http://dspace.dtu.ac.in:8080/jspui/handle/123456789/51 2026-04-28T06:25:02Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22692 Title: DESIGN AND IMPLEMENTATION OF REVERSIBLE LOGIC GATES AND ITS APPLICATION IN EMERGING TECHNOLOGIES Authors: RUHELA, DIKSHA Abstract: The increasing demand for high-speed, low-power, and scalable computing architectures has driven significant interest in reversible logic and all-optical computation. Conventional irreversible electronic systems inherently dissipate energy due to information loss, while photonic technologies offer ultra-fast signal propagation and immunity to electromagnetic interference. Reversible logic, when combined with optical implementations, provides a promising pathway toward energy-aware, quantum-compatible, and high-throughput computing systems. This thesis presents a comprehensive investigation into the design, modeling, and realization of reversible arithmetic and logic circuits for fixed-point unsigned integer data, with a primary focus on quantum and all-optical architectures. The work begins with the design of a reversible 4×4-bit Vedic multiplier based on the Urdhva– Tiryagbhyam (UT) algorithm, constructed using two proposed 2×2 Vedic multiplier modules and newly developed reversible ripple-carry adder (RCA) and carry-save adder (CSA) circuits. The proposed 4-bit Vedic multiplier is analytically compared with state-of-the-art reversible designs and is shown to be superior in terms of quantum cost (QC), gate count (GC), hardware complexity (HC), ancilla inputs (AI), and garbage outputs (GO). Logical correctness is verified using Xilinx Vivado, and an entropy-based analysis confirms the energy-saving benefits of reversibility by demonstrating reduced information loss. Building on this optimized real multiplier, the architecture is extended to realize a reversible complex Vedic multiplier in the quantum domain, following a modular and scalable design methodology. The thesis then explores all-optical N-bit Vedic multipliers based on MZI–SOA interferometric switches, constructed using existing optical adder architectures. These designs are analytically benchmarked against prior optical multipliers in terms of optical cost (OC) and optical delay (OD), and closed-form expressions for N-bit OC and OD are derived. The analysis reveals inherent scalability and stability limitations of interferometer-based designs due to phase sensitivity, interferometer duplication, and irreversibility. To overcome these challenges, interferometer-free RSOA-based reversible optical architectures are proposed. A family of RSOA-based reversible logic gates and arithmetic blocks is developed using cross-gain modulation (XGM). Using these primitives, scalable N- vi bit reversible Vedic multipliers are formulated analytically, and expressions for OC, OD, constant inputs (CI), and garbage outputs (GO) are derived. A key contribution of the optical domain is the design of a parity-preserving 2×2-bit reversible optical Vedic multiplier, implemented using RSOA-based logic gates. This multiplier is photonic-level simulated, and its performance is quantitatively evaluated using Extinction Ratio (ER), Q-factor, and Relative Eye Opening Percentage (REOP). Comparative analysis with existing optical multipliers demonstrates superior signal integrity and fault-awareness. This parity-preserving 2-bit building block is then extended analytically to construct an N-bit complex Vedic multiplier, with closed-form expressions derived for OC, OD, CI, and GO. Additionally, the thesis introduces a novel universal reversible NAND–NOR gate (NTG) realized using a hybrid RSOA and MZI–SOA switching mechanism, capable of implementing all fundamental Boolean logic functions and serving as a universal primitive for reversible optical logic synthesis. The final part of the thesis develops a reversible RSOA-based universal multiplexer framework, including 2×1 and 4×1 multiplexers, and demonstrates how these structures can realize all basic logic functions (AND, OR, NOT, XOR, XNOR, NAND, NOR) as well as half- adder and half-subtractor circuits. Performance evaluation based on Contrast Ratio (CR), Extinction Ratio (ER), and Relative Eye Opening Percentage (REOP) confirms the superior signal quality, robustness, and integration potential of the proposed RSOA–FRG architecture compared to existing photonic logic designs. Overall, this thesis establishes a unified, modular, and scalable methodology for reversible arithmetic and logic circuit design across quantum and photonic domains. The proposed architectures demonstrate strong potential for energy-efficient, fault-tolerant, and high-speed computation, providing a solid foundation for future extensions toward floating-point arithmetic, sequential reversible circuits, and large-scale integrated optical and quantum computing systems. 