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Shortest Job First with Gateway-based Resource Management Strategy for Fog Enabled Cloud Computing

Shortest Job First with Gateway-based Resource Management St...

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Data engineering and communication Systems (ICDECS), International Conference on

作者： K. Anbazhagan Rotash Kumar R. Thilagavathy D. Anuradha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering Government Engineering College Jhalawar Rajasthan India Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Chennai India

In recent years, fog computing has extended cloud services to the network edge, lowering network latency and congestion and overcoming cloud computing restrictions. Many hurdles, however, must be overcome before the benefits of the fog-IoT paradigm for low-latency applications can be completely realized. The most challenging difficulty in IoT applications is developing efficient resource management strategies that maximize throughput while minimizing delay. IoT and fog computing, rather than simply utilizing cloud computing technology, have brought intriguing new data management solutions. Fog computing makes real-time analytics applications conceivable. After combining fog computing technologies with Internet of Things (IoT) applications, the system can reply in milliseconds. Existing fog resource managers focus on a subset of fog resource management parameters such as system response time, network bandwidth, energy consumption, and latency. No Fog resource management takes multiple factors into account at the same time for decision-making, which will become increasingly crucial in intelligent homes. Gateway-based Fog Computing pioneered the introduction of the shortest job (SJFGFC) to address these difficulties. The technique is validated in an IoT-based imaginative house automation scenario before being evaluated in a small-scale bright home experiment using the iFogSim toolbox and empirical models.

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An Enhanced Animal Species Classification and Prediction Engine using CNN

An Enhanced Animal Species Classification and Prediction Eng...

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International Conference on Edge Computing and Applications (ICECAA)

作者： P Kanaga Priya T Vaishnavi N Selvakumar G Ramesh Kalyan A Reethika Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering SNS College of Technology Coimbatore India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India

Animal species classification is a fundamental task in wildlife conservation, animal behavior studies, and biodiversity research. Convolutional Neural Networks (CNNs) have become a potent technique for automatic classification tasks in recent times. This abstract presents an overview of the use of CNNs for animal species classification. The proposed approach involves pre-processing of the input images, followed by feature extraction and classification using CNN architecture. The pre-processing step involves image resizing, normalization, and augmentation to enhance the resilience of the model. The feature extraction is performed by convolutional layers, followed by max-pooling layers, and fully connected layers for classification. Transfer learning is also utilized to leverage the pre-trained CNN models and fine-tune them for specific animal species classification tasks. The proposed approach achieves high accuracy of 98% and can be extended to various animal species classification tasks. Overall, CNNs provide an effective means for automated animal species classification, enabling more efficient and accurate animal behavior studies, and wildlife conservation efforts.

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Multi objective Ant Colony Optimization Technique for Task Scheduling in Cloud Computing

Multi objective Ant Colony Optimization Technique for Task S...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： S V Hemanth D. Kirubha Sheri Ramchandra Reddy T. Chelladurai A. Gnana Soundari G. Amirthayogam Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana India Department of Computer Science and Engineering Rajarajeswari College of engineering Bangalore Karnataka India Department of Computer Science and Engineering (AIML) CMR Technical Campus Hyderabad Telangana india Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Thandalam Tamil Nadu Department of Information Technology Hindustan Institute of Technology and Science (Deemed To Be University) Chennai Tamilnadu India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Cloud Computing (CC) is a powerful paradigm for accessing IT services over the internet, allowing multiple consumers to share resources. Therefore, it requires task scheduling, allocating, and gradually releasing resources. Hence, efficient Task Scheduling (TS) and Resource Allocation (RA) in cloud systems is crucial to optimize resource usage and ensure timely completion. Assigning computing tasks in a cloud environment is a multifaceted process affected by numerous factors, including network bandwidth availability, completion time, and cost considerations. Traditional scheduling models based on heuristic algorithms have limitations in complex in cloud environments. To tackle these issues, we introduce Multi objective Ant Colony Optimization (MACO) and Adaptive Sequence Dynamic Allocation (ASDA) techniques for TS and RA in the cloud environment. Initially, the tasks submitted globally are divided into long and short queues. After that, the MACO algorithm schedules the task based on queues. Subsequently, the proposed ASDA algorithm efficiently allocates the most appropriate Virtual Machine (VM). If a VM is already occupied, the proposed method promptly chooses the next available VM based on the progress of the work completion process. According to the experimental results, the proposed algorithm achieves significant cost savings compared to other algorithms.

关键词： Performance evaluation Cloud computing Ant colony optimization Schedules Costs Processor scheduling Heuristic algorithms

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Power-Efficient Deceptive Wireless Beamforming Against Eavesdroppers

Power-Efficient Deceptive Wireless Beamforming Against Eaves...

