RAJAT CHAUDHARY

EDUCATION

Ph.D. [Computer Science & Engineering], July 2016 - February 2021
Thapar Institute of Engineering & Technology, Patiala (Punjab), India.
Thesis Topic: Software-Defined Networking based Control Flow Optimization for Multi-Cloud
Environment
Supervisor: Prof. (Dr.) Neeraj Kumar
M.Tech. in Information Security & Management, July 2010 - July 2012 Percentage: 74%
Dehradun Institute of Technology, Uttrakhand Technical University, Dehradun (India).
B.Tech. in Computer Science & Engineering, July 2006 - June 2010 Percentage: 63%
Vidya College of Engineering, Meerut Affiliated with UPTU, Lucknow (India)

RESEARCH INTERESTS

Software Defined Networking, Time-Sensitive Networks, Security and Privacy, Energy-Efficient Networking, Resilience

Scopus Publications

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, and Rajkumar Buyya
Elsevier BV



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 and Mubarak Alrashoud




Quantum-resilient blockchain-enabled secure communication framework for connected autonomous vehicles using post-quantum cryptography
Anjum Mohd Aslam, Aditya Bhardwaj, and Rajat Chaudhary
Elsevier BV



A comprehensive survey on software-defined networking for smart communities
Rajat Chaudhary, Gagangeet Singh Aujla, Neeraj Kumar, and Pushpinder Kaur Chouhan
Wiley



A Cooperative Game Approach for Multi-lane Merging Decision-making Algorithm for CAVs
Anjum Mohd Aslam, Aditya Bhardwaj, Rajat Chaudhary, and Ishan Budhiraja
ACM



Predicting Hotel Booking Cancellations using Machine Learning Techniques
Aditya Bhardwaj, Tanishq Yadav, and Rajat Chaudhary
IEEE
This study introduces a comprehensive machine learning system to enhance the accuracy of hotel reservation cancellation forecasts. By leveraging a diverse dataset encompassing factors like booking timelines, customer demographics, room preferences, and past cancellation rates, we employ advanced machine learning techniques such as Logistic Regression (LR), Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to craft predictive models. Rigorous evaluation using metrics like recall, F1-score, accuracy, and precision demonstrated the effectiveness of these models. The ANN achieved the highest performance with an accuracy of 99.02%, a recall of 97%, a precision of 99%, and an F1-Score of 99%. DTC and RFC also showed strong results, while LR and SVM had relatively lower performance. Integrating these models into a cohesive system allows for real-time prediction and management of booking cancellations, providing hotel management with a valuable tool for optimizing operations, enhancing revenue forecasting, and improving customer satisfaction. This study highlights the potential of machine learning to transform hotel booking management through data-driven decision-making.


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, and Abdullah M. Almuhaideb
Institute of Electrical and Electronics Engineers (IEEE)



Parameterize Deep Q Network for Backscattering Data Capture with Multiple UAVs
Deepak Gupta, Ishan Budhiraja, Rajat Chaudhary, and Neeraj Kumar
IEEE
The battery issue with Internet of Things (IoT) devices has been identified as a feasible solution in the shape of forthcoming backscatter communication technology. Wireless sensor networks, for example, that use backscatter communication technology can effectively monitor remote situations without requiring regular battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To overcome this issue, we proposed a solution that employs several unmanned aerial vehicles (UAVs) to aid in data collection. These UAVs may approach the backscatter sensor node (BSN), activate it, and then collect data. Our goal is to lower the overall flight duration required for rechargeable UAVs after the data collection mission is completed. The simulation results show that the proposed algorithms PDQN may outperform multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) approaches.


