Browsing by Author "Bhatia, Ashutosh"

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3TAAV: A three-tier architecture for pseudonym-based anonymous authentication in VANETs
(IEEE, 2018) Bhatia, Ashutosh
Vehicular Ad-hoc Network (VANET) is an emerging communication framework to abet in day-to-day management of vehicular traffic and safety of vehicles. Anonymous authentication is one of the key requirements in VANET ensures the privacy of the origin of the message. Existing security protocols in VANETs, that provide anonymous authentication based on the two-tier architecture, consists of two VANET elements, namely vehicles and road side units (RSUs) working as the key generating server (KGS). These protocols rely heavily on RSU to provide anonymous identity to vehicles. In these protocols, with the movement of the vehicle from one RSU to another RSU, a fresh anonymous identity needs to be generated for every vehicle with additional handing over and taking over procedures. In this paper, we propose a novel security protocol for anonymous authentication based on three-tier architecture involving three VANET elements, viz., vehicle, RSU and pseudonym server (PSS) for achieving multiple levels of anonymity in VANETs. The proposed protocol reduces the overhead incurred by the RSU for certificate management and eliminates the requirement of handing over and taking over of vehicles while changing from one RSU to another. Considering the overhead of RSU in certificate management, the number of pseudonyms cached by each vehicle and blackout-time (duration for which vehicle is not part of VANET due to non-acquisition of pseudo-identity) as performance metrics for simulation, the simulation results demonstrate the efficacy of the proposed approach
Achieving waypoint enforcement in multi-VLAN hybrid SDN
(IEEE, 2018) Haribabu, K; Bhatia, Ashutosh
The waypoint enforcement in a network can be seen as the act of diverting the path of packets flowing in the network towards a predefined checkpoint to gain a higher degree of control over the network. Most of the existing solutions which perform waypoint enforcement in hybrid-SDN either disturb the existing VLAN configurations or possess certain limitation in terms of placement of SDN switches in the network. In this paper, we address the problem of achieving waypoint enforcement in a multi-VLAN hybrid software defined network (hybrid-SDN), which does not have these limitations. In particular, the proposed method uses the concept of gratuitous ARP (Address Resolution Protocol) to poison the ARP table of all the hosts in the network to divert the traffic packet towards an SDN switch.
Adaptive RIS design and optimization for cooperative ris-assisted wireless systems
(IEEE, 2025-07) Bitragunta, Sainath; Bhatia, Ashutosh
We propose an adaptive RIS-based cooperative transmission strategy that jointly selects one of two RIS paths and dynamically optimizes the number of active meta-atoms to maximize physical layer (PHY) secrecy capacity under a total average power constraint. Unlike existing approaches that fix the RIS size K or assume identical fading on all links, our framework uses long-term statistics to probabilistically choose between two RISs (upper or lower) with arbitrary first-hop fading, and leverages instantaneous channel state information (CSI) on the selected path to solve a convex K-sizing problem via a Lagrangian multiplier approach. We derive and present the solution for optimal K, and numerically evaluate the average PHY secrecy capacity and average PHY secrecy efficiency for the proposed optimal strategy. Numerical results show that the proposed optimal-K strategy achieves up to 35% higher average PHY secrecy capacity and 50% improvement in average PHY secrecy efficiency compared to a fixed-K benchmark strategy across moderate power thresholds. Furthermore, we present an insightful asymptotic analysis for average PHY secrecy capacity in an interesting scaling regime. Our findings demonstrate the practical benefits of adaptive RIS for cooperative PHY secure and energy-efficient beyond fifth generation (B5G) wireless systems
Analysis and Performance Evaluation of Different Methods to Achieve Way-Point Enforcement in Hybrid SDN
(Springer, 2020-03) Haribabu, K; Bhatia, Ashutosh
Software Defined Networking (SDN) is a new paradigm that gives central control over distributed SDN-enabled switches. SDN is begin adapted very rapidly to gain the advantages of centralized programmable control over the network. But it is difficult to go for a green-field deployment of SDN due to several reasons. It requires a huge budget to install SDN network infrastructure. The deployment of SDN devices will take some time, due to which the network can go down. The safe option is to go for partial deployment, where the SDN devices can be installed incrementally in the traditional network. Over the last few years, the research community brings their attention to hybrid SDN networks. To gain SDN control over the network traffic, it has to go through at least one SDN switch. There exist a few solutions which enforce the traffic to go through the SDN switch. In this paper, our aim is to analyze and evaluate the performance of existing methods to achieve way-point enforcement in hybrid SDN networks in terms of average path length, and percentage of way-point enforcement achieved.
