Theses

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Browsing Theses by Supervisor "Das, Debasis"

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    Advanced Security Solutions for Large-Scale and Highly Dynamic Internet of Vehicles (IoV)
    (Indian Institute of Technology, Jodhpur, 2025-05-13) Das, Debasis
    Abstract The Internet of Vehicles (IoV) is a large-scale, highly dynamic network of heterogeneous communications.Maintaining a balance between authentication and privacy is crucial in such types of networks. Moreover, ensuring a high level of security through heterogeneous communication can result in significant computational overhead due to the numerous cryptographic operations required, which have higher execution times. Such trade-off between security and efficiency, are impractical in IoV networks, where the IoV devices have limited processing power and storage capacity. Furthermore, diverse communications generate substantial data that requires secure storage. Therefore, it is essential to minimize data storage and retrieval times to facilitate real-time communication in rapidly changing resource-constrained IoV network. These problems can be solved using lightweight security solutions that make sure users are authenticated without compromising their privacy. These solutions can also improve the trade-off between security and performance and make it easier to store and retrieve the large amounts of data that are created by different types of communication in the IoV network. The research is conducted in two phases. The first phase proposes advanced security solutions for the aforementioned issues using Tamper-Proof Devices (TPD). The TPD, a type of Hardware Security Module (HSM), stores cryptographic keys along with secret parameters and performs cryptographic operations such as authentication, encryption, decryption, and so on within the device. Initially, a lightweight conditionalprivacy preservation-based authentication mechanism is proposed, enhancing security through innovative key management mechanisms, such as Hard Key Update (HKU) and Soft Key Update (SKU). These mechanisms safeguard against key disclosure and ensure continuous protection even in the case of key compromise. The proposed solution significantly reduces computational and communication overhead using cryptographic techniques such as Message Authentication Codes (MACs), hash functions, and XOR operations. The research is further extended to mitigate the security-performance trade-off by implementing the idea of static and dynamic batch verification of multiple messages, leveraging lightweight cryptographic operations. The proposed method reduces computational overhead and ensures data integrity and confidentiality across the network. Additionally, it provides a solution for efficient and secure storage-retrieval of information while enhancing the responsiveness and efficiency of IoV services having dynamic and largescale environment. The proposed approach reduces latency and prevents bottleneck issues in a centralized system by integrating edge computing with the Vehicular Cloud (V-Cloud). Despite their robust design, TPDs can still be vulnerable to advanced physical attacks because of potential hardware flaws or inadequate protection against complex tampering techniques. Attackers can exploit these weaknesses to bypass security measures, potentially compromising the integrity of the device. This issue is resolved by introducing alternative approaches using Physical Unclonable Functions (PUF) devices. PUFs use hardware’s inherent physical irregularities to generate cryptographic keys on demand, making them nearly impossible to clone and highly resistant to tampering, unlike TPDs, which require secure key storage. In the second phase, PUF devices are used as secure hardware modules to design a highly secure and lightweight authentication scheme using a Challenge-Response Pair (CRP) mechanism. Furthermore, the research provides an effective solution for enhancing security on resource-constrained devices, where efficient and secure data handling is crucial. The framework uses PUFs to generate dynamic nonce and keys to address nonce reuse issue, without imposing significant computational or energy demands. Additionally, implementing this framework on low-power hardware platforms demonstrates its suitability for systems that require high security with efficient power usage, making it an ideal choice for securing resource constrained IoV devices. Comprehensive security analysis using BAN logic, Random OracleModel (ROM), and the ProVerif tool validates the technical depth of the proposed solutions, making them highly effective in countering a wide variety of security threats prevalent in IoV scenarios. The proposed methods are also evaluated against existing techniques, demonstrating improvements in terms of computational overhead, communication overhead, energy consumption, latency, and scalability.
