Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
Browse
10 results
Search Results
Item K-distinct strong minimum energy topology problem in wireless sensor networks(Springer Verlag, 2015) Panda, B.S.; Shetty D, D.P.; Pandey, A.Given a set of sensors, the strong minimum energy topology (SMET) problem is to assign transmit power to each sensor such that the resulting topology containing only bidirectional links is strongly connected and the total energy of all the nodes is minimized. The SMET problem is known to be NP-hard. Currently available sensors in the market support a finite set of transmission ranges. So we consider the k- Distinct-SMET problem, where only k transmission power levels are used. We prove that the k-Distinct-SMET problem is NP-complete for k ≥ 3. However, on the positive side, we show that the 2-Distinct- SMET problem can be solved in polynomial time. The energy cost of transmitting a bit is higher than the cost of computation, and hence it may be advantageous to organize the sensors into clusters and form a hierarchical structure. This motivated the study of k-Distinct-rStrong Minimum Energy Hierarchical Topology (k-Distinct-rSMEHT) problem: Given a sensor network consisting of n sensors, and integers k and r, assign transmit powers to all sensors out of the k distinct power levels such that (i) the graph induced using only the bi-directional links is connected, (ii) at most r sensors are connected to two or more sensors by a bidirectional link and (iii) the sum of the transmit powers of all the sensors is minimum. We Propose a(formula presented.) approximation algorithm for the k-Distinct-rSMEHT problem for any fixed r and arbitrary k. © Springer International Publishing Switzerland 2015.Item VoteChain: A Blockchain Based E-Voting System(Institute of Electrical and Electronics Engineers Inc., 2019) Pandey, A.; Bhasi, M.; Chandrasekaran, K.In the past, electronic voting systems have not seen widespread adoption due to data privacy concerns. Previously proposed e-voting systems make use of a central database to store data, resulting in the servers used to store these databases being a single point of failure. These systems have also been found to be vulnerable to DoS attacks, leading to concerns over their reliability.Blockchains have been used to build secure and scalable distributed systems which have shown several benefits over centralized systems. They have seen uses in sectors ranging from finance and healthcare to food and energy.In this paper, we present VoteChain, a blockchain based voting system to help bring transparency and security to polls. We report on our implementation of VoteChain, as well as the results obtained in testing the system in a real-world poll which prove that such a system can be used in practice for large-scale elections. © 2019 IEEE.Item Adaptive RED for FreeBSD: Design, Implementation and Challenges(Institute of Electrical and Electronics Engineers Inc., 2019) Pandey, A.; Anand, T.; Shah, M.; Tahiliani, M.P.Bufferbloat problem arises due to buffering of large amounts of data in queues, owing to the large size of these queues. Bufferbloat being a relatively new phenomenon meant that earlier queue management algorithms did not specifically address this problem. Despite this issue, there is merit in analysing and evaluating old queue management algorithms which have helped alleviate the undesirable performance issues that arose due to persistently full buffers. One of the earliest and most significantly studied Active Queue Management (AQM) algorithms is Random Early Drop (RED). RED helps to keep the average size of the queues low and allow occasional bursts of packets through the queue. Once the number of packets queued crosses a minimum threshold, incoming packets are dropped with a random probability. However, the resulting average queue length is quite sensitive to the level of congestion and the RED parameter settings. Adaptive RED (ARED) solves most of the issues faced by RED with minimal changes and leaves its basic idea intact. The ARED algorithm regularly adapts the value of the maximum dropping probability and ensures that the queue length stays within the targeted range. Despite its ability to resolve the inherent problems in RED, ARED went largely unnoticed for several years, until the issue of Bufferbloat arose. Although ARED predates Bufferbloat, its fundamental design makes it an effective solution to handle Bufferbloat. This discovery led to the implementation of ARED in Linux and in network simulators like ns-3. Besides Linux, FreeBSD is one of the most popular open source operating systems. Although RED is supported in FreeBSD, ARED is not. Since ARED is one of the viable solutions to tackle Bufferbloat, this paper discusses the design and implementation of ARED in FreeBSD. We also detail the challenges faced during the implementation, and