RAJAT CHAUDHARY

Scopus Publications

TalkWell: Natural Language Processing-Assisted Deep Learning Model for Real-Time Mental Health Monitoring
Krishna Goswami, Krisha Raval, Arpan Gadhiya, Rajat Chaudhary, Prajakta Sadaria
2026 International Conference on Recent Advances in Electrical Electronics Ubiquitous Communication and Computational Intelligence Raeeucci 2026, 2026
This paper introduces a truly novel way of determining mental health problems like anxiety, feelings of sadness, and stress in chat and audio communication, on the fly in online chats. The proposed model involves natural language processing (NLP) and voice analysis to understand the content as well as emotions expressed in text, in contrast to ordinary chatbots which rely only on keyword spotting. In this research, the system model operates in two phases; initially, the model interprets textual data for psychological signs using powerful language models, including but not limited to BERT models, and machine learning algorithms such as K-Nearest Neighbours, Convolutional Neural Networks, Support Vector Machines, as well as Long Short-Term Memory networks. Secondly, Deep Learning algorithms are adopted to extract the emotional component from the speech clips. This integrated proposed model performs a real-time mental health risk score, which can alert caregivers or identify appropriate support providers. Lastly, the performance analysis of the model is added as a browser plugin or linked into current chat platforms to allow for early identification of mental health challenges, raise awareness, and provide timely support to prevent catastrophic mental health emergencies.
A secure and privacy-preserving authentication framework for Connected and Autonomous Vehicles based on DRG-PBFT and zero-knowledge proof
Anjum Mohd Aslam, Aditya Bhardwaj, Rajat Chaudhary
Accident Analysis and Prevention, 2025
Digital twins-enabled game theoretical models and techniques for metaverse Connected and Autonomous Vehicles: A survey
Anjum Mohd Aslam, Rajat Chaudhary, Aditya Bhardwaj, Neeraj Kumar, Rajkumar Buyya
Journal of Network and Computer Applications, 2025
Quantum-resilient blockchain-enabled secure communication framework for connected autonomous vehicles using post-quantum cryptography
Anjum Mohd Aslam, Aditya Bhardwaj, Rajat Chaudhary
Vehicular Communications, 2025
Proximal Policy Optimization based sum rate maximization scheme for STAR-RIS-assisted vehicular networks underlaying UAV
Shivam Chaudhary, Ishan Budhiraja, Rajat Chaudhary, Sahil Garg, B. Choi, Mubarak Alrashoud
Alexandria Engineering Journal, 2025
The consumer electronics industry is undergoing significant transformations due to the ongoing advancements in mobile Internet technology, 5G, Internet of Things (IoT), artificial intelligence (AI), and other emerging technologies. Additionally, the development of intelligent electronic products is accelerating. Higher communication quality is required as a result of the convergence of consumer electronics and developing technologies. The low cost and simple deployment of the Simultaneous Transmitting and Reflecting Reconfigurable intelligent surface (STAR-RIS) can show considerable possibilities. STAR-RIS is a well-known for potentially improving wireless network performance. STAR-RIS enables users positioned on different sides of the surfaces to simultaneously receive signals that are transmitted or reflected. In this article, we examines the difficulties of sum rate maximization in a STAR-RIS assisted downlink network with NOMA assistance, where the incident signal energy at STAR-RIS is divided into two halves for transmitting and reflecting. This dynamic nature of wireless networks makes it challenging to tackle the sum rate maximization problem using the conventional approach of convex optimization techniques. To overcome the difficulties of the sum rate, the proposed scheme uses the Proximal Policy Optimization (PPO) based algorithm based on Deep Reinforcement Learning (DRL) which optimizes the beamforming vectors at the base station and the coefficient matrices and symbol rate at the STAR-RIS. Finally, the performance evaluation demonstrates that the proposed scheme maximizes the system sum rate while considering time-varying channels into account, and the PPO-based algorithm performs better than the Deep Deterministic Policy Gradient (DDPG) algorithm. Also, the results shows that the proposed scheme has 22.05%, 35.12% and 48.9% higher sum rate as compared to DDPG, Zero forcing and random.