STAR-RIS Based Resource Scheduling and Mode Selection for Drone Assisted 5G Communications
Shivam Chaudhary, Anushka Nehra, Ishan Budhiraja, Rajat Chaudhary, and Abhay Bansal
IEEE
For a range of future vehicle usage in 5G, communication between vehicle road cooperative systems (VRCS) is cru-cial. With the advent of 5G deployments, vehicle-to-everything (V2X) communications is enabled to boost network density, optimize transmission mode selection, expand resource capacity, and provide near-ubiquitous connectivity between vehicles with extremely dependable and low latency. On the other hand, mode selection and resource scheduling in V2X communications come with intrinsic difficulties. In this paper, we present a joint optimization approach to address the problems associated with scheduling resources and choosing a transmission mode for the drone-mounted Simultaneous Transmitting and Reflecting-Intelligent Reflecting Surface (STAR- RIS) infrastructure for virtual reality surveillance. The Markov decision process is used to formulate the problem, and the deep reinforcement learning (DRL)-based STAR-RIS drone mounted approach maximizes vehicle-to-infrastructure user capacity while meeting the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs. Moreover, a DRL approach called PDQN (Parametrised Deep Q Network) is employed to generate resilient models while considering the training constraints of local D RL models. The results of the simulation validate the PDQN DRL algorithm's superiority for V2V pairs and show that the proposed DRL-based algorithm outperforms current baseline techniques.


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, and Andreas Kassler
Elsevier BV



SecGreen: Secrecy Ensured Power Optimization Scheme for Software-Defined Connected IoV
Rajat Chaudhary and Neeraj Kumar
Institute of Electrical and Electronics Engineers (IEEE)



Metaverse for 6G and Beyond: The Next Revolution and Deployment Challenges
Anjum Mohd Aslam, Rajat Chaudhary, Aditya Bhardwaj, Ishan Budhiraja, Neeraj Kumar, and Sherali Zeadally
Institute of Electrical and Electronics Engineers (IEEE)
Metaverse is an evolving paradigm for the next-generation Internet, intends to provide 3D immersive experiences and self–sustaining virtual shared space by utilising a wide range of relevant technologies. Due to rapid advancement in technologies such as augmented reality (AR), mixed reality and virtual reality (VR) in real-time applications, metaverse is proceeding from science fiction to extended reality (XR). In this article, we present the vision of metaverse's future in wireless technologies, including 6G and beyond. We illustrate the framework of metaverse-based wireless systems, describing the requirements and fundamental technologies to be integrated with 6G to realize the metaverse. We consider a case study of an autonomous vehicle's remote assistance system that leverages VR technology, 360° live stream, and a mobile edge-enabled distributed computing paradigm. The simulation findings demonstrate the effectiveness of employing VR technology to create an immersive environment for remotely controlling and supporting autonomous vehicles to enable quicker decision-making. Furthermore, we compare several potential future networks, discuss the deployment challenges, and present cloud-edge-end framework-driven solutions to employ Next-G wireless systems in the metaverse. Finally, we outline several open research directions to realize the true vision of metaverse towards 6G and beyond.


Energy-Efficient THz NOMA-Enabled Small Cells Underlaying Macrocell Using Reinforcement Learning
Varun Kumar, Ishan Budhiraja, Akansha Singh, Rajat Chaudhary, and Srinivas Aluvala
IEEE
The Terahertz (THz) frequency range has attracted significant interest owing to its exceptional high frequency and broad bandwidth that can be easily accessed. THz technology is a crucial component of the sixth generation (6G) wireless communication networks. In this research, we have incorporated the downlink non-orthogonal multiple access (NOMA) technology in small cell (SC) networks operating in the THz band to enhance the overall performance of the network. Despite the above mentioned advantage, the combination of these technologies increase the energy consumption. So, to address this problem, we formulated a problem to maximize the energy efficiency (EE) of THz-NOMA downlink network by optimizing resource block (RB) assignment and power control. To achieve the target, we used deep deterministic policy gradient (DDPG) technique because it has an ability to solve the continuous action spaces as compared to traditional model-based approaches. Numerical results demonstrated that the proposed scheme acquire better results than the state-of-art schemes.