validate through real testbed experiments that our implementation in FreeBSD exhibits ARED's key characteristics. © 2019 IEEE.Item DPDK-FQM: Framework for Queue Management Algorithms in DPDK(Institute of Electrical and Electronics Engineers Inc., 2020) Pandey, A.; Bargaje, G.; Avinash; Krishnam, S.; Anand, T.; Monis, L.; Tahiliani, M.P.The advantages of Network Function Virtualization (NFV) have attracted many use cases ranging from virtual Customer Premises Equipment (vCPE) to virtual Radio Access Network (vRAN) and virtual Evolved Packet Core (vEPC). Fast packet processing libraries such as Data Plane Development Kit (DPDK) are necessary to enable NFV. Currently, DPDK provides a framework for Quality of Service (QoS) which is used for queue management, traffic shaping and policing, but it lacks a general purpose queue management framework. In this paper, we propose DPDK-FQM, a framework to implement queue management algorithms in DPDK, run them and collect the desired statistics. Subsequently, we implement Proportional Integral controller Enhanced (PIE) and Controlled Delay (CoDel) queue management algorithms by using the proposed framework. We develop a new DPDK application to demonstrate the usage of APIs in DPDK-FQM, and verify the correctness of the framework and implementations of PIE and CoDel. Our experiments on a high speed network testbed show that PIE and CoDel exhibit their key characteristics by controlling the queue delay at a desired target, while fully utilizing the bottleneck bandwidth. © 2020 IEEE.Item Productivity Analysis of Shuttering Works for Sewage Treatment Plant(Springer Science and Business Media Deutschland GmbH, 2021) Pandey, A.; Chaudhary, P.K.; Das, B.B.Formwork is considered as important element of construction projects like in traditional reinforced concrete infrastructure projects. It is labor-intensive work that requires highly skilled workers such as carpenters, bar benders, etc., to execute the work more accurately and efficiently. In view of the fact that it is difficult to find high-skilled workers for formwork process and hence it is important to find the ways or methods of formwork construction that is less labor dependent or in other words methods that are highly productive with minimum number of workers. The quality of formwork exerts a direct influence on the surface of concrete and on its dimensions. Since reinforced concrete work is involved in majority of the buildings, the level of workmanship of the construction project can be identified by seeing the quality of formwork. In order to improve the productivity of the formwork process and its quality then it is necessary to improve its working methodology by identifying the bottlenecks using scientific management. And as we very well know that money is always the center of discussion in our construction projects. To complete the project within its expected, designed cost of the project is one of the major requirements of the project to become a successful project. That’s why the topic ‘Productivity analysis in shuttering for Sewer Treatment Plant Project’ is a great tool to analyze the shortcomings in the present methodology of formwork erection and to mechanize a highly effective model for formwork and this can be achieved by the productivity analysis. © 2021, Springer Nature Singapore Pte Ltd.Item Deepfake Audio Detection Using Quantum Learning Models(Institute of Electrical and Electronics Engineers Inc., 2024) Pandey, A.; Rudra, B.Artificial intelligence makes it easy for humans to create high-quality images, speech, audio dubbing, and more. However, this technology is often misused to create fake content, such as phony speech, which is then made public to tarnish someone's image. This technology is known as deepfake, which uses deep learning, a field of artificial intelligence, to generate fake content. Advancements in deepfake technology pose the challenge of detecting fake content. Although many classical models exist to detect fake content, they often do not consider suitable audio features, and training these classical models is resource-intensive. Therefore, in this paper, we use a recently created real-time AI-generated fake speech dataset and propose a method to detect fake content using quantum learning models. This emerging technology leverages the properties of quantum mechanics to increase processing speed. We have trained the quantum learning models using the Lightning Qubit simulator. © 2024 IEEE.Item Hybrid Classical Quantum Learning Model Framework for Detection of Deepfake Audio(Science and Technology Publications, Lda, 2025) Pandey, A.; Rudra, B.Artificial intelligence (AI) has simplified individual tasks compared to earlier times. However, it also enables the creation of fake images, audio, and videos that can be misused to tarnish the reputation of a person on social media. The rapid advancement of