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Articles 121 - 150 of 919
Full-Text Articles in Computer Sciences
Effective Data Sharing In An Edge-Cloud Model: Security Challenges And Solutions, Arijit Karati, Sajal K. Das
Effective Data Sharing In An Edge-Cloud Model: Security Challenges And Solutions, Arijit Karati, Sajal K. Das
Computer Science Faculty Research & Creative Works
The proposed protocol offers privacy-preserving authentication across several cloud platforms, flexible key management for consumer data protection, and effective user revocation. Performance evaluation demonstrates that the proposed framework supports low latency, safe unified remote access, and data privacy in the contemporary edge-enabled environment.
Reinforcement Learning Based Proactive Entanglement Swapping For Quantum Networks, Tasdiqul Islam, Md Arifuzzaman, Engin Arslan
Reinforcement Learning Based Proactive Entanglement Swapping For Quantum Networks, Tasdiqul Islam, Md Arifuzzaman, Engin Arslan
Computer Science Faculty Research & Creative Works
Entanglement generation and swapping is a difficult process due to probabilistic nature of quantum mechanics. To overcome this issue, existing quantum routing algorithms try to create entanglement on multiple paths between source and destination. Although it is possible to save entangled qubits on unused links using quantum memories, the quantum routing algorithms discard them and try creating new entanglement in each time slot. In this work, we leverage the longevity of entanglement and introduce two enhancements to improve the performance of existing routing algorithms: (i) The generation and caching of entanglements across multiple time slots, and (ii) the proactively executing …
Maximizing Network Throughput In Heterogeneous Uav Networks, Shuyue Li, Jing Li, Chaocan Xiang, Wenzheng Xu, Jian Peng, Ziming Wang, Weifa Liang, Xinwei Yao, Xiaohua Jia, Sajal K. Das
Maximizing Network Throughput In Heterogeneous Uav Networks, Shuyue Li, Jing Li, Chaocan Xiang, Wenzheng Xu, Jian Peng, Ziming Wang, Weifa Liang, Xinwei Yao, Xiaohua Jia, Sajal K. Das
Computer Science Faculty Research & Creative Works
In this paper we study the deployment of an Unmanned Aerial Vehicle (UAV) network that consists of multiple UAVs to provide emergent communication service for people who are trapped in a disaster area, where each UAV is equipped with a base station that has limited computing capacity and power supply, and thus can only serve a limited number of people. Unlike most existing studies that focused on homogeneous UAVs, we consider the deployment of heterogeneous UAVs where different UAVs have different computing capacities. We study a problem of deploying K heterogeneous UAVs in the air to form a temporarily connected …
Prevention Of Web Scraping And Copy And Paste Of Content By Font Obfuscation, Mingkui Wei, Yao Liu, Zhuo Lu, Junjie Xiong
Prevention Of Web Scraping And Copy And Paste Of Content By Font Obfuscation, Mingkui Wei, Yao Liu, Zhuo Lu, Junjie Xiong
Computer Science Faculty Research & Creative Works
A method and system provide and utilize obfuscated fonts for displayable content. Responsive to a request for displayable content having text, a text portion of the requested displayable content to be obfuscated is determined. For that text portion, obfuscated fonts are provided, by retrieving obfuscated fonts or by generating obfuscated fonts from a set of obfuscated glyphs created from a plurality of glyphs representative of a plurality of characters of the text portion of the displayable content. The obfuscated fonts can be created by assigning obfuscated glyphs of the set into the obfuscated fonts in accordance with one or more …
Scalable Pythagorean Mean-Based Incident Detection In Smart Transportation Systems, Md Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Abhishek Dubey, Keiichi Yasumoto, Sajal K. Das
Scalable Pythagorean Mean-Based Incident Detection In Smart Transportation Systems, Md Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Abhishek Dubey, Keiichi Yasumoto, Sajal K. Das
Computer Science Faculty Research & Creative Works
Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. to materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. in this article, we first propose a scalable data-driven anomaly-Based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. …
Hls Taking Flight: Toward Using High-Level Synthesis Techniques In A Space-Borne Instrument, Marion Sudvarg, Chenfeng Zhao, Ye Htet, Meagan Konst, Thomas Lang, Nick Song, Roger D. Chamberlain, Jeremy Buhler, James H. Buckley
Hls Taking Flight: Toward Using High-Level Synthesis Techniques In A Space-Borne Instrument, Marion Sudvarg, Chenfeng Zhao, Ye Htet, Meagan Konst, Thomas Lang, Nick Song, Roger D. Chamberlain, Jeremy Buhler, James H. Buckley