Improving the Transmission Power of UAVs with Intelligent Reflecting Surfaces in V2X
Shivam Chaudhary, Rajat Chaudhary, Ishan Budhiraja, Aditya Bhardwaj, Anushka Nehra, and Sheshikala Martha
IEEE
Unmanned aerial vehicles (UAVs), which can help with high-speed communications and provide better coverage, are an important component of next-generation wireless networks. Because of its high mobility and aerial nature, it is suitable for a wide range of mobile wireless communications-based applications. However, low data rates with limited transmission power constitute a significant difficulty in wireless communication that lowers network performance. To overcome this issue, integrating a UAV with a relay device capable of delivering high data speeds while utilising minimum transmission power is a promising approach. In this research, we presented an edge-cutting framework called UAV-IRS, in which an Intelligent reflective surface (IRS) supports unmanned aerial vehicles (UAVs) that traverse areas with low signal strength. Furthermore, we discussed the applications, challenges and research directions of UAV-IRS in vehicle-to-everything (V2X) communication. We considered a case study of UAV-IRS in V2X communication. The performance evaluation demonstrates how the viable data rate and minimum transmission power decrease with distance as the number of IRS elements increases.


Blockchain-based Robust SDN Framework for Digital Twin-Enabled IoT Networks
Aditya Bhardwaj, Rajat Chaudhary, Anjum Mohd Aslam, and Ishan Budhiraja
IEEE
To promote interaction between physical IoT assets and digital services, the rapid expansion of the Internet of Things (IoT) necessitates digitizing industrial processes. Integrating digital twins into an IoT network enables real-time virtualization of physical entities, allowing for efficient real-time control, rapid maintenance, and better decision-making. Furthermore, a digital twin-enabled IoT network may generate a vast amount of data, posing storage, processing, and security difficulties. In this study, we presented a blockchain and software-defined networking (SDN) integrated framework for offering decentralized and secure data operations in IoT networks to address these concerns. To filter malicious packets, the proposed system incorporates a packet analyzer and feature extraction modules at the SDN control layer. The blockchain is then constructed using an elliptic curve point technique for authenticating IoT devices. The results reveal that, when compared to the existing model, our suggested approach performs significantly better in terms of latency and throughput.


P-HrDPS: Security-Aware Heart Disorder Prediction Support Model Using Ensemble Learning Technique
Madhuri Gupta, Deepika Pantola, Deepanshi, Ishan Budhiraja, and Rajat Chaudhary
IEEE
Biomedical applications' rapid technological development produces vast amounts of personal data. This biological data creates privacy concerns as it discloses private information like a person's health status and way of life. In Biomedical field, heart disorder is the most critical and major threat for the population across the world. Due to the advancement in machine learning and parametric technologies, heart disorder is possible to predict. In this presented work, heart disorder prediction is performed based on univariate and multivariate feature analysis. Along with this, the proposed system keeps private data safe by using Paillier homomorphic encryption technique to encrypt and decrypt the patients' records. In this work, the max voting ensemble learning technique is used by incorporating five machine learning techniques such as support vector machine, logistic regression, naïve Bayes, decision tree and K-nearest neighbor. The outcome shows that ensemble learning performed better in comparison to others. According to the univariate feature analysis, type (0) chest pain is critical than others. In multivariate analysis, chest pain, exang, and old peak parameters are more correlated with the heart disorder than others.


Container-based Migration Technique for Fog Computing Architecture
Aditya Bhardwaj, Umesh Gupta, Ishan Budhiraja, and Rajat Chaudhary
IEEE
The fog computing paradigm has gained significant popularity in meeting the emerging demand for delay-sensitive applications. A fog computing paradigm provides low latency, reduces communication delay, and increases system energy efficiency. Like cloud computing, the fog computing framework also relies on virtualization. In a fog-enabled virtualization environment, challenges of system failure, hardware maintenance and load balancing can be addressed by facilitating the migration of fog-virtualized components. To address this issue, in this study, we first proposed an experimental testbed to facilitate the migration of running nodes using container-based virtualization technology. Furthermore, we discussed recent research challenges and trends for fog-enabled container-based virtualization technology.