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    Cutting-Edge Consensus Algorithms in Scalable and Interoperable Blockchain
    (Indian Institute of Tehcnology, Jodhpur, 02-05-2024) Das, Debasis
    The development of consensus algorithms is crucial for enhancing the scalability and interoperability of blockchain technology, which is key to its wider adoption. However, existing consensus algorithms, including Proof-of-Work (PoW), Proof-of-Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT), face challenges such as high consensus delay, low throughput, and limited transaction scalability, primarily due to their high computational and communication complexity. Moreover, these algorithms rely on third parties to communicate across diverse blockchain platforms, making blockchain vulnerable, which emphasizes the necessity for interoperable solutions. In addition, ensuring information privacy within blockchain systems remains a significant challenge. Therefore, in this thesis, we develop novel consensus algorithms while focusing on improving consensus delay and throughput. The proposed modified Practical Byzantine Fault Tolerance (mPBFT) and Lightweight Consensus Algorithm (LCA) consensus algorithms are the improvement over the PBFT consensus protocol, which reduces the computation and communication complexity. The GridChain (a reputation based consensus algorithm) and Weighted Node Selection Byzantine Fault Tolerance (WNS-BFT) are the reputation and trust-based consensus algorithms that establish trust among the nodes while keeping the performance high. Additionally, to improve the performance further, we have developed a technique called BlockTree (a nonlinear structured, scalable, and distributed ledger) and UnoShard (Harmonizing Scalability and Security) schemes that address the issue of limited scalability by enabling the parallel processing of transactions. Furthermore, to address the interoperability issues between the diverse blockchain platforms, we developed a technique called CrossLedger (a pioneer cross-chain asset transfer protocol) that eliminates the presence of third parties for communication. This thesis has also addressed the issue of information privacy in the proposed technique called zk-SGB, a privacy-preserved blockchain framework for energy trading in a smart grid using zero-knowledge-proof (ZKP) without compromising system integrity. Next, this thesis discusses the various applications based on blockchain technology, such as the Internet of Things (IoT), Intelligent Transportation Systems (ITS), and Energy Trading in the Smart Grid. We have developed three schemes for IoT using smart contracts. The first scheme is a BLISS (Blockchain-based Integrated Security System for Internet of Things (IoT) Applications), and the second scheme is SCAB-IoTA (Secure Communication and Authentication for IoT Applications using Blockchain. We have implemented these schemes on the Ethereum blockchain testnet. Additionally, we have developed a testbed using Raspberry Pi devices to show the efficiency of the proposed scheme. Furthermore, we have developed two schemes for ITS. The first scheme is a LBSV ( Lightweight Blockchain Security Protocol for Secure Storage and Communication in SDN-Enabled IoV), and the second scheme is SmartCoin (A Novel Incentive Mechanism for Vehicles in Intelligent Transportation System Based on Consortium Blockchain). The LBSV and SmartCoin work on mPBFT and LCA consensus algorithms, respectively. Furthermore, we have also developed three schemes for the smart grid application. The first scheme is a SmartGrid-NG (Blockchain Protocol for Secure Transaction Processing in the Next Generation Smart Grid), the second scheme is an ETradeChain (Blockchain-Based Energy Trading in the Local Energy Market (LEM) Using a Modified Double Auction Protocol), and the third scheme is zk-SGB (Privacy-Preserved Blockchain Framework for Energy Trading in Smart Grid Using Zero Knowledge Proof). The first two schemes are focused on trading and transaction processing and work on GridChain and two-level consensus algorithms, respectively. The zk-SGB is designed to achieve privacy in smart grid transaction processing and works on the WNS-BFT consensus algorithm. A Raspberry Pi 4 Model B devices based testbed has been developed for the implementation of ITS and smart grid schemes.
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    Next Generation Routing and Data Dissemination Techniques for Vehicular Ad-hoc Networks
    (Indian Institute of Technology, Jodhpur, 2024-07-23) Das, Debasis; Das, Sajal K.