Computer Science Faculty Research & Creative Works
FPGAs are widely deployed on high-energy astrophysics telescopes to preprocess and reduce sensor data read out by front-end electronics. Across instruments, these computational pipelines have similar semantics, sharing common stages such as pedestal subtraction, signal integration, zero-suppression, island detection, and centroiding. However, diverse telescope designs require unique implementations of these algorithms, and the logic is often rewritten from scratch for a new instrument. As an alternative, High-Level Synthesis (HLS) tools enable these algorithms to be implemented in a high-level language, which eases modifications and enables fast prototyping and deployment. Nonetheless, writing performant HLS code requires augmentation of the code with …
Rainbowcake: Mitigating Cold-Starts In Serverless With Layer-Wise Container Caching And Sharing, Hanfei Yu, Rohan Basu Roy, Christian Fontenot, Devesh Tiwari, Jian Li, Hong Zhang, Hao Wang, Seung Jong Park
Rainbowcake: Mitigating Cold-Starts In Serverless With Layer-Wise Container Caching And Sharing, Hanfei Yu, Rohan Basu Roy, Christian Fontenot, Devesh Tiwari, Jian Li, Hong Zhang, Hao Wang, Seung Jong Park
Computer Science Faculty Research & Creative Works
Serverless Computing Has Grown Rapidly as a New Cloud Computing Paradigm that Promises Ease-Of-Management, Cost-Efficiency, and Auto-Scaling by Shipping Functions Via Self-Contained Virtualized Containers. Unfortunately, Serverless Computing Suffers from Severe Cold-Start Problems - -Starting Containers Incurs Non-Trivial Latency. Full Container Caching is Widely Applied to Mitigate Cold-Starts Yet Has Recently Been Outperformed by Two Lines of Research: Partial Container Caching and Container Sharing. However, Either Partial Container Caching or Container Sharing Techniques Exhibit their Drawbacks. Partial Container Caching Effectively Deals with Burstiness While Leaving Cold-Start Mitigation Halfway; Container Sharing Reduces Cold-Starts by Enabling Containers to Serve Multiple Functions While Suffering …
Drone-Based Bug Detection In Orchards With Nets: A Novel Orienteering Approach, Francesco Betti Sorbelli, Federico Coró, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti
Drone-Based Bug Detection In Orchards With Nets: A Novel Orienteering Approach, Francesco Betti Sorbelli, Federico Coró, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti
Computer Science Faculty Research & Creative Works
The Use of Drones for Collecting Information and Detecting Bugs in Orchards Covered by Nets is a Challenging Problem. the Nets Help in Reducing Pest Damage, But They Also Constrain the Drone's Flight Path, Making It Longer and More Complex. to Address This Issue, We Model the Orchard as an Aisle-Graph, a Regular Data Structure that Represents Consecutive Aisles Where Trees Are Arranged in Straight Lines. the Drone Flies Close to the Trees and Takes Pictures at Specific Positions for Monitoring the Presence of Bugs, But its Energy is Limited, So It Can Only Visit a Subset of Positions. to …
Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy
Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy
Computer Science Faculty Research & Creative Works
Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed …
Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Computer Science Faculty Research & Creative Works
The Capacity to Generalize to Future Unseen Data Stands as One of the Utmost Crucial Attributes of Deep Neural Networks. Sharpness-Aware Minimization (SAM) Aims to Enhance the Generalizability by Minimizing Worst-Case Loss using One-Step Gradient Ascent as an Approximation. However, as Training Progresses, the Non-Linearity of the Loss Landscape Increases, Rendering One-Step Gradient Ascent Less Effective. on the Other Hand, Multi-Step Gradient Ascent Will Incur Higher Training Cost. in This Paper, We Introduce a Normalized Hessian Trace to Accurately Measure the Curvature of Loss Landscape on Both Training and Test Sets. in Particular, to Counter Excessive Non-Linearity of Loss Landscape, …
Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch
Computer Science Faculty Research & Creative Works
The Transferability of Adversarial Examples is of Central Importance to Transfer-Based Black-Box Adversarial Attacks. Previous Works for Generating Transferable Adversarial Examples Focus on Attacking Given Pretrained Surrogate Models While the Connections between Surrogate Models and Adversarial Trasferability Have Been overlooked. in This Paper, We Propose Lipschitz Regularized Surrogate (LRS) for Transfer-Based Black-Box Attacks, a Novel Approach that Transforms Surrogate Models towards Favorable Adversarial Transferability. using Such Transformed Surrogate Models, Any Existing Transfer-Based Black-Box Attack Can Run Without Any Change, Yet Achieving Much Better Performance. Specifically, We Impose Lipschitz Regularization on the Loss Landscape of Surrogate Models to Enable a Smoother …