2025-12-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22691 Title: NODE LOCALIZATION AND ROUTING SCHEMES IN WIRELESS SENSOR NETWORKS Authors: MOHAN, YOGENDRA; Yadav, Rajesh Kumar (SUPERVISOR); Manjul, Manisha (CO-SUPERVISOR) Abstract: Numerous applications of wireless sensor networks (WSNs) highly depend on the node location, such as maritime rescue, agriculture, and hazardous environments. GPS-enabled sensors are neither cost-effective nor energy-efficient. Finding the position of a target node (an unknown node) is known as node localization. Henceforth, the precise location of sensor nodes significantly impacts the performance of WSNs. Energy efficiency and network life are crucial concerns in WSNs due to the limited battery life of the sensor nodes. An efficient cluster head (CH)-based routing is a need of WSNs. Security is a major challenge in WSNs. Malicious nodes cannot be ignored in the hostile environment of WSN operations. Firstly, the thesis proposed an RSSI-based node localization. The localization error estimated using the RSSI and trilateration method is chosen as a fitness function for the Seagal Optimization Algorithm (SOA) for further minimization of the localization error. The SOA is modified using logistic chaotic maps and Lévy flights, known as C-SOA and LF-SOA. The simulation results illustrate that the performance of the LF-SOA is better than the C-SOA and SOA. Secondly, to improve the life of the sensor nodes, a cluster head selection (CHS)-based routing scheme is proposed. The Pelican Optimization Algorithm (POA) is modeled for an energy- efficient CHS and routing. The CHS is based on five fitness functions: energy of the sensor nodes, distance between cluster members (CMs) and cluster head (CHs), distance between CHs and base station (BS), node degree, and node centrality. These five fitness functions are applied to the POA to select the CHs. Further, the selected optimal CHs are participating in the routing process to send the aggregated data from the neighbor’s nodes (CMs). To choose the optimal path between CHs and BS (sink node), three fitness functions (residual energy of the CHs, distance between CHs and BS, and distance between CMs and CH) are chosen to model POA for optimal routing. The outcome of the proposed scheme (POA_Proposed) is energy-efficient, which enhances the network life. Lastly, a trust-aware node localization scheme (TANLS) is proposed. This work presents a novel model to resolve the shortcomings of existing localization techniques in WSNs. TANLS leverages trust-based approaches to accurately estimate the positions of sensor nodes while iv mitigating the impact of compromised or malevolent nodes on localization accuracy. The trust values of anchor nodes are evaluated against the reputation value of the anchor nodes. The highly trustworthy anchor nodes are subsequently selected as legitimate nodes for the localization process, and the unknown nodes get information from highly trustworthy anchor nodes to perform the NL. To enhance the performance of the localization, the Coati Optimization Algorithm (COA) is modeled using suitable fitness functions, and its performance is compared with the butterfly optimization algorithm (BOA) and particle swarm optimization (PSO). The simulations are performed using the COA_Proposed, BOA, and PSO. The comparative analysis among the COA_Proposed, BOA, and PSO demonstrates that the COA_Proposed outperforms the compared algorithms in terms of localization, localization computation time, and localization error. 2026-02-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22686 Title: DESIGN AND DEVELOPMENT OF SMART AND SECURE HEALTHCARE SYSTEM Authors: SHARMA, NIKHIL; Shambharkar, Prashant Giridhar (SUPERVISOR) Abstract: The digital transformation of healthcare through the Internet of Medical Things (IoMT) has enabled real-time monitoring, remote diagnosis, and intelligent health management. However, the proliferation of interconnected medical devices and the continuous exchange of sensitive health data exposes IoMT systems to significant cyber threats. Ensuring data confidentiality, integrity, and availability in resource-constrained environments remains a critical challenge. This thesis presents a novel security framework that integrates deep learning-based intrusion detection with blockchain technology to provide comprehensive protection for healthcare data. First, an intelligent intrusion detection system (IDS) is developed using advanced deep learning architectures that combine convolutional and recurrent neural networks with attention mechanisms. These models effectively capture both