A comprehensive survey on software-defined networking for smart communities
Rajat Chaudhary, Gagangeet Singh Aujla, Neeraj Kumar, Pushpinder Kaur Chouhan
International Journal of Communication Systems, 2025
Summary The need to provide services closer to the end‐user proximity leads to the exchange of a large volume of data generated from the smart devices deployed at different geo‐distributed sites. The massive amount of data generated from the smart devices need to be transmitted, analyzed, and processed. This requires seamless data exchanges among geo‐separated nodes, which results in a considerable burden on the underlying network infrastructure and can degrade the performance of any implemented solution. Therefore, a dynamic, agile, and programmable network management paradigm is required. To handle the challenges mentioned above, software‐defined networking (SDN) gained much attention from academia, researchers, and industrial sectors. Shifting the computational load from forwarding devices to a logically centralized controller is a dream of every network operator who wants to have complete control and global visibility of the network. Also, the concept of network functions virtualization (NFV) in SDN controller is required to increase resource utilization efficiency. Thus, in this paper, a comprehensive survey on SDN for various smart applications is presented. This survey covers the infrastructural details of SDN hardware and OpenFlow switches, controllers, simulation tools, programming languages, open issues, and challenges in SDN implementation with advanced technologies such as 5G and microservices. In addition, the challenges on the control plane and data plane are highlighted in detail, such as fault tolerance, routing, scheduling of flows, and energy consumption on OpenFlow switches. Finally, various open issues and challenges future scope of SDN are discussed and analyzed in the proposal.
An AIoT-Enabled Digital Twin CAVs With a DRL-Based Framework for Trajectory Planning
Anjum Mohd Aslam, Rajat Chaudhary, Aditya Bhardwaj
IEEE Transactions on Intelligent Transportation Systems, 2025
The convergence of intelligent transportation systems and urban informatics has given rise to the deployment of connected and autonomous vehicles (CAVs) which offers the potential to enhance the safety and efficiency. However, the increasing volume of automobiles on highways causes frequent and often mismanaged multi-lane changing (MLC), coupled with inadequate trajectory planning. This results in traffic congestion and accidents, which leads to substantial societal losses. Additionally, these issues raise substantial concerns about environmental sustainability, safety, and traffic efficiency, necessitating innovative solutions. To address these challenges, we leverage the transformative capabilities of Artificial Intelligence of Things (AIoT) and introduce a deep reinforcement learning (DRL)-based non-cooperative game approach, named Nash-SAC (Soft Actor-Critic), enabled by digital twin technology, to facilitate optimized decision-making in CAVs. We consider various driving behaviors and social interaction characteristics that influence driving safety, ride comfort, and travel efficiency. The efficacy of the proposed framework is validated through simulations using the Python-based Highway-env simulator and Matlab/Simulink. The simulation analysis reveals that the proposed algorithm attains 22.48%, 40.32%, and 52.02% reductions in average delay, and achieves 39.50%, 58%, and 64.46% lesser computational time compared to the Twin-Delayed Deep Deterministic Policy Gradient (TD3), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN) algorithms, respectively.