Technical Note: Generalizable And Promptable Artificial Intelligence Model To Augment Clinical Delineation In Radiation Oncology, Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, William W. Wong, Sujay A. Vora, Dajiang Zhu, Tianming Liu, Wei Liu
Technical Note: Generalizable And Promptable Artificial Intelligence Model To Augment Clinical Delineation In Radiation Oncology, Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, William W. Wong, Sujay A. Vora, Dajiang Zhu, Tianming Liu, Wei Liu
Computer Science Faculty Research & Creative Works
Background: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning-based auto-segmentation approaches face two challenges in clinical practice: generalizability and human-AI interaction. A generalizable and prompt able auto-segmentation model, which segments OARs of multiple disease sites simultaneously and supports on-the-fly human-AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning. Purpose: Meta's segment anything model (SAM) was proposed as a generalizable and prompt able model for next-generation …
Improved Algorithms For Co-Scheduling Of Edge Analytics And Routes For Uav Fleet Missions, Aakash Khochare, Francesco Betti Sorbelli, Yogesh Simmhan, Sajal K. Das
Improved Algorithms For Co-Scheduling Of Edge Analytics And Routes For Uav Fleet Missions, Aakash Khochare, Francesco Betti Sorbelli, Yogesh Simmhan, Sajal K. Das
Computer Science Faculty Research & Creative Works
Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visit waypoints and accomplish activities as part of their mission. a common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. to this end, for a fleet of drones, we propose a novel Mission Scheduling Problem (MSP) that co-schedules the flight routes to visit and record video …
Smartgrid-Ng: Blockchain Protocol For Secure Transaction Processing In Next Generation Smart Grid, Lokendra Vishwakarma, Debasis Das, Sajal K. Das, Christian Becker
Smartgrid-Ng: Blockchain Protocol For Secure Transaction Processing In Next Generation Smart Grid, Lokendra Vishwakarma, Debasis Das, Sajal K. Das, Christian Becker
Computer Science Faculty Research & Creative Works
With the advent of Blockchain and the Internet of Things (IoT), the Smart Grid is a rapidly growing technology in decentralized energy distribution and trading. However, this advancement came with some serious cyber security challenges and attacks, such as single-point failure due to a centralized architecture of smart grids, slow transaction processing, emerging cybersecurity threats, double-spending, fork, and fault tolerance. We propose a comprehensive framework for the smart grid called SmartGrid-NG to solve all these issues. Instead of using blockchain as a blackbox plugin tool, we also propose a reputation-based blockchain protocol called GridChain to increase the performance of blockchain-based …
Splitfed-Based Patient Severity Prediction And Utility Maximization In Industrial Healthcare 4.0, Himanshu Singh, Biken Moirangthem, Ajay Pratap, Shilpi Kumari, Abhishek Kumar, Sajal K. Das
Splitfed-Based Patient Severity Prediction And Utility Maximization In Industrial Healthcare 4.0, Himanshu Singh, Biken Moirangthem, Ajay Pratap, Shilpi Kumari, Abhishek Kumar, Sajal K. Das
Computer Science Faculty Research & Creative Works
The healthcare industry has transitioned from traditional healthcare 1.0 to AI-powered healthcare 4.0. However, overall cost for patient treatment remains high and challenging to manage due to the absence of a centralized cost evaluation mechanism before hospital visits. Therefore, in this paper, we devise a cloud-based mechanism to calculate hospitals' star rating based on questionnaire with the application of Z-score and K∗clustering algorithm. To evaluate disease severity at cloud, splitfed technique is utilized in coordination with Wireless Body Area Network (WBAN). Finally, the cloud calculates provisional treatment costs and finds a preferable hospital with a low payable treatment cost and …
Approximation Algorithm For Connected Submodular Function Maximization Problems, Wenzheng Xu, He Xue, Jing Li, Weifa Liang, Zichuan Xu, Pan Zhou, Xiaohua Jia, Sajal K. Das
Approximation Algorithm For Connected Submodular Function Maximization Problems, Wenzheng Xu, He Xue, Jing Li, Weifa Liang, Zichuan Xu, Pan Zhou, Xiaohua Jia, Sajal K. Das