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2025 IEEE Wireless communications and Networking Conference, WCNC 2025

作者： Chrysanidis, Georgios Argyriou, Antonios Tran, Le-Nam Zhang, Yanming Liu, Yanwei University of Thessaly Department of Electrical and Computer Engineering Greece School of Electrical and Electronic Engineering University College Dublin Ireland The Chinese University of Hong Kong Department of Electronic Engineering Shatin NT Hong Kong Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9798350368369

Eavesdroppers of wireless signals want to infer as much as possible regarding the transmitter (Tx). Popular methods to minimize information leakage to the eavesdropper include covert communication, directional modulation, and beamforming with nulling. In this paper we do not attempt to prevent information leakage to the eavesdropper like the previous methods. Instead we propose to beamform the wireless signal at the Tx in such a way that it incorporates deceptive information. The beamformed orthogonal frequency division multiplexing (OFDM) signal includes a deceptive value for the Doppler (velocity) and range of the Tx. To design the optimal baseband waveform with these characteristics, we define and solve an optimization problem for power-efficient deceptive wireless beamforming (DWB). The relaxed convex Quadratic Program (QP) is solved using a heuristic algorithm. Our simulation results indicate that our DWB scheme can successfully inject deceptive information with low power consumption, while preserving the shape of the created beam. © 2025 IEEE.

关键词： Beamforming

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Deep Learning Empowered High Accuracy and Low Complexity Indoor Channel Prediction for Wireless communication Systems

Deep Learning Empowered High Accuracy and Low Complexity Ind...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Rong-Terng Juang Tong-Wen Wang Jun-Xiang Cao Hsin-Piao Lin Ding-Bing Lin Institute of Aerospace and Systems Engineering National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering Feng Chia University Taichung Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The rapid expansion of wireless communication mandates the development of efficient and computationally light solutions for base station deployment. Despite the manifold advantages of the latest 5G networks, challenges such as limited coverage and signal attenuation persist, underscoring the need for swift and precise estimation of base station coverage. To address this imperative, this paper introduces a deep learning-based indoor channel prediction model. Through comparative analysis with a traditional high-precision ray-tracing model, the paper showcases the computational prowess of the proposed deep-learning model. Once trained, it achieves an impressive 99.7% reduction in computation time while maintaining comparable accuracy to the ray-tracing model. The proposed method promises expedited and accurate evaluation of optimal base station placements, particularly beneficial for indoor networks such as 5G small cells or wireless LAN. By integrating into handheld platforms, users can swiftly input indoor layouts and base station locations to obtain signal coverage. Such integration not only reduces power consumption but also drives advancements in energy-efficient practices within telecommunication infrastructure.

关键词： Wireless communication Base stations Analytical models Wireless LAN Accuracy 5G mobile communication Computational modeling

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Advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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Mobile Networks and Wireless communications (ICMNWC), IEEE International Conference on

作者： R. Mohandas M Elavarasi RVS Praveen Hemanand Chittapragada C. Nithiya S. Prabagar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of BCA - IT Vels Institute of Science Technology & Advanced Studies Pallavaram India Digital Engineering and Assurance LTIMindtree Limited Hyderabad India Department of Computer Science and Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems.

关键词： Wireless communication Accuracy Computational modeling Scalability Boosting Rendering (computer graphics) Complexity theory Internet of Things Reliability Cyberattack

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Hybrid Threshold Speech Enhancement Scheme Using TEO And Wavelet Coefficients

Hybrid Threshold Speech Enhancement Scheme Using TEO And Wav...

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Electrical, electronics, Information and communication Technologies (ICEEICT), International Conference on

作者： Latha R Suhas A R B P Pradeep Kumar M.Mohammed Ibrahim Sathiyapriya V Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India

Speech Enhancement (SE) aims to improve the quality of degraded speech while maintaining its intelligibility. The Wavelet Transform (WT) has become a powerful tool of signal analysis thereby widely used in signal detection and signal denoising. In this paper, we propose an effective means of SE by a hybrid threshold scheme using WT. The proposed methodology looks into both falling the noise and preserving edges of the speech signal unlike the conventional Hybrid Threshold (HT) and Soft Threshold (ST) in the wavelet domain. The threshold value in the wavelet domain is maintained constant for all sub-bands of the signal which reduces denoising efficiency. A novel speech augmentation technique built on the wavelet onsets and time adaption of introduced by calculating wavelet coefficients of the Teager Energy. Performance analysis of speech enhancement techniques using Wavelet coefficients and Teager Energy Operator (TEO) with hybrid threshold method is done. The experiment is carried out for speech data with various values of SNR vacillating from -10 to +10 db with Additive White Gaussian Noise (AWGN).