Energy and Latency Tradeoff for STAR-IRS assisted Vehicle Road Cooperative System in Carbon Intelligent IIoT Leveraging Quantized Federated Reinforcement Learning
Shivam Chaudhary, Ishan Budhiraja, Rajat Chaudhary
IEEE Internet of Things Journal, 2025
The rapid expansion of the Industrial Internet of Things (IIoT) in vehicular networks has significantly increased the demand for energy-efficient and low-latency communication to support intelligent transportation systems. However, the associated carbon footprint poses major challenges to sustainable development. To resolve these problems, we propose an Energy-Efficient and Latency-Minimizing Simultaneously Transmitting and Reflecting-Intelligent Reflecting Surface (STAR-IRS)-Assisted Vehicle-Road Cooperative System (VRCS) within a Carbon-Aware IIoT environment, leveraging Quantized Federated Reinforcement Learning (Q-FRL). The STAR-IRS dynamically enhances signal strength and energy efficiency by adjusting transmission and reflection coefficients, ensuring robust connectivity in complex vehicular environments. Q-FRL helps adjust STAR-IRS settings in real time while reducing computational complexity through quantized decision-making. Adaptive quantized DDPG improves energy efficiency, whereas adaptive quantized DDQN minimizes latency under carbon-aware constraints. Simulation results substantiate the performance of the proposed framework, demonstrating superior energy efficiency, lower latency, and reduced carbon emissions. Specifically, the adaptive quantized DDPG (AQ-DDPG) reduces energy consumption by 31.57%, while gradient quantized DDPG (GQ-DDPG) and fixed (4-bit) DDPG improve it by 16.84% and 6.31%, respectively, compared to fixed (2-bit) DDPG. Furthermore, AQ-DDPG reduces vehicle carbon emissions by 15% and 10% compared to FQ-DDPG and GQ-DDPG, respectively.
Quantum Deep Q Network Technique for Latency Minimization in STAR-RIS assisted VRCS
Shivam Chaudhary, Ishan Budhiraja, Rajat Chaudhary, Neeraj Kumar, Isaac Woungang
Proceedings IEEE Global Communications Conference Globecom, 2025
The increasing demand for ultra-reliable and low-latency communication (URLLC) in vehicle road cooperation systems (VRCS) has propelled the development of intelligent and efficient optimization techniques. This paper presents a Quantum Deep Q-Network (QDQN) based approach for minimizing latency in a Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) enabled VRCS. STAR-RIS improves signal coverage and energy efficiency by simultaneously serving users in both transmission and reflection modes. However, latency optimization remains a critical challenge due to dynamic environments and computational complexity. The proposed QDQN technique integrates quantum computing principles with deep reinforcement learning (DRL) to accelerate decision making and optimize resource allocation in real time. Using quantum parallelism and entanglement, QDQN reduces convergence time while effectively learning the dynamic state of the communication environment. The simulation results demonstrate that the proposed method achieves a significant latency reduction compared to conventional DRL and classical Q-learning techniques. This study highlights the potential of quantum-enhanced reinforcement learning for future URLLC applications in intelligent vehicular networks.
Asynchronous Federated Learning Technique for Latency Reduction in STAR-RIS Enabled VRCS
Shivam Chaudhary, Ishan Budhiraja, Rajat Chaudhary, Neeraj Kumar, Sujit Biswas
IEEE International Conference on Communications, 2025
With the advent of smart and autonomous vehicles, a number of novel data-intensive and latency-critical vehicular communication applications have emerged. However, dynamic vehicular mobility and urban environments introduce severe propagation challenges, leading to increased latency. In order to reduce latency in Vehicle Road Cooperative Systems (VRCS), this research introduces a unique architecture that combines Asynchronous Federated Learning (AFL) with Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS). The proposed system leverages a Markov Decision Process (MDP)-based optimization framework to minimize latency by jointly optimizing STAR-RIS elements and offloading decisions. Our approach allows vehicles to asynchronously update global models, ensuring robust learning while adapting to dynamic network conditions. The simulation results show that the recommended strategy provides at least a 20 % reduction in latency in AFL when compared to FL.
COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks
Gagangeet Singh Aujla, Anish Jindal, Kuljeet Kaur, Sahil Garg, Rajat Chaudhary, Hongjian Sun, Neeraj Kumar
IEEE International Conference on Communications, 2025
A Cooperative Game Approach for Multi-lane Merging Decision-making Algorithm for CAVs
Anjum Mohd Aslam, Aditya Bhardwaj, Rajat Chaudhary, Ishan Budhiraja
ACM International Conference Proceeding Series, 2024
Probing the Convergence of Vehicular Edge Metaverse and 6G: Blockchain-enabled Framework
Shree Kumar, Rajat Chaudhary, Ishan Budhiraja
International Symposium on Advanced Networks and Telecommunication Systems Ants, 2024
Predicting Hotel Booking Cancellations using Machine Learning Techniques
Aditya Bhardwaj, Tanishq Yadav, Rajat Chaudhary
2024 15th International Conference on Computing Communication and Networking Technologies Icccnt 2024, 2024
Parameterize Deep Q Network for Backscattering Data Capture with Multiple UAVs
Deepak Gupta, Ishan Budhiraja, Rajat Chaudhary, Neeraj Kumar
IEEE International Conference on Communications, 2024
FogIoT: Fog Computing based Security Frameworks for Software-defined IoT
Anjum Mohd Aslam, Rajat Chaudhary, Ishan Budhiraja, Vishal Sharma
International Symposium on Advanced Networks and Telecommunication Systems Ants, 2024
Path Planning for Self-driving Vehicles using Metaverse in 6G era with AI-enabled Networks
Rachit Choudhary, Rahul Prakash, Rajat Chaudhary, Ishan Budhiraja, Amit Choudhary
International Symposium on Advanced Networks and Telecommunication Systems Ants, 2024
STAR-RIS Based Resource Scheduling and Mode Selection for Drone Assisted 5G Communications
Shivam Chaudhary, Anushka Nehra, Ishan Budhiraja, Rajat Chaudhary, Abhay Bansal
IEEE INFOCOM 2024 IEEE Conference on Computer Communications Workshops INFOCOM Wkshps 2024, 2024
Mitigating Cross-Site Request Forgery Vulnerabilities: An Examination of Prevention Systems
Yuvraj Singh, Paranjay Goel, Shubhani Aggarwal, Rajat Chaudhary, Ishan Budhiraja
International Symposium on Advanced Networks and Telecommunication Systems Ants, 2024
Adaptive Trajectory Planning in Autonomous Vehicles: A Hierarchical Reinforcement Learning Approach with Soft Actor-Critic
Amit Kumar Sharma, Amit Choudhary, Rajat Chaudhary, Aditya Bhardwaj, Anjum Mohd Aslam
International Symposium on Advanced Networks and Telecommunication Systems Ants, 2024
Quantum Federated Reinforcement-Learning-Based Joint Mode Selection and Resource Allocation for STAR-RIS-Aided VRCS
Shivam Chaudhary, Ishan Budhiraja, Rajat Chaudhary, Neeraj Kumar, Deepak Garg, Abdullah M. Almuhaideb
IEEE Internet of Things Journal, 2024
Fingerprint-Based Multifactor Authentication For Bank Transaction
Mananjeet Kaur, Shreya Kaistha, Shubhani Aggarwal, Ishan Budhiraja, Rajat Chaudhary, Abhay Bindle
International Symposium on Advanced Networks and Telecommunication Systems Ants, 2024
AIDA—A holistic AI-driven networking and processing framework for industrial IoT applications
Hamza Chahed, Muhammad Usman, Ayan Chatterjee, Firas Bayram, Rajat Chaudhary, Anna Brunstrom, Javid Taheri, Bestoun S. Ahmed, Andreas Kassler
Internet of Things Netherlands, 2023
SecGreen: Secrecy Ensured Power Optimization Scheme for Software-Defined Connected IoV
Rajat Chaudhary, Neeraj Kumar
IEEE Transactions on Mobile Computing, 2023
Metaverse for 6G and Beyond: The Next Revolution and Deployment Challenges
Anjum Mohd Aslam, Rajat Chaudhary, Aditya Bhardwaj, Ishan Budhiraja, Neeraj Kumar, Sherali Zeadally
IEEE Internet of Things Magazine, 2023