Computer Science Faculty Research & Creative Works
In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K, the problem is to find a subset S with K nodes from a graph G so that a given submodular function f (S) on S is maximized while the induced subgraph G[S] by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas …
Tasr: A Novel Trust-Aware Stackelberg Routing Algorithm To Mitigate Traffic Congestion, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das
Tasr: A Novel Trust-Aware Stackelberg Routing Algorithm To Mitigate Traffic Congestion, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das
Computer Science Faculty Research & Creative Works
A Stackelberg routing platform (SRP) reduces congestion in one-shot traffic networks by proposing optimal route recommendations to the selfish travelers. Traditionally, Stackel-berg routing is cast as a partial control problem where a fraction of the traveler flow complies with route recommendations, while the remaining responds as selfish travelers. In this paper, we formulate a novel Stackelberg routing framework where the agents exhibit probabilistic compliance by accepting SRP's route recommendations with a trust probability. Specifically, we propose a greedy Trust-Aware Stackelberg Routing algorithm (in short, TASR) for SRP to compute unique path recommendations to each traveler flow with a unique demand. …
Shedding Light On Software Engineering-Specific Metaphors And Idioms, Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski
Shedding Light On Software Engineering-Specific Metaphors And Idioms, Mia Mohammad Imran, Preetha Chatterjee, Kostadin Damevski
Computer Science Faculty Research & Creative Works
Use of figurative language, such as metaphors and idioms, is common in our daily-life communications, and it can also be found in Software Engineering (SE) channels, such as comments on GitHub. Automatically interpreting figurative language is a challenging task, even with modern Large Language Models (LLMs), as it often involves subtle nuances. This is particularly true in the SE domain, where figurative language is frequently used to convey technical concepts, often bearing developer affect (e.g., 'spaghetti code). Surprisingly, there is a lack of studies on how figurative language in SE communications impacts the performance of automatic tools that focus on …
On The K-Weak Coverage Of Random Mobile Sensors, Sajal K. Das, Rafal Kapelko
On The K-Weak Coverage Of Random Mobile Sensors, Sajal K. Das, Rafal Kapelko
Computer Science Faculty Research & Creative Works
This paper studies the fundamental problem of energy consumption in the movement of mobile random sensors ensuring k-weak coverage on the domain. In particular, we analyze two notions of k-weak coverage on the unit square, namely (1) (k, x)-weak coverage in which every straight-line path across the width of the unit square passes through the sensing range of at least k sensors; and (2) (k, x, y)-weak coverage in which every straight-line path across the width and the length of the unit square passes through the sensing range of at least k sensors. The number of reliable and p-reliable sensors …
Real-Time Analysis Of Encrypted Dns Traffic For Threat Detection, Marta Moure-Garrido, Sajal K. Das, Celeste Campo, Carlos Garcia-Rubio
Real-Time Analysis Of Encrypted Dns Traffic For Threat Detection, Marta Moure-Garrido, Sajal K. Das, Celeste Campo, Carlos Garcia-Rubio
Computer Science Faculty Research & Creative Works
Domain Name System (DNS) tunneling is a well-known cyber-attack that allows data exfiltration - the attackers exploit this tunnel to extract sensitive information from the system. Advanced Persistent Threat (APT) attackers encapsulate malicious traffic in a DNS connection to elude security mechanisms such as Intrusion Detection System (IDS). Although different techniques have been implemented to detect these targeted attacks, their rise induces a threat to Cyber-Physical Systems (CPS). The DNS over HTTPS (DoH) tunnel detection is a challenge because the encrypted data prevents an analysis of DNS traffic content. In this paper, we present a novel detection system that identifies …
L3dml: Facilitating Geo-Distributed Machine Learning In Network Layer, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Bo Lei, Hongke Zhang, Sajal K. Das
L3dml: Facilitating Geo-Distributed Machine Learning In Network Layer, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Bo Lei, Hongke Zhang, Sajal K. Das
Computer Science Faculty Research & Creative Works
