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208 results on '"Auluck, Nitin"'

1. A Framework for Performance Optimization of Internet of Things Applications

Author

Almurshed, Osama, Meshoul, Souham, Muftah, Asmail, Kaushal, Ashish Kumar, Almoghamis, Osama, Petri, Ioan, Auluck, Nitin, Rana, Omer, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zeinalipour, Demetris, editor, Blanco Heras, Dora, editor, Pallis, George, editor, Herodotou, Herodotos, editor, Trihinas, Demetris, editor, Balouek, Daniel, editor, Diehl, Patrick, editor, Cojean, Terry, editor, Fürlinger, Karl, editor, Kirkeby, Maja Hanne, editor, Nardelli, Matteo, editor, and Di Sanzo, Pierangelo, editor

Published
2024
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2. Scheduling Algorithms for Hierarchical Fog Networks

Author

Kaur, Amanjot and Auluck, Nitin

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Fog computing brings the functionality of the cloud near the edge of the network with the help of fog devices/micro data centers ($mdcs$). Job scheduling in such systems is a complex problem due to the hierarchical and geo-distributed nature of fog devices. We propose two fog scheduling algorithms, named $FiFSA$ (Hierarchical $Fi$rst $F$og $S$cheduling $A$lgorithm) and $EFSA$ ( Hierarchical $E$lected $F$og $S$cheduling $A$lgorithm). We consider a hierarchical model of fog devices, where the computation power of fog devices present in higher tiers is greater than those present in lower tiers. However, the higher tier fog devices are located at greater physical distance from data generation sources as compared to lower tier fog devices. Jobs with varying granularity and cpu requirements have been considered. In general, jobs with modest cpu requirements are scheduled on lower tier fog devices, and jobs with larger cpu requirements are scheduled on higher tier fog devices or the cloud data center $(cdc)$. The performance of $FiFSA$ and $EFSA$ has been evaluated using a real life workload trace on various simulated fog hierarchies as well as on a prototype testbed. Employing $FiFSA$ offers an average improvement of 27% and 57.9% in total completion time and an improvement of 32% and 61% in cost as compared to Longest Time First ($LTF$) and cloud-only ($cdc-only$) scheduling algorithms, respectively. Employing $EFSA$ offers an average improvement of 48% and 70% in total completion time and an improvement of 52% and 72% in cost as compared to $LTF$ and $cdc-only$ respectively.

Published
2021

6. An IoT-Based Intelligent Irrigation Management System

Author

Sahi, Mansi, Auluck, Nitin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Patgiri, Ripon, editor, Bandyopadhyay, Sivaji, editor, Borah, Malaya Dutta, editor, and Emilia Balas, Valentina, editor

Published
2022
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7. Dynamic hierarchical intrusion detection task offloading in IoT edge networks.

Author

Sahi, Mansi, Auluck, Nitin, Azim, Akramul, and Maruf, Md Al

Subjects
ARTIFICIAL neural networks, STREAMING video & television, FEATURE extraction, EXTRACTION techniques, QUALITY of service, INTRUSION detection systems (Computer security)
Abstract

The Internet of Things (IoT) has gained widespread importance in recent time. However, the related issues of security and privacy persist in such IoT networks. Owing to device limitations in terms of computational power and storage, standard protection approaches cannot be deployed. In this article, we propose a lightweight distributed intrusion detection system (IDS) framework, called FCAFE‐BNET (Fog based Context Aware Feature Extraction using BranchyNET). The proposed FCAFE‐BNET approach considers versatile network conditions, such as varying bandwidths and data loads, while allocating inference tasks to cloud/edge resources. FCAFE‐BNET is able to adjust to dynamic network conditions. This can be advantageous for applications with particular quality of service requirements, such as video streaming or real‐time communication, ensuring a steady and reliable performance. Early exit deep neural networks (DNNs) have been employed for faster inference generation at the edge. Often, the weights that the model learns in the initial layer may be sufficiently qualified to perform the required classification tasks. Instead of using subsequent layers of DNNs for generating the inference, we have employed the early‐exit mechanism in the DNNs. Such DNNs help to predict a wide range of testing samples through these early‐exit branches, upon crossing a threshold. This method maintains the confidence values corresponding to the inference. Employing this approach, we achieved a faster inference, with significantly high accuracy. Comparative studies exploit manual feature extraction techniques, that can potentially overlook certain valuable patterns, thus degrading classification performance. The proposed framework converts textual/tabular data into 2‐D images, allowing the DNN model to autonomously learns its own features. This conversion scheme facilitated the identification of various intrusion types, ranging from 5 to 14 different categories. FCAFE‐BNET works for both network‐based and host‐based IDS: NIDS and HIDS. Our experiments demonstrate that, in comparison with recent approaches, FCAFE‐BNET achieves a 39.12%–50.23% reduction in the total inference time on benchmark real‐world datasets, such as: NSL‐KDD, UNSW‐NB 15, ToN_IoT, and ADFA_LD. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery.