    Consider a driver on a busy highway where their vehicle immediately receives alerts about a collision occurring three cars ahead, well before it comes into their line of sight. Or imagine a navigation system dynamically rerouting the driver to avoid a newly formed traffic jam just a few kilometers away. This is the capability offered by Vehicular Ad-hoc Networks (VANETs), which enable direct communication between vehicles and roadside infrastructure, establishing a real-time digital network that enhances road safety and optimizes traffic flow. However, deploying VANETs is complex. On actual roads, particularly in developing countries, traffic is highly heterogeneous ranging from cars and buses to motorcycles and auto-rickshaws, all traveling at varying speeds, frequently changing lanes unpredictably, and constantly joining or leaving the network. Traditional communication protocols, originally designed for relatively stable and homogeneous networks, struggle under these conditions: communication links frequently break, message flooding occurs in dense traffic scenarios (known as "broadcast storms"), and critical safety messages that require delivery within 100 milliseconds often fail to meet the stringent latency requirements. This thesis addresses these fundamental networking challenges (packet routing) by developing and validating next-generation routing and data dissemination techniques that maintain reliability despite high node mobility and diverse traffic conditions. Our contributions encompass three different directions: direction-aware forwarding mechanisms,metaheuristic-based clustering frameworks, and hypergraph-based communication models. We first propose three orientation-informed routing protocols, Cosine Similarity-Based Routing (CSBR), Orientation-Based QoS Routing (OBQR), and SDCast leveraging vehicles’movement direction and road context to improve data dissemination. Unlike conventional broadcast or shortest-path schemes, these protocols dynamically bias message forwarding along the direction of traffic flow, reducing redundant transmissions and avoiding relays on vehicles that are likely to move out of range. In CSBR, a cosine similarity metric between vehicle velocity vectors is used to select relay candidates, ensuring that only vehicles with aligned directions participate in rebroadcasting. OBQR builds on this by incorporating multi-constraint QoS metrics (link stability, transit delay, etc.) into routing decisions, using a weighted optimization to find routes that honor safety-critical latency and reliability requirements. Finally, SDCast introduces a hybrid Software-Defined Networking (SDN) architecture into the VANET: a two-tier controller system (a central controller working with local Roadside Units) that orchestrates cluster-based forwarding policies. To improve communication stability and QoS in dynamic conditions, we next develop advanced clustering and routing optimization techniques using metaheuristics. Two frameworks, i.e., MetaLearn and Multi-constraint Routing using Hybrid Metaheuristics (MRMH) are introduced to intelligently organize vehicles into semi-stable clusters and optimize multi-hop routes within and between these clusters. MetaLearn employs a hybrid learning approach: it uses meta-heuristic algorithms (Grey Wolf Optimization (GWO)) to bootstrap efficient clustering, and then applies reinforcement-learning principles (fast adaptation based on prior outcomes) to continually refine routing policies as conditions change. This enables the routing strategy to “learn” from the network’s behavior, quickly adapting to recurring traffic patterns (e.g., rush hour flows) and thereby improving long-term performance. MRMH, on the other hand, hybridizes multiple optimization techniques (GWO and Sequential Quadratic Programming (SQP)) to solve the routing problem under multiple constraints (such as latency, link durability, and bandwidth) simultaneously. By hybridizing metaheuristics methods, MRMH avoids the pitfalls of single-metaheuristic approaches (like premature convergence or high computational cost) and finds high-quality routes that satisfy all QoS requirements even as the network scales. Finally, we present an approach using Spatio-Temporal Information-Aware Hypergraph formulation that generalizes the traditional network graph model to a hypergraph structure. In a hypergraph, an edge (now called a hyperedge) can connect any number of vertices, which in our context means a communication event can directly involve multiple vehicles. This representation is paired with a deep learning-driven routing strategy that uses spatial (geographic/positional) and temporal (time-dependent) dynamics of vehicles and network conditions to make optimized decisions. By capturing higher-order relationships (beyond simple pairwise links) and feeding them into a deep learning algorithm, the network can better anticipate and adapt to changes. Further, we introduce a Software-Defined Fog Computing (SDFC) framework for VANETs,which pushes computational intelligence and control closer to the network edge (the vehicles and roadside units). This enables data processing and decision-making to occur in proximity to where data is generated. By doing so, fog computing can drastically reduce end-to-end communication delays and offload traffic from the core network. In our SDFC framework, VANET management functions (such as cluster formation,routing control, and load balancing) are distributed across a hierarchy of cloud, fog, and edge layers. This design improves scalability and reliability by avoiding single points of failure and by adapting to local conditions. To ensure experimental validations, all proposed techniques were implemented using standard VANET simulation tools and real hardware. Simulations leveraged frameworks like NS-2/NS-3 and OMNeT++ (with the Vehicle in Network Simulations (VEINS) open source library) for network-layer behavior, standard Vehicle-to-Everything (V2X) communication technology (IEEE 802.11p, Cellular-V2X) and Simulation of Urban Mobility (SUMO) for generating realistic vehicle mobility on road layouts imported from OpenStreetMap (openly-licensed data from national mapping agencies and other sources). We seeded simulations with actual city road maps and traffic patterns (including heterogeneous vehicle types, intersections and traffic lights) to closely mirror real-world conditions. Key performance metrics, end-to-end latency, packet delivery ratio, routing overhead, cluster membership time, throughput, and route discovery time were measured across a range of scenarios (urban environments, highways, varying vehicle densities from sparse to congested). Furthermore, the algorithms were tested on a physical testbed: our Duckietown setup (miniature autonomy test bed) and anedge computing platform with Raspberry Pi and JetsonNano devices (working as Onboard Units and Roadside Units) allowed us to verify that the protocols run within real-time constraints on resource-constrained hardware.