Bandwidth Efficient Clock Skew Compensation in TDMA-Based Star Topology Wireless Networks
(IEEE, 2017) Bhatia, Ashutosh
One of the prevalent method to achieve clock synchronization in star topology based wireless network, is by periodic transmission of a “Beacon” message from the controller. Using this message other nodes locate the start of the time frame established by the controller. This technique has not been energy efficient in case of low duty cycle applications, where the nodes have to receive the Beacon at the start of every frame even when they don't have any data to transmit. Few of existing works solve this problem by introducing a larger fixed size guard time between the transmission slots allocated to the devices. This allows the devices to skip multiple Beacons and still transmit in their allocated time slot without any collision. In such methods amount of bandwidth which remain unutilized due to fixed guard time becomes a considerably large fraction of the total available bandwidth. In this paper, we propose a novel technique to allow the nodes to skip the Beacon for certain number of frames by introducing the concept called Variable Guard Time (VGT). The number of frames that a node can remain synchronized with the coordinator without receiving the Beacon depends upon the number of Guard Slots allocated to it. This is decided based upon factors like the node's energy saving requirement, the actual clock drift rate between the devices and the available bandwidth. Simulation results show that, in terms of bandwidth utilization, the proposed idea of VGT outperforms the traditional approach of fixed guard time scheme.
Bayesian deep learning meets self-attention: a risk-aware approach to advertisement optimization
(IEEE, 2025-05) Bhatia, Ashutosh; Tiwari, Kamlesh
In the highly competitive landscape of e-commerce advertising, maximizing Return on Advertising Spend (ROAS) is critical, yet remains inherently uncertain due to auction-based bidding dynamics and fluctuating market conditions. Traditional deterministic models fail to capture this uncertainty, necessitating a probabilistic approach that balances predictive accuracy with interpretability. To address this challenge, the paper proposes a novel Hierarchical Bayesian Deep Learning framework that integrates a Bayesian Belief Network (BBN) for structured probabilistic reasoning and a Mixture Density Network (MDN) for full distributional modeling of ROAS. The BBN models dependencies among campaign variables, offering interpretable insights, while the hierarchical deep learning architecture overcomes scalability limitations in high-dimensional settings through self-attention mechanisms. Experiments demonstrate up to 22.8% lower RMSE and 27.4% better Negative Log Likelihood (NLL) and up to 31.2% lower Kullback-Leibler divergence (KLD) than state-of-the-art methods (DeepAR, Prophet, NGBoost), achieving an R2 of 98% with an inference speed of 5.2 ms per campaign, making real-time bidding feasible. Ablation studies confirm that attention-driven feature selection and calibrated uncertainty quantification significantly enhance both predictive performance and explainability, identifying key drivers of campaign success. By providing precise, uncertainty-aware, and explainable predictions, this approach enables adaptive bidding strategies, optimized budget allocation, and risk management, setting a new benchmark for intelligent decision-making in digital advertising.
Bitcoin Data Analytics: Exploring Research Avenues and Implementing a Hadoop-Based Analytics Framework
(Springer, 2020-03) Bhatia, Ashutosh
Bitcoin is the most successful cryptocurrency since its inception in 2009 [30]. There are 18.1 million BTCs in circulation as of December 2019, which roughly translates to 149 Billion USD [12]. With Bitcoin’s substantial market capitalization and unique features like pseudo-anonymity and immutability, it draws much attention from the researchers across the world. Despite this enormous spotlight towards Bitcoin, it remains under-researched because of the large size of the Bitcoin Data, (Roughly 250 GB) and the inability to process this data in small time. To explore avenues for further research, this article presents a survey of the recent advancements done regarding the big data analytics of the Bitcoin Cryptocurrency. Furthermore, we propose an analysis framework based on the Apache Hadoop ecosystem.
Bitcoin Data Analytics: Scalable techniques for transaction clustering and embedding generation
(IEEE, 2021) Bhatia, Ashutosh
Bitcoin provides pseudo-anonymity to its users, leading to many transactions related to illicit activities. The advent of mixing services like OnionBC, Bitcoin Fog, and Blockchain.info has allowed users to increase their anonymity further. This paper tackles the pseudo-anonymity of the Bitcoin blockchain by developing a scalable spark based framework to find patterns in the transaction data. The efficacy of the framework is demonstrated by performing exploratory analysis. Furthermore, the paper shows the capabilities of bitcoin-based graph representations and addresses the issue of user profiling based on unsupervised learning approaches for analysing Bitcoin transactions and users. The authors convert the transaction graph of the Bitcoin data to contain only Wallet-IDs and generate graph embeddings using Variational Graph Autoencoder [1]. Additionally, the authors use explainable-AI techniques and Kohonen self organizing maps to visualize and understand the results obtained from the unsupervised learning methods.