FLBCPS: Federated Learning based Secured Computation Offloading in Blockchain-Assisted Cyber-Physical Systems
Prakhar Consul, Ishan Budhiraja, Rajat Chaudhary, and Deepak Garg
IEEE
Mobile-edge computing (MEC) is a in demand method for improving the quality of computation experience on mobile devices (MD) since it helps MD’s to offload computing activities to MEC servers, which provide strong computing capabilities. However, there are certain unresolved concerns in present computation-offloading works: 1) safety issue; 2) joint computation offloading; and 3) flexible optimization. To solve safety and privacy concerns, we use Federated Learning-based blockchain technology, which provides data accuracy and irreversibility in MEC systems. Federated Learning (FL) is a promising technique towards effective machine learning while protecting privacy in dispersed situations such as the Internet of Things (IoT) and MEC. FL’s effectiveness is dependent on a network of participant nodes contributing their data and computational resources to the collective training of a globally model. As a result, preventing malicious nodes from interfering with model training while rewarding trustworthy nodes to assist to the learning process is critical for improved FL security and performance. We created an efficient resource allocation technique that optimizes computational offloading using a Blockchain-based Federated Learning (FL) method in order to add to the literature. The experimental findings show’s that the recommended FLBCPS technique improve system latency while maintaining consensus security.


Security Reassessing in UAV-Assisted Cyber-Physical Systems Based on Federated Learning
Prakhar Consul, Ishan Budhiraja, Rajat Chaudhary, and Neeraj Kumar
IEEE
Mobile-edge computing (MEC) is a popular method for increasing the quality of computing experience for consumers since it allows them to offload computing operations to MEC servers, which have significant processing capabilities. However, safety and information security are major challenges that must be addressed. We employ Federated Learning-based Cyber-Physical Systems (CPS) to address safety and information security in MEC systems, which offers data accuracy and un-sustainability. Security of UAV assisted CPS against cyber-attacks yet an another challenging problem. Because most cyber-attacks occur in unpredictable ways, it is difficult to define them in a structured manner. Instead of developing a unique cyber-attack model, we focus on exploring the dynamic behavior of the system to cyber-attacks throughout this article. Attacks that repeat themselves by interfering with system components or data are insignificant if they can be quickly identified by the system's control mechanism. Intelligent cyber attackers remain undetected by the tracking system by carefully designing cyber-attacks. Our primary goal is to examine the effectiveness of such cyberattacks from the aspect of the CPS and overcome them using Federated Learning (FL).


EnFlow: An Energy-Efficient Fast Flow Forwarding Scheme for Software-Defined Networks
Rajat Chaudhary and Neeraj Kumar
Institute of Electrical and Electronics Engineers (IEEE)
In recent years, the huge expansion of Datacenters (DC) to execute billions of end-user applications in real-time leads to a large amount of energy consumption across the globe. So, the traditional TCP/IP-based networks which are being used for DC inter-connections are facing challenges of managing stringent Quality-of-Service (QoS) requirements of different applications of the end-users and service providers. Moreover, the existing solutions rely on distributed architecture and do not scale for large scale data centers. The issue of high power consumption at DC arises with the increase in the number of nodes and links in the network. Also, it becomes problematic on the DC whenever the underlying network resources (switches and routers) are not efficiently utilized at the time of peak data traffic resulting in high operational cost of energy utilization. However, Software-Defined Networking (SDN) emerges as one of the leading technologies to address the aforementioned issues using the programmable switches and controllers. Inspired from these facts, in this paper, we have formulated the Energy-Aware Routing (EAR) problem of DCs as a Mixed Integer Non-Linear Programming (MINLP) for which an Energy-Efficient Fast Flow Forwarding (EnFlow) scheme is designed. The EnFlow scheme uses the power-saving mode of the network to solve the EAR problem. It has three modules namely– priority scheduling, routing, and re-routing. The first module works according to the First-in-First-Out Push Out Priority (FIFO-POP) scheduling using the multiple OpenFlow switches. The FIFO-POP is designed to save the energy usage of multiple switches by reducing the average waiting time of incoming packets in the queue buffers. The second module is based upon an efficient flow re-routing for a new node and link adaptation to provide the maximum bandwidth to the wired links. The third module is based upon the meta-heuristic Ant Colony Routing (ACR) to execute the stochastic decision policy on the network controller for computation of the shortest path of the forwarding nodes. The proposed EnFlow scheme is simulated using the data traces of 34 cities of NorthAmerica zone with Omnet++ 5.1 using various performance evaluation metrics. The results obtained demonstrated that the proposed EnFlow scheme is 24.55% and 71.15% more energy-efficient in comparison to the RE-FPR and ILP-EAR schemes. Also, it consumes 9.72% and 40.83% lower energy in comparison to the FFHA and EXR schemes respectively.