deepfake technology presents significant challenges in detecting such fabricated content. Therefore, in this paper, we particularly focus on the deepfake audio detection. Many Classical models exist to detect deepfake audio, but they often overlook critical audio features, and training these models can be computationally resource-intensive. To address this issue, we used a real-time AI-generated fake speech dataset, which includes all the necessary features required to train models and used Quantum Machine Learning (QML) techniques, which follow principles of quantum mechanics to process the data simultaneously. We propose a hybrid Classical-Quantum Learning Model that takes advantage of Classical and Quantum Machine Learning. The hybrid model is trained on a real-time AI-generated fake speech dataset, and we compare the performance with existing Classical and Quantum models in this area. Our results show that the hybrid Classical-Quantum model gives an accuracy of 98.81% than the Quantum Support vector Machine (QSVM) and Quantum Neural Network (QNN). © 2025 by SCITEPRESS – Science and Technology Publications, Lda.Item Function Scheduling with Data Security in Serverless Computing Systems(Institute of Electrical and Electronics Engineers Inc., 2025) Saha, S.; Pandey, A.; Addya, S.K.; Brata Nath, S.In serverless computing, the service provider takes full responsibility for function management. However, serverless computing has many challenges regarding data security and function scheduling. To address these challenges, we have proposed a system to secure the data of an end-user. We also aim to meet the quality of service (QoS) for the end-user requests. This work presents a Simulated Annealing-based optimization algorithm for function placement. Also, we have Hyperledger Fabric, a blockchain framework in the system architecture for securing the data of an end-user. We have conducted experiments in Amazon Elastic Compute Cloud (EC2) taking virtual machine instances. The experiments in Amazon EC2 indicate that the proposed system secures the data and enhances the end-user's QoS. © 2025 IEEE.Item Binarization in DeepFake Audio Detection: A Comparative Study and Performance Analysis(Institute of Electrical and Electronics Engineers Inc., 2025) Gowhar, S.; Pandey, A.; Rudra, B.DeepFake audio, generated through advanced AI techniques, poses significant risks such as fraud, misinformation, and identity theft. As the quality of synthetic audio improves, detecting such fakes has become increasingly challenging. Traditional detection methods struggle to keep pace as AI-generated voices replicate speech patterns, tone, and pitch convincingly. While computationally intensive large-scale models can help detect DeepFakes generated by AI, their resource requirements make them impractical for deployment on mobile devices as well as on resource-constrained devices. This paper proposes a lightweight yet effective approach using binarized neural networks (BNNs) and further enhancements using additional dense layers and stacked modeling to overcome these challenges. We conduct a comprehensive performance analysis of the network and compare it with various machine learning and neural network methods to evaluate the tradeoff between detection accuracy and computational efficiency as an effect of binarization and precision loss in feature embeddings. © 2025 IEEE.Item An Effective Approach for Deepfake Video Detection using Binarized Neural Network(Institute of Electrical and Electronics Engineers Inc., 2025) Praveen, K.; Pandey, A.; Rudra, B.The rise of DeepFake technologies, especially in audio and video, poses significant threats to information integrity, security, and privacy. Artificially driven Artificial Intelligence (AI) methods and their advancement make it difficult to trace synthetic media through deepfakes that closely approximate real speech, facial expressions, and body movements. Consequently, traditional methods of detecting these are losing the race because they cannot compete with the newly invented methods that are more advanced in comparison. This paper proposes a lightweight and scalable approach to deepfake video detection using Binarized Neural Networks (BNNs). We integrate BNNs with Convolutional Neural Networks (CNNs) and Multi-task Cascaded Convolutional Networks (MTCNN) to boost feature extraction and analysis while making sure that this is done at a computational efficiency, especially to be deployed in resource-constrained systems such as mobile and embedded devices. The binarization of network weights and activations naturally deals with the trade-off regarding detection accuracy and computational cost. Our approach introduces a practical solution for real-time deepfake detection, thus advancing toward more secure and trusted digital environments. Our proposed model has achieved an accuracy of 80%. © 2025 IEEE.