Bitcoin’s Blockchain Data Analytics: A Graph Theoretic Perspective
(Springer, 2022-03) Bhatia, Ashutosh; Tiwari, Kamlesh
Bitcoin is the first and most widely used cryptocurrency in the world. It provides a pseudonym identity to its users that is established using the user’s public key, which leads to preserving the user’s privacy. Each transfer of bitcoin cryptocurrency among the users makes a transaction. The pseudonym identities are considered as transaction end-points. These transactions are recorded on an immutable public ledger called Blockchain which is an append-only data structure. The popularity of Bitcoin has increased unreasonably. The general trend shows a positive response from the common masses indicating an increase in trust and privacy concerns which makes an interesting use case from the analysis point of view. Moreover, since the blockchain is publicly available and up-to-date, any analysis would provide a live insight into the usage patterns which ultimately would be useful for making a number of inferences by law-enforcement agencies, economists, tech-enthusiasts, etc. In this paper, we study various applications and techniques of performing data analytics over Bitcoin blockchain from a graph theoretic perspective. We also propose a framework for performing such data analytics and explored a couple of use cases using the proposed framework.
A Blockchain based Solution to Know Your Customer (KYC) Dilemma
(IEEE, 2019) Bhatia, Ashutosh
Know Your Customer (KYC) is done as a mandatory entry step into any financial institution. However, even today the amount of manual intervention involved in the process is staggering. Often the data is centrally stored, and the computer programs acting are also centrally governed thus are not tamperproof making them susceptible to vulnerabilities and attacks. Different organizations do not have a unified application where the KYC information can be seamlessly shared between them without any risk of repudiation from any of the participating organizations. Our application, that is based on blockchain technology, aims to provide this platform as a service to financial institutions as an electronic-KYC solution, in the process of making the life of the end consumer easier
A Classification Framework for TDMA Scheduling techniques in WSNs
(2020) Bhatia, Ashutosh
One of the major challenges in wireless sensor networks (WSNs) is the mitigation of collisions due to simultaneous transmissions by multiple nodes over a common channel which are located in a proximity. TDMA-based channel access provides energy-efficient and collision-free transmissions. It is especially suitable for traffic with periodic transmission patterns and guaranteed QoS requirements. For that reason, a large number of TDMA-scheduling algorithms are available in the literature, and consequently, a good number of survey papers on TDMA-scheduling algorithms have been written. In this work, we propose a novel classification framework to categorize the existing TDMA-scheduling algorithms available for WSNs. As against existing survey works, the proposed framework possess certain new dimensions (categories) to classify existing TDMA-scheduling algorithms. Additionally, we introduce a couple of new sub-categories for the existing classes which would help researchers to even differentiate between two TDMA-Scheduling algorithms that are assumed to be similar as per existing classification schemes. Finally, we also discuss few important works in the context of proposed classification scheme.
A cluster based minimum battery cost AODV routing using multipath route for zigbee
(IEEE, 2008) Bhatia, Ashutosh
IEEE 802.15.4 standard is uniquely designed for low data rate wireless personal area networks (LR-WPANs). The IEEE 802.15.4 targets the applications such as industrial, agricultural, vehicular, residential, medical sensors and actuators which have more relaxed throughput requirements. ZigBee is a wireless technology based on IEEE 802.15.4. ZigBee routing uses ad hoc on-demand distance vector (AODV) routing protocol. In this paper we present an improved version of AODV called multipath energy aware AODV routing (ME-AODV), which utilizes the topology of network to divide it into one or more logical clusters and restricts the flooding of route request outside the cluster. The mesh links created at the time of cluster formation are used to decrease the routing path. ME-AODV uses nodes of the same cluster to share routing information, which significantly reduces the route path discovery. Since ZigBee routing is based on shortest-hop count, which causes overuse of a small set of nodes hence decreasing node as well as network lifetime. We also propose a mix of ad hoc on-demand multipath distance vector routing (AOMDV) and minimal-battery cost routing (MBCR) as an extension to AODV to increase the lifetime of network. The simulations have been performed using IEEE 802.15.4, ns-2 module.