A systematic review on the identification and diagnosis of clinical characteristics of covid-19 patients




CURE: An effective covid-19 remedies based on machine learning prediction models




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, and Abderrahim Benslimane
Institute of Electrical and Electronics Engineers (IEEE)
With the evolution of the Internet of Vehicles (IoV), there has been an overwhelming increase in the number of connected vehicles in recent times. Due to this reason, massive amounts of data generated by connected vehicles makes traditional host-centric approach inevitable in IoV ecosystem. Moreover, the existing TCP/IP based congestion control mechanisms cannot be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput. So, in this article,11.This article is an extended version of paper entitled “Deep Learning-based Content Centric Data Dissemination Scheme for Internet of Vehicles“ published in IEEE ICC, 20-24 May 2018, Kansas City, USA DiLSe: A Lattice-based Secure and Dependable Data Dissemination Scheme for Social Internet of Vehicles is designed, which works in three modules. The first module, i.e., deep learning based content centric data dissemination scheme, works in three phases. 1) In the first phase, the connection probability of vehicles is computed to identify stable and reliable connections using Weiner process model. 2) In the second phase, a convolutional neural network based scheme is presented for estimating the social relationship score among vehicle-to-vehicle pair. 3) In the third phase, a content centric data dissemination scheme is presented. However, the mobility of vehicles in IoV ecosystem gives them the liberty to move in/out of the network without IP assignment. This makes it necessary to replicate the content at each node for providing fault tolerance. So, in the second module, a data replication scheme for fault tolerance in IoV network is designed, which is followed by an access control mechanism for read/right access for network content in third module. Finally, in the last module, a crucial lattice-based exchange and authentication scheme using blockchain is also designed for handling secure communication in IoV ecosystem. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained prove the efficacy of the proposed scheme concerning various performance metrics.


PARC: Placement Availability Resilient Controller Scheme for Software-Defined Datacenters
Rajat Chaudhary and Neeraj Kumar
Institute of Electrical and Electronics Engineers (IEEE)
Software-Defined Datacenters (SDDC) have been widely used for load-aware data management for different applications across the globe. Due to its centralized architecture, the issues of scalability along with resilience (to overcome the failure of single or multiple controllers) are still challenging because of an exponential increase in the data generated from different smart devices. Most of the solutions reported in the literature for this problem use a single controller which may not address the scalability issues. However, the issues as mentioned above of scalability and resilience in SDDC can be solved by deploying multiple distributed controllers at the control plane. However, the primary concern in a network having various controllers is the optimal Controller Placement Problem (CPP) to resolve the issues of fault-tolerance, latency among controllers, availability, and placement. Hence, to resolve the issues described above, in this paper, we propose Placement Availability Resilient Controller (PARC) scheme. The PARC scheme works in the following four phases: (i) stable network partitioning (ii) localization of controllers using the cooperative game theory (iii) computation of an optimal number of multiple controllers and (iv) computation of minimal extra backup controllers to improve the overall network cost. The numerical results of the PARC scheme are evaluated on Internet2 OS3E topology using POCO-toolset simulated in Matlab. The experimental results demonstrated that the cost of deploying the number of controllers using the PARC scheme has reduced to 12.98%, 8.16%, and 6.25% as compared to the POCO-SA, POCO-MOALO, and CNCP schemes respectively. Moreover, the PARC scheme outperforms the existing state-of-the-art schemes (POCO-SA, POCO-MOALO, and CNCP) for inter-controller as well as switch-to-controller latency.