Container-based Migration Technique for Fog Computing Architecture
Aditya Bhardwaj, Umesh Gupta, Ishan Budhiraja, Rajat Chaudhary
2023 International Conference for Advancement in Technology Iconat 2023, 2023
Blockchain-based Robust SDN Framework for Digital Twin-Enabled IoT Networks
Aditya Bhardwaj, Rajat Chaudhary, Anjum Mohd Aslam, Ishan Budhiraja
IEEE Vehicular Technology Conference, 2023
P-HrDPS: Security-Aware Heart Disorder Prediction Support Model Using Ensemble Learning Technique
Madhuri Gupta, Deepika Pantola, Deepanshi, Ishan Budhiraja, Rajat Chaudhary
2023 IEEE International Conference on Communications Workshops Sustainable Communications for Renaissance Icc Workshops 2023, 2023
Energy-Efficient THz NOMA-Enabled Small Cells Underlaying Macrocell Using Reinforcement Learning
Varun Kumar, Ishan Budhiraja, Akansha Singh, Rajat Chaudhary, Srinivas Aluvala
International Symposium on Advanced Networks and Telecommunication Systems Ants, 2023
Improving the Transmission Power of UAVs with Intelligent Reflecting Surfaces in V2X
Shivam Chaudhary, Rajat Chaudhary, Ishan Budhiraja, Aditya Bhardwaj, Anushka Nehra, Sheshikala Martha
IEEE Vehicular Technology Conference, 2023
Security Reassessing in UAV-Assisted Cyber-Physical Systems Based on Federated Learning
Prakhar Consul, Ishan Budhiraja, Rajat Chaudhary, Neeraj Kumar
Proceedings IEEE Military Communications Conference MILCOM, 2022
FLBCPS: Federated Learning based Secured Computation Offloading in Blockchain-Assisted Cyber-Physical Systems
Prakhar Consul, Ishan Budhiraja, Rajat Chaudhary, Deepak Garg
Proceedings 2022 IEEE ACM 15th International Conference on Utility and Cloud Computing Ucc 2022, 2022
EnFlow: An Energy-Efficient Fast Flow Forwarding Scheme for Software-Defined Networks
Rajat Chaudhary, Neeraj Kumar
IEEE Transactions on Intelligent Transportation Systems, 2021
CURE: An effective covid-19 remedies based on machine learning prediction models
Ceur Workshop Proceedings, 2021
A systematic review on the identification and diagnosis of clinical characteristics of covid-19 patients
Ceur Workshop Proceedings, 2021
DiLSe: Lattice-Based Secure and Dependable Data Dissemination Scheme for Social Internet of Vehicles
Amuleen Gulati, Gagangeet Singh Aujla, Rajat Chaudhary, Neeraj Kumar, Mohammad S. Obaidat, Abderrahim Benslimane
IEEE Transactions on Dependable and Secure Computing, 2021
PARC: Placement Availability Resilient Controller Scheme for Software-Defined Datacenters
Rajat Chaudhary, Neeraj Kumar
IEEE Transactions on Vehicular Technology, 2020
LOADS: Load Optimization and Anomaly Detection Scheme for Software-Defined Networks
Rajat Chaudhary, Neeraj Kumar
IEEE Transactions on Vehicular Technology, 2019
Blockchain for smart communities: Applications, challenges and opportunities
Shubhani Aggarwal, Rajat Chaudhary, Gagangeet Singh Aujla, Neeraj Kumar, Kim-Kwang Raymond Choo, Albert Y. Zomaya
Journal of Network and Computer Applications, 2019
BEST: Blockchain-based secure energy trading in SDN-enabled intelligent transportation system
Rajat Chaudhary, Anish Jindal, Gagangeet Singh Aujla, Shubhani Aggarwal, Neeraj Kumar, Kim-Kwang Raymond Choo
Computers and Security, 2019
Lattice-based public key cryptosystem for internet of things environment: Challenges and solutions
Rajat Chaudhary, Gagangeet Singh Aujla, Neeraj Kumar, Sherali Zeadally
IEEE Internet of Things Journal, 2019
DLRS: Deep Learning-Based Recommender System for Smart Healthcare Ecosystem