Comparative Analysis of Impact of Cryptography Algorithms on Wireless Sensor Networks
(2021-07) Bhatia, Ashutosh
Cryptography techniques are essential for a robust and stable security design of a system to mitigate the risk of external attacks and thus improve its efficiency. Wireless Sensor Networks (WSNs) play a pivotal role in sensing, monitoring, processing, and accumulating raw data to enhance the performance of the actuators, micro-controllers, embedded architectures, IoT devices, and computing machines to which they are connected. With so much threat of potential adversaries, it is essential to scale up the security level of WSN without affecting its primary goal of seamless data collection and communication with relay devices. This paper intends to explore the past and ongoing research activities in this domain. An extensive study of these algorithms referred here, are studied and analyzed. Based on these findings this paper will illustrate the best possible cryptography algorithms which will be most suited to implement the security aspects of the WSN and protect it from any threat and reduce its vulnerabilities. This study will pave the way for future research on this topic since it will provide a comprehensive and holistic view of the subject.
CP-Net: Multi-Scale Core Point Localization in Fingerprints Using Hourglass Network
(IEEE, 2023) Bhatia, Ashutosh; Tiwari, Kamlesh
Core point is a location that exhibits high curvature properties in a fingerprint. Detecting the accurate location of a core point is useful for efficient fingerprint matching, classification, and identification tasks. This paper proposes CP-Net, a novel core point detection network that comprises the Macro Localization Network (MLN) and the Micro-Regression Network (MRN). MLN is a specialized autoencoder network with an hourglass network at its bottleneck. It takes an input fingerprint image and outputs a region of interest that could be the most probable region containing the core point. The second component, MRN, regresses the RoI and locates the coordinates of the core point in the given fingerprint sample. Introducing an hourglass network in the MLN bottleneck ensures multi-scale spatial attention that captures local and global contexts and facilitates a higher localization accuracy for that area. Unlike existing multi-stage models, the components are stacked and trained in an end-to-end manner. Experiments have been performed on three widely used publicly available fingerprint datasets, namely, FVC2002 DB1A, FVC2004 DB1A, and FVC2006 DB2A. The proposed model achieved a true detection rate (TDR) of 98%, 100%, and 99.04% respectively, while considering 20 pixels distance from the ground truth as correct. Obtained experimental results on the considered datasets demonstrate that CP-Net outperforms the state-of-the-art core point detection techniques.
D-insta: A Decentralized Image Sharing Platform
(Springer, 2023-03) Bhatia, Ashutosh; Tiwari, Kamlesh
Due to the covid-19 pandemic, people have moved toward digitization and using digital technologies in their daily life. For instance, photographers and artists use social media platforms or stock photo websites to showcase their art to people to get recognition and credit. Since social media platforms attract people more than stock photo websites, we consider incorporating the stock photo website features into the social media platforms. Currently, such platforms are running in a centralized fashion where their proprietary algorithms mask most of the content to which some users and advertisement posts are given more priority. Due to the centralization, such hidden algorithms create trust issues among the users along with other issues such as single point of failure, identity theft, etc. This causes genuine artists and photographers to lose their interest and motivation. Providing due credit to the authors and deserved recognition are significant concerns for photographers who share images on stock photo websites or social media platforms. In this paper, we propose a decentralized image-sharing platform/application utilizing blockchain and a distributed file storage system to address all these issues. The proposed platform leverages Ethereum-based smart contracts to maintain trust as deployed smart contracts are immutable, and the logic written in them is publicly available. We leverage a distributed file storage system to solve the blockchain scalability issue in terms of storage.
DCGit: Decentralized Internet Hosting for Software Development
(IEEE, 2023) Bhatia, Ashutosh; Tiwari, Kamlesh
Git has been the de-facto version control system for the Software Development industry. Although Git is distributed, developers’ tools for collaboration, such as GitHub, are centralized entities owned by large corporations such as Microsoft. The centralization creates trust and privacy issues for software development companies (preserving their intellectual property), along with a significant" single point of failure" issue. In addition, such centralized systems are susceptible to Sybil and distributed denial of service (DDoS) attacks due to the presence of malicious individuals. Blockchain technology has many key characteristics (such as decentralization, transparency, immutability, and audibility), solving these centralization issues. However, the requirement of having a storage system to store the user’s repositories over the blockchain creates a scalability issue (in terms of storage). Most importantly, it makes data (code) privacy more severe due to its open nature. In this paper, we propose a privacy-preserving decentralized alternative solution and framework named "DCGit" powered by Web3 technologies such as the Ethereum Blockchain and InterPlanetary File System (IPFS) to provide security and scalability yet user-friendly collaboration for software development.
