends current capabilities for more efficient and secure Agriculture 4.0 practices. We deploy the suggested architecture onto the Ethereum blockchain, demonstrating its practicality through the obtained results.
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    Enhancing security through continuous biometric authentication using wearable sensors
    (Elsevier, 2024) Bhatia, Ashutosh; Tiwari, Kamlesh
    The paper presents a novel approach for biometric continuous driver authentication (CDA) for secure and safe transportation using wearable photoplethysmography (PPG) sensors and deep learning. Conventional one-time authentication (OTA) methods, while effective for initial identity verification, fail to continuously verify the driver’s identity during vehicle operation, potentially leading to safety, security, and accountability issues. To address this, we propose a system that employs Long Short-Term Memory (LSTM) models to predict subsequent PPG values from wrist-worn devices and continuously compare them with real-time sensor data for authentication. Our system calculates a confidence level representing the probability that the current user is the authorized driver, ensuring robust availability to genuine users while detecting impersonation attacks. The raw PPG data is directly fed into the LSTM model without pre-processing, ensuring lightweight processing. We validated our system with PPG data from 15 volunteers driving for 15 min in varied conditions. The system achieves an Equal Error Rate (EER) of 4.8%. Our results demonstrate that the system is a viable solution for CDA in dynamic environments, ensuring transparency, efficiency, accuracy, robust availability, and lightweight processing. Thus, our approach addresses the main challenges of classical driver authentication systems and effectively safeguards passengers and goods with robust driver authentication.