LOADS: Load Optimization and Anomaly Detection Scheme for Software-Defined Networks
Rajat Chaudhary and Neeraj Kumar
Institute of Electrical and Electronics Engineers (IEEE)
The decoupling of control functionality from the forwarding devices to the control plane in Software-Defined Networks (SDN) provides a unique platform to design a programmable and reconfigurable network. A single controller due to its limited capacity and resources may not handle heavy load traffic generated from various smart devices. In order to handle this, multiple controllers need to be deployed at the control plane so as to ensure improved efficiency and scalability of the network. The data flow by the distributed controllers fluctuates frequently which results in an uneven load distribution amongst different controllers. So, to handle the aforementioned issues, in this paper, a Load Optimization and Anomaly Detection Scheme (LOADS) is proposed. Using LOADS, the probability of switch selection is determined using the following two factors (i) distance from the switch to the controller, and (ii) resource consumption ratio of the switch to its controller. Also, an IP flow-based network anomaly detection module has been designed to classify the traffic as malicious or normal. In order to address the network anomaly, the LOADS scheme uses Access Control Policies (ACPs) on the user's behavior in the network. The proposed scheme is evaluated on Mininet emulator using POX controller with datasets of Internet Topology Zoo from BTNorthAmerica zone. The performance analysis reveals that LOADS minimizes the average execution time by 6.74% and 20.64% as compared to the existing competitive schemes, Distributed Hopping Algorithm (DHA) and Elastic Distributed Controller (ElastiCon). Also, it helps in improving the overall migration cost and response time of each controller. The proposed LOADS scheme has the migration cost of 55.1 milliseconds as compared to the ElastiCon and DHA schemes alongwith the migration cost of 110 milliseconds and 140 milliseconds respectively. In addition to the migration cost, the response time of the proposed scheme is 32.8 milliseconds as compared to DHA and ElastiCon which takes almost 90 milliseconds and 78 milliseconds respectively.

RECENT SCHOLAR PUBLICATIONS

Proximal Policy Optimization based sum rate maximization scheme for STAR-RIS-assisted vehicular networks underlaying UAV
S Chaudhary, I Budhiraja, R Chaudhary, S Garg, BJ Choi, M Alrashoud
Alexandria Engineering Journal 118, 700-710 2025



Quantum-resilient blockchain-enabled secure communication framework for connected autonomous vehicles using post-quantum cryptography
AM Aslam, A Bhardwaj, R Chaudhary
Vehicular Communications 52, 100880 2025



Asynchronous Federated Learning Technique for Latency Reduction in STAR-RIS enabled VRCS
S Chaudhary, I Budhiraja, R Chaudhary, N Kumar, S Biswas
2025



A comprehensive survey on software‐defined networking for smart communities
R Chaudhary, GS Aujla, N Kumar, PK Chouhan
International Journal of Communication Systems 38 (1), e5296 2025



Probing the Convergence of Vehicular Edge Metaverse and 6G: Blockchain-enabled Framework
S Kumar, R Chaudhary, I Budhiraja
2024 IEEE International Conference on Advanced Networks and 2024



FogIoT: Fog Computing based Security Frameworks for Software-defined IoT
AM Aslam, R Chaudhary, I Budhiraja, V Sharma
2024 IEEE International Conference on Advanced Networks and 2024



Mitigating Cross-Site Request Forgery Vulnerabilities: An Examination of Prevention Systems
Y Singh, P Goel, S Aggarwal, R Chaudhary, I Budhiraja
2024 IEEE International Conference on Advanced Networks and 2024



Fingerprint-Based Multifactor Authentication For Bank Transaction
M Kaur, S Kaistha, S Aggarwal, I Budhiraja, R Chaudhary, A Bindle
2024 IEEE International Conference on Advanced Networks and 2024