DD-Locker: Blockchain-based Decentralized Personal Document Locker
(IEEE, 2022) Bhatia, Ashutosh
Document verification is the first step whenever we enter any organization or institute. In any organization, it is essential to track, verify, and check the person’s background who will become a part of the organization. This process is very time-consuming and hectic for both parties involved. Various governments provide cloud-based digital locker services for the citizens storing the public document on a centralized server. But due to its centralized nature, this type of service is weak against information breaches and Denial of Service (DoS) attacks. Also, there are some privacy concerns with such centralized digital locker services as the stored documents may contain users’ crucial personal information. This paper proposes a blockchain-based digital locker in a decentralized application using Ethereum Blockchain to securely store personal documents with high availability. The proposed solution also verifies documents with ease, confidentiality, access control, data privacy, authenticity, and maintaining the integrity of documents.
Decentralized marketplace for maintenance of electric vehicles
(Springer, 2025-04) Bhatia, Ashutosh; Tiwari, Kamlesh
As electric vehicles (EVs) become an integral part of the global transportation ecosystem, the need for efficient and cost-effective maintenance solutions will rise. This paper explores the application of game theory, specifically reverse auctions, to establish a decentralized, driver-centric marketplace for EV maintenance, promoting sustainability. The proposed system enables EV drivers to act as price determiners by launching individual smart contracts, which serve as reverse auction platforms. Maintenance providers can then bid on these contracts, competing to offer the most cost-effective services. By leveraging blockchain technology, smart contracts ensure transparency, trust, and secure fund management throughout the process. This innovative approach empowers EV drivers, reduces maintenance costs, and promotes a competitive service market. The paper outlines the underlying mechanisms, system architecture, and potential benefits of this model, along with a discussion of its implementation challenges and future implications for the EV ecosystem.
Decentralized Online Voting using Blockchain and Secret Contracts
(IEEE, 2021) Bhatia, Ashutosh
Voting is a complex process with a lot depending on it. Building an e-voting system that can guarantee anonymity, verifiability, and transparency together is a challenging task. Continuous efforts are being made to improve the voting system to achieve these properties. Recently, blockchain has hit the technology space with many promises, especially to make verifiable and transparent decentralized systems. However, a major challenge faced with blockchain-based e-voting systems is to achieve the users' anonymity while ensuring only authorized voters should be able to vote, and that too only once. To address these issues, this paper proposes a blockchain-based e-voting system with secret contracts. We have used Enigma (a secure multiparty computation platform) to design secret contracts. The proposed system meets most of the features required to conduct free and fair voting electronically.
Deep learning approaches for driver distraction detection using driver facing cameras: literature review and empirical study using cnn classifiers on a 100-driver image dataset
(2025-05) Bhatia, Ashutosh; Sharma, Yashvardhan; Tiwari, Kamlesh
Distracted driving contributes to thousands of fatalities and injuries globally. According to India’s Ministry of Road Transport and Highways (MoRTH), distraction-related behaviors such as rear-end and off-road collisions accounted for nearly one-fourth of all traffic incidents in 2022. The U.S. National Highway Traffic Safety Administration (NHTSA) reported 3,275 deaths and over 324,000 injuries from distraction-related crashes in 2023. In Europe, the European Road Safety Observatory (ERSO) observed handheld phone use by drivers in up to 9.4% of vehicles across member states, with self-reported texting rates reaching 53%. Despite numerous studies and surveys on driver distraction detection, existing literature remains fragmented, often combining multiple sensor modalities or distraction with related driver states such as fatigue. Prior empirical efforts also lack a unified benchmarking strategy to assess model generalization under shifts in viewpoint or spectral input. This paper presents a focused survey and empirical study of visiononly distraction detection using deep learning models applied to driver-facing camera inputs. It introduces a conceptual model linking behavioral cues to cognitive distraction, defines the visionbased Driver Distraction Detection (vDDD) system with alert logic, and develops structured taxonomies of datasets, architectures, and learning strategies. Using the 100-Driver dataset, the empirical study evaluates 26 CNN classifiers under 64 crossdomain configurations, systematically analyzing generalization across modality and camera view changes. Results show that frontal RGB-trained models generalize better than their NIRtrained counterparts and that lightweight models trade off accuracy under rare class scenarios for faster inference. The study establishes the vDDD paradigm as a vision-based behavioral modeling approach for distraction detection using driver-facing camera data. It outlines future research directions in spectrumaligned augmentation, attention modeling, and lightweight visuallanguage fusion, emphasizing deployment-focused strategies such as quantization, contrastive learning, and progressive fine-tuning.