Geo-Distributed Machine Learning (GDML) aims to train large-scale machine learning models across geographically dispersed datacenters. However, the performance of GDML systems is constrained by the limited Wide Area Network (WAN) bandwidth and the presence of the straggler problem. Existing GDML designs often show contradictory effects in addressing these challenges, while in-network computing attempts are typically restricted to single datacenter environments rather than the more complex GDML scenarios. To overcome these limitations, this paper proposes L3DML to facilitate GDML using the P4-based Software-defined Network (SDN). Our approach incorporates three key innovations. Firstly, we introduce a novel network addressing scheme that enables …
Interlude: Interactions Between Labeled And Unlabeled Data To Enhance Semi-Supervised Learning, Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes
Interlude: Interactions Between Labeled And Unlabeled Data To Enhance Semi-Supervised Learning, Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes
Computer Science Faculty Research & Creative Works
Semi-supervised learning (SSL) seeks to enhance task performance by training on both labeled and unlabeled data. Mainstream SSL image classification methods mostly optimize a loss that additively combines a supervised classification objective with a regularization term derived solely from unlabeled data. This formulation often neglects the potential for interaction between labeled and unlabeled images. In this paper, we introduce InterLUDE, a new approach to enhance SSL made of two parts that each benefit from labeled-unlabeled interaction. The first part, embedding fusion, interpolates between labeled and unlabeled embeddings to improve representation learning. The second part is a new loss, grounded in …
Core-Periphery Multi-Modality Feature Alignment For Zero-Shot Medical Image Analysis, Xiaowei Yu, Lu Zhang, Zihao Wu, Dajiang Zhu
Core-Periphery Multi-Modality Feature Alignment For Zero-Shot Medical Image Analysis, Xiaowei Yu, Lu Zhang, Zihao Wu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Multi-modality learning, exemplified by the language-image pair pre-trained CLIP model, has demonstrated remarkable performance in enhancing zero-shot capabilities and has gained significant attention recently. However, simply applying language-image pre-trained CLIP to medical image analysis encounters substantial domain shifts, resulting in severe performance degradation due to inherent disparities between natural (non-medical) and medical image characteristics. To address this challenge and uphold or even enhance CLIP's zero-shot capability in medical image analysis, we develop a novel approach, Core-Periphery feature alignment for CLIP (CP-CLIP), to model medical images and corresponding clinical text jointly. To achieve this, we design an auxiliary neural network whose …
Enhancing Group-Wise Consistency In 3-Hinge Gyrus Matching Via Anatomical Embedding And Structural Connectivity Optimization, Chao Cao, Xiaowei Yu, Lu Zhang, Tong Chen, Yanjun Lyu, Tianming Liu, Dajiang Zhu
Enhancing Group-Wise Consistency In 3-Hinge Gyrus Matching Via Anatomical Embedding And Structural Connectivity Optimization, Chao Cao, Xiaowei Yu, Lu Zhang, Tong Chen, Yanjun Lyu, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Recently, a novel cortical folding pattern known as the 3-hinge gyrus (3HG) has been identified. 3HGs are defined as the convergence of the gyri coming from three distinct directions on gyral crests. In contrast to cortical regions, 3HGs are defined at a finer scale and they widely exist across different individuals, representing both commonalities and individualities of cortical folding patterns. It is important to note that 3HGs are identified in individual spaces, lacking natural cross-subject correspondences. To address this issue, we have developed a learning-based method to encode anatomical features of 3HGs into a set of embedding vectors that can …
Resource Aware Clustering For Tackling The Heterogeneity Of Participants In Federated Learning, Rahul Mishra, Hari Prabhat Gupta, Garvit Banga, Sajal K. Das
Resource Aware Clustering For Tackling The Heterogeneity Of Participants In Federated Learning, Rahul Mishra, Hari Prabhat Gupta, Garvit Banga, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated Learning Is A Training Framework That Enables Multiple Participants To Collaboratively Train A Shared Model While Preserving Data Privacy. The Heterogeneity Of Devices And Networking Resources Of The Participants Delay The Training And Aggregation. The Paper Introduces A Novel Approach To Federated Learning By Incorporating Resource-Aware Clustering. This Method Addresses The Challenges Posed By The Diverse Devices And Networking Resources Among Participants. Unlike Static Clustering Approaches, This Paper Proposes A Dynamic Method To Determine The Optimal Number Of Clusters Using Dunn Indices. It Enables Adaptability To The Varying Heterogeneity Levels Among Participants, Ensuring A Responsive And Customized Approach To …