Quantum Federated Reinforcement Learning Based Joint Mode Selection and Resource Allocation for STAR-RIS aided VRCS
S Chaudhary, I Budhiraja, R Chaudhary, N Kumar, D Garg, ...
IEEE Internet of Things Journal 2024



Predicting Hotel Booking Cancellations using Machine Learning Techniques
A Bhardwaj, T Yadav, R Chaudhary
2024 15th International Conference on Computing Communication and Networking 2024



Parameterize Deep Q Network for Backscattering Data Capture with Multiple UAVs
D Gupta, I Budhiraja, R Chaudhary, N Kumar
ICC 2024-IEEE International Conference on Communications, 4536-4541 2024



STAR-RIS Based Resource Scheduling and Mode Selection for Drone Assisted 5G Communications
S Chaudhary, A Nehra, I Budhiraja, R Chaudhary, A Bansal
IEEE INFOCOM 2024-IEEE Conference on Computer Communications Workshops 2024



A cooperative game approach for multi-lane merging decision-making algorithm for cavs
AM Aslam, A Bhardwaj, R Chaudhary, I Budhiraja
Proceedings of the 25th International Conference on Distributed Computing 2024



Energy-Efficient THz NOMA-Enabled Small Cells Underlaying Macrocell Using Reinforcement Learning
V Kumar, I Budhiraja, A Singh, R Chaudhary, S Aluvala
2023 IEEE International Conference on Advanced Networks and 2023



Blockchain-based robust SDN framework for digital twin-enabled IoT networks
A Bhardwaj, R Chaudhary, AM Aslam, I Budhiraja
2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 1-6 2023



Improving the transmission power of uavs with intelligent reflecting surfaces in v2x
S Chaudhary, R Chaudhary, I Budhiraja, A Bhardwaj, A Nehra, S Martha
2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 1-6 2023



AIDA—A holistic AI-driven networking and processing framework for industrial IoT applications
H Chahed, M Usman, A Chatterjee, F Bayram, R Chaudhary, A Brunstrom, ...
Internet of Things 22, 100805 2023



P-HrDPS: Security-Aware Heart Disorder Prediction Support Model Using Ensemble Learning Technique
M Gupta, D Pantola, I Budhiraja, R Chaudhary
2023 IEEE International Conference on Communications Workshops (ICC 2023



Container-based Migration Technique for Fog Computing Architecture
RC Aditya Bhardwaj, Umesh Gupta, Ishan Budhiraja
2023 International Conference for Advancement in Technology (ICONAT) 2023



Metaverse for 6G and beyond: The next revolution and deployment challenges
AM Aslam, R Chaudhary, A Bhardwaj, I Budhiraja, N Kumar, S Zeadally
IEEE Internet of Things Magazine 6 (1), 32-39 2023

MOST CITED SCHOLAR PUBLICATIONS

Blockchain for smart communities: Applications, challenges and opportunities
S Aggarwal, R Chaudhary, GS Aujla, N Kumar, KKR Choo, AY Zomaya
Journal of Network and Computer Applications 144, 13-48 2019
Citations: 386



BEST: Blockchain-based secure energy trading in SDN-enabled intelligent transportation system
R Chaudhary, A Jindal, GS Aujla, S Aggarwal, N Kumar, KKR Choo
Computers & Security 85, 288-299 2019
Citations: 318



SDN-enabled multi-attribute-based secure communication for smart grid in IIoT environment
R Chaudhary, GS Aujla, S Garg, N Kumar, JJPC Rodrigues
IEEE Transactions on Industrial Informatics 14 (6), 2629-2640 2018
Citations: 223



LSCSH: Lattice-based secure cryptosystem for smart healthcare in smart cities environment
R Chaudhary, A Jindal, GS Aujla, N Kumar, AK Das, N Saxena
IEEE Communications Magazine 56 (4), 24-32 2018
Citations: 173



