Artificial Intelligence, Autonomous Agent and Multiagent Systems, Intelligent Transportation System, Internet of Vehicles, Distributed Systems.
14
Scopus Publications
Scopus Publications
ELAS-FG: Efficient Lightweight Authentication Scheme in Factor-Graph-Based Communication for VANET Poulami Dalapati, Priya Jyotiyana, Jayaprakash Kar IEEE Internet of Things Journal, 2026 Vehicular Ad-Hoc Networks (VANETs) enable secure, real-time communication among vehicles and roadside infrastructure, improving traffic safety and system efficiency. However, VANETs face significant security and performance challenges due to their dynamic topology, real-time constraints, and resource limitations. This paper presents a novel efficient lightweight authentication scheme in certificateless(ELAS-FG) setting for VANETs, utilizing Factor Graph-based (FG) communication models to optimize efficiency and scalability. The proposed scheme integrates conditional probability distributions and message-passing algorithms from factor graph theory with cryptographic primitives to reduce computational and communication overhead. Performance analysis demonstrates that our method achieves robust security guarantees, including mutual authentication, integrity, and resistance against replay and impersonation attacks, while maintaining low latency and minimal resource consumption. Comparative analysis with existing schemes highlights the superior efficiency and practicality of our approach for real-world VANET environments.
Network Digital Twin Toward Networking, Telecommunications, and Traffic Engineering: A Survey Reza Poorzare, Dimitris N. Kanellopoulos, Varun Kumar Sharma, Poulami Dalapati, Oliver P. Waldhorst IEEE Access, 2025 Network Digital Twin (NDT) is an evolving technology that provides a framework through which a network administrator can have a virtual representation of a computer network. As a result, analysis, monitoring, testing, running new protocols, and more can be performed using the NDT before the final deployment of the developed approach. In this way, the consequences of direct deployment and the negative impact on network operations can be avoided. Telecommunications, along with traffic engineering as one of its critical components, play a prominent role across various networking domains, including Internet service providers, data centers, cellular networks, intelligent transportation systems, and smart cities. In this context, NDT has the potential to serve as a key enabler for optimizing these domains by providing a digital framework, which can facilitate the evaluation and enhancement of different scenarios. Accordingly, this paper presents a comprehensive survey on how NDT can facilitate advancements in network traffic engineering across a wide range of networking domains. First, we start with an in-depth analysis of the evolution of the network digital twin technology and provide a comparison with simulation tools. Next, we examine the role of NDT in various networking and telecommunication domains. We also explore the applicability of NDT technology from a traffic engineering perspective across different network types. Subsequently, we highlight key open research questions and potential future directions that warrant further investigation. Finally, we conclude by outlining the promising future trajectory of NDT within the aforementioned domains.
Lifelong Dynamic Timed A∗ (LTA∗) for Fastest Path Retrieval in Congested Road Networks Using Predicted Speeds Kartikey Sondhi, Poulami Dalapati, Saurabh Kumar International Conference on Vehicle Technology and Intelligent Transport Systems Vehits Proceedings, 2024 : Efficient transportation systems are crucial for the ever-growing smart cities. With the increasing urbanization and growth in vehicular traffic, congestion has become a significant challenge. This research paper addresses the critical issue of identifying the fastest, least congested path in road transport networks, aiming to enhance overall travel efficiency and reduce the negative impact of traffic congestion. The study employs an improved version of the Lifelong Planning A* (LPA*) that helps find the fastest route between two points in dynamic changing environments. The proposed methodology is called the Lifelong Dynamic Timed A ∗ ( LTA ∗ ) algo-rithm with an optimal bound weight factor integrated with it to make the search more guided and efficiently predict optimized traffic paths to provide real-time recommendations. To validate the effectiveness of the developed algorithm, extensive simulations and case studies are conducted on a small area in Washington as well as on Grid Worlds. The experimental results show that LTA*, within accurate weight bounds, always managed to find the fastest path, and in some cases, the time taken was close to half of that produced by A*.