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NeuroSparse: An Unsupervised Framework for Inferring Brain Connectivity in Autism Diagnosis

NeuroSparse: An Unsupervised Framework for Inferring Brain C...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Shuqi Zhang, Lijuan Lan, Qing Ma, Qiujie Huang, Huan Miao, Jiaqing Mou, Shiyu Luo, Cheng Yao, Dezhong Tan, Ying Southwest Minzu University Key Laboratory for Computer Systems of State Ethnic Affairs Commission Chengdu China Moe Key Lab for Neuroinformation University of Electronic Science and Technology of China Clinical Hospital Chengdu Brain Science Institute Chengdu China University of Malaya Faculty of Engineering Department of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350386226

Autism spectrum disorder (ASD) is a mental disorder that severely affects social interaction and communication skills. A timely and accurate diagnosis is crucial for effective intervention. However, as an objective diagnostic tool, the existence of spurious connections and noise within the functional connectivity (FC) matrix in the brain contribute to the complexity of ASD diagnosis. In this study, we propose an unsupervised sparse graph structure learning framework, called NeuroSparse, for reasoning regarding the topological connectivity relationships between brain regions. First, the framework fuses spatiotemporal information from static and dynamic FC to provide a comprehensive representation of complex brain networks. Next, the context and neighborhood information of nodes are captured as their local and global embeddings, and the latent representations of node embeddings are learned, which are then decoded to infer the connectivity relationships among the regions of interest. Finally, we introduce three innovative loss functions to regularize and optimize the sparse representation generation process. The experimental results showed that, after learning using NeuroSparse, the FC matrix achieved state-of-the-art performance in ASD diagnosis using only a simple multilayer perceptron, indicating its wide-ranging application potential. The code is available at https://***/yangshuqigit/NeuroSparse. © 2024 IEEE.

关键词： Unsupervised learning

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A Hybrid Learning-Based Q-Commerce Framework Evolving from E-Commerce

A Hybrid Learning-Based Q-Commerce Framework Evolving from E...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： X. Arogya Presskila J. Relin Francis Raj P. Selva Kumar G. Ram Sankar G. Vinoth Rajkumar P. Sundaravadivel Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Management Studies Francis Xavier Engineering College Tirunelveli India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering J. P. College of Engineering Tenkasi India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid evolution of E-Commerce has led to the emergence of Quick Commerce (Q-Commerce), a model that prioritizes ultra-fast deliveries and real-time decision-making. This paper proposes a Hybrid Learning-based framework that enhances Q-Commerce operations by integrating Self-Supervised, Supervised, Reinforcement, and Unsupervised Learning techniques. The model leverages BERT for self-supervised pretraining, enabling context-aware customer personalization and improved search relevance. LSTMs and CNNs are employed for demand forecasting and product classification, ensuring optimized inventory management. GNNs enhance personalized recommendations by analyzing user-product relationships. For logistics optimization, DQN and PPO are implemented to dynamically adjust last-mile delivery routes, reducing delivery times and operational costs. To ensure fraud prevention, AEs and GANs detect anomalous transaction behaviors, improving security while minimizing false positives. The proposed hybrid deployment strategy, combining cloud-based AI inference and Edge AI at micro-fulfillment centers (MFCs), enhances system responsiveness and scalability. The framework is evaluated using MAE, RMSE, HR, Fl-Score, and ROC curves, ensuring accurate demand prediction, optimized logistics, and effective fraud detection. Future advancements in Autonomous Deliveries, AI-Powered Warehousing, and Blockchain Integration will further revolutionize Q-Commerce, making it an indispensable extension of E-Commerce.

关键词： Costs Scalability Demand forecasting Edge AI Real-time systems Fraud electronic commerce Security Optimization Logistics

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An S-band Dielectric Rod Antenna Based on Substrate Integrated Waveguide (SIW) Technology

An S-band Dielectric Rod Antenna Based on Substrate Integrat...

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International Symposium on Antennas and Propagation (ISAP)

作者： LiBo Wang Shu Lin Yingqiao Wang Xinyue Zhang Xingqi Zhang Harbin Institute of Technology Harbin China Department of Communication Engineerings Jilin University Changchun China Department of Electronic Engineering Maynooth University Ireland Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

In order to reduce the profile of an S-band high-gain antenna, this paper designs and simulates an S-band dielectric rod antenna based on substrate integrated waveguide (SIW) technology. The antenna is composed of four media and achieves a low-reflection effect combination of feed-transition-emitter by leveraging boundary conditions and dielectric constants. The resulting printed dielectric rod antenna has a low electrical size profile and operates at 3.14-3.8 GHz with an impressive in-band gain greater than 8.5 dBi and a center operating frequency gain of 11.4 dBi. Its high gain, low profile, and small mass make it an excellent candidate for sub-6G applications.

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