Data offloading in 5G-enabled software-defined vehicular networks: A Stackelberg-game-based approach
GS Aujla, R Chaudhary, N Kumar, JJPC Rodrigues, A Vinel
IEEE Communications Magazine 55 (8), 100-108 2017
Citations: 147



Network service chaining in fog and cloud computing for the 5G environment: Data management and security challenges
R Chaudhary, N Kumar, S Zeadally
IEEE Communications Magazine 55 (11), 114-122 2017
Citations: 140



Optimized big data management across multi-cloud data centers: Software-defined-network-based analysis
R Chaudhary, GS Aujla, N Kumar, JJPC Rodrigues
IEEE Communications Magazine 56 (2), 118-126 2018
Citations: 137



SAFE: SDN-assisted framework for edge–cloud interplay in secure healthcare ecosystem
GS Aujla, R Chaudhary, K Kaur, S Garg, N Kumar, R Ranjan
IEEE Transactions on Industrial Informatics 15 (1), 469-480 2018
Citations: 118



Energychain: Enabling energy trading for smart homes using blockchains in smart grid ecosystem
S Aggarwal, R Chaudhary, GS Aujla, A Jindal, A Dua, N Kumar
Proceedings of the 1st ACM MobiHoc workshop on networking and cybersecurity 2018
Citations: 118



SeDaTiVe: SDN-enabled deep learning architecture for network traffic control in vehicular cyber-physical systems
A Jindal, GS Aujla, N Kumar, R Chaudhary, MS Obaidat, I You
IEEE network 32 (6), 66-73 2018
Citations: 117



Lattice-based public key cryptosystem for internet of things environment: Challenges and solutions
R Chaudhary, GS Aujla, N Kumar, S Zeadally
IEEE Internet of Things Journal 6 (3), 4897-4909 2018
Citations: 106



SecSVA: secure storage, verification, and auditing of big data in the cloud environment
GS Aujla, R Chaudhary, N Kumar, AK Das, JJPC Rodrigues
IEEE Communications Magazine 56 (1), 78-85 2018
Citations: 101



Metaverse for 6G and beyond: The next revolution and deployment challenges
AM Aslam, R Chaudhary, A Bhardwaj, I Budhiraja, N Kumar, S Zeadally
IEEE Internet of Things Magazine 6 (1), 32-39 2023
Citations: 70



Deep learning-based content centric data dissemination scheme for internet of vehicles
A Gulati, GS Aujla, R Chaudhary, N Kumar, MS Obaidat
2018 IEEE international conference on communications (ICC), 1-6 2018
Citations: 51



A Novel Approach of Text Steganography based on null spaces
AA Prem Singh, Rajat Chaudhary
IOSR Journal of Computer Engineering (IOSRJCE) 3 (4), 11-17 2012
Citations: 51



DLRS: deep learning-based recommender system for smart healthcare ecosystem
GS Aujla, A Jindal, R Chaudhary, N Kumar, S Vashist, N Sharma, ...
ICC 2019-2019 IEEE international conference on communications (ICC), 1-6 2019
Citations: 50



Dilse: Lattice-based secure and dependable data dissemination scheme for social internet of vehicles
A Gulati, GS Aujla, R Chaudhary, N Kumar, MS Obaidat, A Benslimane
IEEE transactions on dependable and secure computing 18 (6), 2520-2534 2019
Citations: 46



An ensembled scheme for QoS-aware traffic flow management in software defined networks
GS Aujla, R Chaudhary, N Kumar, R Kumar, JJPC Rodrigues
2018 IEEE international conference on communications (ICC), 1-7 2018
Citations: 45



LaCSys: Lattice-based cryptosystem for secure communication in smart grid environment
R Chaudhary, GS Aujla, N Kumar, AK Das, N Saxena, JJPC Rodrigues
2018 IEEE International Conference on Communications (ICC), 1-6 2018
Citations: 38



LOADS: Load optimization and anomaly detection scheme for software-defined networks
R Chaudhary, N Kumar
IEEE Transactions on Vehicular Technology 68 (12), 12329-12344 2019
Citations: 36