Author

Ganesan, Rajarajan, Kaur, Tarunpreet, Mittal, Alisha, Sahi, Mansi, Konar, Sushant, Samra, Tanvir, Puri, Goverdhan Dutt, Thingnum, Shayam Kumar Singh, and Auluck, Nitin

Subjects
CARDIAC surgery, TIME series analysis, MACHINE learning, DATA distribution, SURVIVAL rate
Abstract

The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly known as drift. Therefore, it is important for ML models to be capable of detecting any changes or drift in the data distribution and updating the ML model accordingly. This study presents various drift detection techniques to identify drift in the survival outcomes of patients who underwent cardiac surgery. Additionally, this study proposes several drift adaptation strategies, such as adaptive learning, incremental learning, and ensemble learning. Through a detailed analysis of the results, the study confirms the superior performance of ensemble model, achieving a minimum mean absolute error (MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay, respectively. Furthermore, the models that incorporate a drift adaptive framework exhibit superior performance compared to the models that do not include such a framework. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Incentivising Resource Sharing in Edge Computing Applications

Author

Petri, Ioan, Rana, Omer F., Bignell, Joseph, Nepal, Surya, Auluck, Nitin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Pham, Congduc, editor, Altmann, Jörn, editor, and Bañares, José Ángel, editor

Published
2017
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12. Controlled Duplication Scheduling of Real-Time Precedence Tasks on Heterogeneous Multiprocessors

Author

Singh, Jagpreet, Auluck, Nitin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Desai, Narayan, editor, and Cirne, Walfredo, editor

Published
2017
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23. An Integrated Scheduling Algorithm for Precedence Constrained Hard and Soft Real-Time Tasks on Heterogeneous Multiprocessors

Author

Auluck, Nitin, Agrawal, Dharma P., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Laurence T., editor, Guo, Minyi, editor, Gao, Guang R., editor, and Jha, Niraj K., editor

Published
2004
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36. Load balancing aware scheduling algorithms for fog networks.

Author

Singh, Anil and Auluck, Nitin

Subjects
FOG, ALGORITHMS, HEURISTIC algorithms, SERVER farms (Computer network management), CLOUD computing, LOAD balancing (Computer networks), PRODUCTION scheduling
Abstract

Summary: Fog networks have attracted the attention of researchers recently. The idea is that a part of the computation of a job/application can be performed by fog devices that are located at the network edge, close to the users. Executing latency sensitive applications on the cloud may not be feasible, owing to the significant communication delay involved between the user and the cloud data center (cdc). By the time the application traverses the network and reaches the cloud data center, it might already be too late. However, fog devices, also known as mobile data centers (mdcs), are capable of executing such latency sensitive applications. In this paper, we study the problem of balancing the application load while taking account of security constraints of jobs, across various mdcs in a fog network. In case a particular mdc does not have sufficient capacity to execute a job, the job needs to be migrated to some other mdc. To this end, we propose three heuristic algorithms: minimum distance, minimum load, and minimum hop distance and load (MHDL). In addition, we also propose an ILP‐based algorithm called load balancing aware scheduling ILP (LASILP) for solving the task mapping and scheduling problem. The performance of the proposed algorithms have been compared with the cloud only algorithm and another heuristic algorithm called fog‐cloud‐placement (FCP). Simulation results performed on real‐life workload traces reveal that the MHDL heuristic performs better as compared to other scheduling policies in the fog computing environment while meeting application privacy requirements. [ABSTRACT FROM AUTHOR]

Published
2020
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39. RT-SANE

Author

Singh, Anil, primary, Auluck, Nitin, additional, Rana, Omer, additional, Jones, Andrew, additional, and Nepal, Surya, additional

Published
2017
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47. Implementing Autonomous Navigation on an Omni Wheeled Robot Using 2D LiDAR, Tracking Camera and ROS

Author

Bhorpe, Atharva, Padalkar, Pratik, Kadam, Pawan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Borah, Malaya Dutta, editor, Laiphrakpam, Dolendro Singh, editor, Auluck, Nitin, editor, and Balas, Valentina Emilia, editor

Published
2024
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48. Convolutional Gated MLP: Combining Convolutions and gMLP

Author

Rajagopal, A., Nirmala, V., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Borah, Malaya Dutta, editor, Laiphrakpam, Dolendro Singh, editor, Auluck, Nitin, editor, and Balas, Valentina Emilia, editor

Published
2024
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49. Facial Recognition-Based Automatic Attendance Management System Using Deep Learning

Author

Nath, Saranga Pani, Borah, Manditjyoti, Das, Debojit, Kalita, Nilam Kumar, Hussain, Zakir, Borah, Malaya Dutta, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Borah, Malaya Dutta, editor, Laiphrakpam, Dolendro Singh, editor, Auluck, Nitin, editor, and Balas, Valentina Emilia, editor

Published
2024
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50. Applications of Machine Learning for Face Mask Detection During COVID-19 Pandemic

Author

Khan, Sarfraz Fayaz, Khan, Mohammad Ahmar, Al-Quadah, Rabiah, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Borah, Malaya Dutta, editor, Laiphrakpam, Dolendro Singh, editor, Auluck, Nitin, editor, and Balas, Valentina Emilia, editor

Published
2024
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