Enabling sustainable technologies using the Internet of Things for Industry 4.0 Poulami Dalapati, Saurabh Kumar Intelligent Green Communication Network for Internet of Things, 2023 In the current scenario, with the advent of the fourth industrial revolution, there is a shift in industrial operations by implementing the Internet of Things. The Internet of Things provides a platform for efficient communication and computation needs of the industries to meet the real-time standards of the market. However, to establish a smooth operation, there is a need to embed intelligence in the machines to achieve automation with the amalgamation of learning. This chapter discusses the fourth industrial revolution and the importance of sustainability in it. Further, it discusses the role of IoT in achieving sustainable development in Industry 4.0 with the help of artificial intelligence, machine learning, deep learning, and big data analytics for real-time service delivery in different application domains. Finally, a case study on intelligent transportation system is presented to emphasize the role of these technologies in the current generation applications to realize the vision of Industry 4.0.
Bat Algorithm for Congestion Alleviation in Power System Network Kaushik Paul, Niranjan Kumar, Poulami Dalapati Technology and Economics of Smart Grids and Sustainable Energy, 2021 Congestion in power transmission lines is designated as one of the critical issues in the deregulated power system scenario. The System Operator (SO) bears the responsibility to manage the congestion in order to ensure a secure and reliable operation of the power system framework. This article proposes a Congestion Management (CM) strategy based on the generator rescheduling approach using Bat Algorithm (BA). BA is one of the recent nature inspired optimization approaches based on the echolocation strategy adopted by the bats in search of prey. In the proposed CM scheme the BA is used to minimize the congestion cost with the optimal rescheduling of the active power output of the generators. The participation of the generators in the CM is accomplished considering the generator sensitivity values. The potency of the proposed method is tested on 39-bus New England framework, IEEE 30 bus system, IEEE 118 bus system and a comparative analysis is established with other recent optimization approaches. The outcomes obtained with the proposed BA for CM outperforms the outcomes achieved with other algorithms. The proposed approach ensures a better convergence profile avoiding the traps into local minima and also aids the SO to manage congestion efficiently.
Congestion control by optimal engagement of distribution generation using hybrid evolutionary algorithm Kaushik Paul*, , Niranjan Kumar, Poulami Dalapati, , and International Journal of Innovative Technology and Exploring Engineering, 2019 The power system congestion is treated as a vital issue in the restructured topology of the power system. The analysis of appropriate technique to control congestion is of preeminent interest. This paper proposes a congestion controlling scheme with the optimal placement and sizing of the Distributed Generation (DG) so as to ensure an optimal power flow in the power system network. A multi-objective framework is formulated for the proposed approach considering the operating cost, Voltage Stability Index (VSI) and the system losses. A hybrid optimization technique is proposed involving Improved Genetic Algorithm (IGA) and Bat Algorithm (BA) to optimize the objectives proposed in this research. The efficiency of the proposed methodology is verified using IEEE 33 and 69 bus systems. A comparative analysis is established between the outcomes obtained with hybrid IGA-BA and Particle Swarm Optimization (PSO) technique. The output obtained clarifies that by combining IGA and BA, greater efficiency is achieved compared to the PSO algorithm output.
Real-time collision handling in railway transport network: an agent-based modeling and simulation approach Poulami Dalapati, Abhijeet Padhy, Bhawana Mishra, Animesh Dutta, Swapan Bhattacharya Transportation Letters, 2019 Advancement in intelligent transportation systems with complex operations requires autonomous planning and management to avoid collisions in day-to-day traffic. As failures and/or inadequacy in traffic safety systems are life-critical, such collisions must be detected and resolved in an efficient way to manage continuously rising traffic. In this paper, we address different types of collision scenarios along with their early detection and resolution techniques in a complex railway system. In order to handle collisions dynamically, a novel agent-based solution approach is proposed using the idea of max-sum algorithm, where each agent communicates and cooperates with others to generate a good feasible solution that keeps the system safe, i.e. collision free. We implement the proposed mechanism in Java Agent DEvelopment Framework (JADE). The simulation results are evaluated with exhaustive experiments and compared with different existing methods to show the efficiency of our proposed approach.