Gagangeet Singh Aujla, Anish Jindal, Rajat Chaudhary, Neeraj Kumar, Sahil Vashist, Neeraj Sharma, Mohammad S. Obaidat
IEEE International Conference on Communications, 2019
SAFE: SDN-Assisted Framework for Edge-Cloud Interplay in Secure Healthcare Ecosystem
Gagangeet Singh Aujla, Rajat Chaudhary, Kuljeet Kaur, Sahil Garg, Neeraj Kumar, Rajiv Ranjan
IEEE Transactions on Industrial Informatics, 2019
SLOPE: A Self Learning Optimization and Prediction Ensembler for Task Scheduling
Lohit Kapoor, Anish Jindal, Abderrahim Benslimane, Gagangeet Singh Aujla, Rajat Chaudhary, Neeraj Kumar, Albert Y. Zomaya
International Conference on Wireless and Mobile Computing Networking and Communications, 2018
SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems
Anish Jindal, Gagangeet Singh Aujla, Neeraj Kumar, Rajat Chaudhary, Mohammad S. Obaidat, Ilsun You
IEEE Network, 2018
An Ensembled Scheme for QoS-Aware Traffic Flow Management in Software Defined Networks
Gagangeet Singh Aujla, Rajat Chaudhary, Neeraj Kumar, Ravinder Kumar, Joel J. P. C. Rodrigues
IEEE International Conference on Communications, 2018
Deep learning-based content centric data dissemination scheme for internet of vehicles
Amuleen Gulati, Gagangeet Singh Aujla, Rajat Chaudhary, Neeraj Kumar, Mohammad S. Obaidat
IEEE International Conference on Communications, 2018
LaCSys: Lattice-based cryptosystem for secure communication in smart grid environment
Rajat Chaudhary, Gagangeet Singh Aujla, Neeraj Kumar, Ashok Kumar Das, Neetesh Saxena, Joel J. P. C. Rodrigues
IEEE International Conference on Communications, 2018
EnergyChain: Enabling energy trading for smart homes using blockchains in smart grid ecosystem
Shubhani Aggarwal, Rajat Chaudhary, Gagangeet Singh Aujla, Anish Jindal, Amit Dua, Neeraj Kumar
Proceedings of the 1st ACM Mobihoc Workshop on Networking and Cybersecurity for Smart Cities Smartcitiessecurity 2018, 2018
SDN-Enabled Multi-Attribute-Based Secure Communication for Smart Grid in IIoT Environment
Rajat Chaudhary, Gagangeet Singh Aujla, Sahil Garg, Neeraj Kumar, Joel J. P. C. Rodrigues
IEEE Transactions on Industrial Informatics, 2018
LSCSH: Lattice-Based Secure Cryptosystem for Smart Healthcare in Smart Cities Environment
Rajat Chaudhary, Anish Jindal, Gagangeet Singh Aujla, Neeraj Kumar, Ashok Kumar Das, Neetesh Saxena
IEEE Communications Magazine, 2018
Optimized Big Data Management across Multi-Cloud Data Centers: Software-Defined-Network-Based Analysis
Rajat Chaudhary, Gagangeet Singh Aujla, Neeraj Kumar, Joel J.P.C. Rodrigues
IEEE Communications Magazine, 2018
SecSVA: Secure Storage, Verification, and Auditing of Big Data in the Cloud Environment
Gagangeet Singh Aujla, Rajat Chaudhary, Neeraj Kumar, Ashok Kumar Das, Joel J. P. C. Rodrigues
IEEE Communications Magazine, 2018
LEASE: Lattice and ECC-Based Authentication and Integrity Verification Scheme in E-Healthcare
Amit Dua, Rajat Chaudhary, Gagangeet Singh Aujla, Anish Jindal, Neeraj Kumar, Joel J.P.C. Rodrigues
Proceedings IEEE Global Communications Conference Globecom, 2018
Network Service Chaining in Fog and Cloud Computing for the 5G Environment: Data Management and Security Challenges
Rajat Chaudhary, Neeraj Kumar, Sherali Zeadally
IEEE Communications Magazine, 2017
Data Offloading in 5G-Enabled Software-Defined Vehicular Networks: A Stackelberg-Game-Based Approach
Gagangeet Singh Aujla, Rajat Chaudhary, Neeraj Kumar, Joel J. P. C. Rodrigues, Alexey Vinel
IEEE Communications Magazine, 2017

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