spatial and temporal features of network traffic, enabling the detection of complex attack patterns in heterogeneous IoMT environments. To address challenges of data tampering, centralized trust, and unauthorized access, a blockchain-based security architecture is introduced. This framework incorporates dynamic encryption schemes, robust access control policies, zero- knowledge proofs for privacy preservation, and a Practical Byzantine Fault Tolerant (PBFT) consensus mechanism. Decentralized storage using the InterPlanetary File System (IPFS) ensures immutability, scalability, and high availability of medical records. Furthermore, a Dynamic Adaptive Deep Reinforcement Learning (DA-DRL) framework is proposed to enhance AES (Advanced Encryption Standard) encryption by dynamically adjusting key generation in response to real-time threats. The multi-layered security design integrates AES, SHA-512, Non-Interactive Zero Knowledge Proofs (NIZKPs), PBFT, and Attribute-Based Access Control (ABAC), providing robust defense against diverse attack vectors. The Comprehensive experimental evaluation demonstrates the effectiveness and scalability of the proposed approach. The DA-DRL-AES-SHA-512 methodology achieves an encryption time of 0.0975 s, decryption time of 0.0846 s, throughput of 75.63 transactions/s, and network overhead of only 0.1289%. Energy consumption and computational overhead are reduced to 0.3664 J and 0.48%, respectively. The Secure and Dependable Bi-LSTM-GRU Intrusion Detection Framework (S- BiLSTMGRU-IDF) achieves 99.94% accuracy in binary classification and 99.89% accuracy in multiclass classification, outperforming state-of-the-art models by 0.6%-3.5%. The results establish that combining the predictive power of deep learning with the immutable and trustless nature of blockchain provides a resilient, scalable, and efficient IoMT security solution. This integrated framework significantly enhances real-time threat mitigation, ensures data integrity and confidentiality, and lays a practical foundation for secure healthcare applications in real-world deployments. 2025-09-01T00:00:00Z http://dspace.dtu.ac.in:8080/jspui/handle/repository/22671 Title: BLOCKCHAIN-BASED PATIENT CENTRIC HEALTHCARE ARCHITECTURE: SECURE MEDICAL DATA SHARING Authors: MISHRA, DEEPAK KUMAR Abstract: The rapid digitization of the healthcare sector is bringing forward new and serious concerns regarding patient confidentiality, patient data protection and integrity, data integration/interoperability and patient consent management. The traditional healthcare architecture models that have been used for decades are centralized, fragmented and insecure and are no longer able to meet the changing demands and the associated risk factors such as identity theft, violations of confidentiality and inefficiency in managing data. Addressing these concerns, this research systematically explores patient-centric blockchain-based healthcare architectures that provide secure, transparent, decentralized and patient-controlled data management. With an extensive literature review, this thesis identifies present research trends and gaps in blockchain-based healthcare solutions, showing the need for better security mea- sures and consent-management systems with higher interoperability standards. This the- sis proposes DiabeticChain, a novel blockchain-based solution made for managing diabetic healthcare data, improving patient control, data security and privacy using smart con- tracts, dynamic consent management and decentralized storage solutions. The proposed architecture integrates HL7/FHIR for semantic interoperability, modern cryptography for confidentiality and fine-grained access control, and a scalable Layer-2 blockchain plus decentralized storage to support real-time healthcare scenarios. Additionally, TotalSol, a novel multi-layer static analysis framework, is introduced to detect vulnerabilities in Ethereum-based smart contracts. The tool enhances the robust- ness and reliability of blockchain applications, by pointing critical security flaws. Because the proposed healthcare architecture relies on Ethereum smart contracts to enforce con- sent and access policies, TotalSol is developed to analyse these contracts and detect vul- nerabilities before deployment. Using Hyperledger Caliper and comparative studies, our experiments show that DiabeticChain outperforms the selected blockchain-based health- care frameworks on the evaluated performance metrics (throughput and latency). Fur- v thermore, the system considers ethical factors like patient data ownership and compliance with major regulations such as GDPR and HIPAA, combining its technical advancements with legal and ethical healthcare standards. 2026-02-01T00:00:00Z