Mime: Mobility-Induced Dynamic Matching For Partial Offloading In Vehicular Edge Computing, Mahmuda Akter, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das
Mime: Mobility-Induced Dynamic Matching For Partial Offloading In Vehicular Edge Computing, Mahmuda Akter, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das
Computer Science Faculty Research & Creative Works
Autonomous vehicles (AVs) execute compute intensive control operations like adjusting speed and steering, causing significant energy dissipation and latency due to resource limited onboard units (OBUs). Offloading these tasks to Roadside Units (RSUs) is a solution, but it faces challenges. First, the stringent latency requirements are impacted by the vehicle's stochastic velocity. Second, allocating limited RSU resources to numerous vehicles within its coverage area is difficult. This paper proposes the MIME framework to address these issues. We use Discrete Fourier transform (DFT) that computes the average velocity over an aperiodic velocity signal extracted from a real-world dataset. for resource allocation, …
A Hypergraph Approach To Deep Learning Based Routing In Software-Defined Vehicular Networks, Ankur Nahar, Nishit Bhardwaj, Debasis Das, Sajal K. Das
A Hypergraph Approach To Deep Learning Based Routing In Software-Defined Vehicular Networks, Ankur Nahar, Nishit Bhardwaj, Debasis Das, Sajal K. Das
Computer Science Faculty Research & Creative Works
Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-Based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-Based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements …
Collect Spatiotemporally Correlated Data In Iot Networks With An Energy-Constrained Uav, Wenzheng Xu, Heng Shao, Qunli Shen, Jian Peng, Wen Huang, Weifa Liang, Tang Liu, Xin Wei Yao, Tao Lin, Sajal K. Das
Collect Spatiotemporally Correlated Data In Iot Networks With An Energy-Constrained Uav, Wenzheng Xu, Heng Shao, Qunli Shen, Jian Peng, Wen Huang, Weifa Liang, Tang Liu, Xin Wei Yao, Tao Lin, Sajal K. Das
Computer Science Faculty Research & Creative Works
UAVs (Unmanned Aerial Vehicles) Are Promising Tools For Efficient Data Collections Of Sensors In IoT Networks. Existing Studies Exploited Both Spatial And Temporal Data Correlations To Reduce The Amount Of Collected Redundant Data, In Which Sensors Are First Partitioned Into Different Clusters, A Master Sensor In Each Cluster Then Collects Raw Data From Other Sensors And Compresses The Received Data. An Energy-Constrained UAV Finally Collects The Maximum Amount Of Compressed Data From Different Master Sensors. We However Notice That The Compressed Data From Only A Portion Of Clusters Are Collected By The UAV In The Existing Studies, While The Data …
Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Computer Science Faculty Research & Creative Works
Wireless Sensor and Actuator Networks (WSANs) are an effective technology for improving the efficiency and productivity in many industrial domains and are also the building blocks for the Industrial Internet of Things (IIoT). To support this trend, the IEEE has defined the 802.5.4 Time-Slotted Channel Hopping (TSCH) protocol. Unfortunately, TSCH does not provide any mechanism to manage node mobility, while many current industrial applications involve Mobile Nodes (MNs), e.g., mobile robots or wearable devices carried by workers. In this article, we present a framework to efficiently manage mobility in TSCH networks, by proposing an enhanced version of the Synchronized Single-hop …
Posca: Path Optimization For Solar Cover Amelioration In Urban Air Mobility, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das
Posca: Path Optimization For Solar Cover Amelioration In Urban Air Mobility, Debjyoti Sengupta, Anurag Satpathy, Sajal K. Das
Computer Science Faculty Research & Creative Works
Urban Air Mobility (UAM) encompasses both piloted and autonomous aerial vehicles, spanning from small unmanned aerial vehicles (UAVs) like drones to passenger-carrying personal air vehicles (PAVs), to revolutionize smart transportation in congested urban areas. This emerging paradigm is anticipated to offer disruptive solutions to the mobility challenges in congested cities. In this context, a pivotal concern centers on the sustainability of transitioning to this mode of transportation, especially with the focus on incorporating clean technology into developing innovative solutions from the ground up. Recent studies highlight that a significant portion of the total energy consumption in UAM can be attributed …