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9th EAI international conference on Industrial Networks and Intelligent Systems, INISCOM 2023

9th EAI International Conference on Industrial Networks and ...

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9th EAI international conference on Industrial Networks and Intelligent Systems, INISCOM 2023

ISBN: (纸本)9783031473586

the proceedings contain 23 papers. the special focus in this conference is on Industrial Networks and Intelligent Systems. the topics include: Multi-modal Speech Emotion Recognition: Improving Accuracy through Fusion of VGGish and BERT Features with Multi-head Attention;performance Analysis of distributed Learning in Edge computing on Handwritten Digits Dataset;FLASH: Facial Landmark Detection Using Active Shape Model and Heatmap Regression;Adaptive Backstepping Sliding Mode Control for Speed of PMSM and DC-Link Voltage in Bidirectional Quasi Z-Source Inverter;neural Networks with Variational Quantum Circuits;sudden Cardiac Arrest Detection Using Deep Learning and Principal Component Analysis;Experimental Study on Fuzzy PD Control for Logistics Transportation Mobile Robot;MQTT-CB: Cloud Based Intelligent MQTT Protocol;enhancing Load Balancing in Cloud computing through Deadlock Prediction;Integrated Intelligent Agent for SNMP-Based Network Management System;A Secrecy Offloading in Radio Frequency Energy Harvesting NOMA Heterogeneous Mobile Edge computing Network;an Application of Non Negative Matrix Factorization in Text Mining;mitigating and Analysis of Memory Usage Attack in IoE System;physical Layer Security of Heterogenous Networks with Unreliable Wireless Backhaul and Small Cell Selections;Genetic Algorithms for Storage- and Energy-Aware Caching and Trajectory Optimisation Problem in UAV-Assisted Content Delivery Networks;joint Computation Offloading and Resource Allocation for Mobile Edge computing;an Open Source Wireless Communication Database for Radio Access Network;Performance Analysis of RF Energy Harvesting Mobile Edge computing Network Using NOMA Scheme with Dual Access Points;Joint Design of Reflection Coefficients and Beamforming in Double RIS-Assisted System;hybrid Beamforming Design for Multi-user mmWave Sum Rate Maximization;multiple Mobile Equipment Localization in Indoor Environment Based on Cell Sectoring.

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An Ensemble Deep Learning Model for Diabetic Retinopathy Identification 9

An Ensemble Deep Learning Model for Diabetic Retinopathy Ide...

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9th IEEE international conference on Smart Structures and Systems, ICSSS 2023

作者： Hussain, Mohammad Manzoor Shanmugam, P. Moorthi, K. Sakthivelu, U. Rajasekar, A. Kumar, R. Naresh B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Rajalakshmi Engineering College Department Computer Science and Engineering Chennai India Sairam Engineering College Department Electronics and Communication Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamilnadu Kattankulathur India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350384208

Diabetic Retinopathy (DR) is an eye disease that can potentially cause significant injury in those diagnosed with diabetes. the condition arises due to issues with blood circulation to the retina, the delicate tissue in the posterior part of the eye. DR is recognized as the leading factor contributing to visual impairment among those affected by diabetes. However, timely identification and intervention might mitigate the occurrence of this condition. Conversely, the timely identification of DR poses challenges, while the diagnostic process can be laborious and time-intensive. the automated detection of DR necessitates categorizing colour retinal fundus pictures. this article presents a computer-aided diagnostic strategy that utilizes Deep Learning (DL) algorithms. In order to classify retinal fundus pictures, this study employed a combination of DL models, specifically ResNet50 and Inception V3. the studies are being conducted on the DIARETDB1 dataset. An analysis is conducted to compare the results of the suggested methodology. the comparison study results show that the proposed approach has more significant accuracy levels than the other methods. A high degree of accuracy, that is, 98.37%, has been attained by utilising the proposed techniques. © 2023 IEEE.

关键词： Deep learning

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A distributed and Parallel Tensor Hierarchical Tucker Decomposition on Clouds 9

A Distributed and Parallel Tensor Hierarchical Tucker Decomp...

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9th international conference on Advanced Cloud and Big Data, CBD 2021

作者： Ning, Weikai Wu, Zebin Sun, Jin Yang, Jiandong Zhang, Yi Zhu, Yaoqin Wei, Zhihui Xia, Lei Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China China Satellite Maritime Tracking and Control Department Jiangyin China China Railway Shanghai Group Co. Ltd Shanghai China

ISBN: (纸本)9781665407458

Hierarchical Tucker decomposition is widely used in image processing, data mining and many other fields. With the increase of data volumes, the hierarchical Tucker decomposition requires much more amounts of memories and computing capacities, beyond the computing resources of a single computer. Accordingly, we propose a distributed and parallel tensor hierarchical Tucker decomposition on a cloud computing platform. Both coarse-grained parallelism and fine-grained parallelism are exploited in the hierarchical Tucker decomposition by splitting the computation tasks on the dimension tree nodes. Each individual node in the dimension tree is independent on the tree topology and can thus be executed in parallel. Experimental results indicate that the proposed method is effective in processing high-dimensional tensor data with significant speedups and high robustness. © 2022 IEEE.

关键词： Tensors

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Polaris Scheduler: SLO- and Topology-aware Microservices Scheduling at the Edge 15

Polaris Scheduler: SLO- and Topology-aware Microservices Sch...

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IEEE/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Pusztai, thomas Nastic, Stefan Morichetta, Andrea Pujol, Victor Casamayor Raith, Philipp Dustdar, Schahram Vij, Deepak Xiong, Ying Zhang, Zhaobo TU Wien Distributed Syst Grp Vienna Austria Futurewei Technol Inc Santa Clara CA USA

ISBN: (纸本)9781665460873

the continuous expansion of Edge computing calls for efficient scheduling techniques for the employed microservices. However, typical container schedulers often fall short when used in an Edge cluster with heterogeneous devices and unstable network connections, because they do not account for any network Quality of Service (QoS) requirements. this makes it hard for applications to fulfill their Service Level Objectives (SLOs). In this paper we present Polaris Scheduler, an SLO-aware scheduler for the Edge that is developed as part of the Linux Foundation Centaurus project. Polaris Scheduler optimizes the placement of an application's microservices to improve the fulfillment of their SLOs. To this end, it supports modeling of the Edge topology as a Cluster Topology Graph to capture the network quality characteristics and allows users to specify the dependencies among their application's microservices, as well as the network QoS requirements (bandwidth, latency, jitter, and packet drop) for each of them in the form of a Service Graph. Polaris Scheduler relies on a plugin-based approach to allow support for multiple SLOs. We implement our scheduler with plugins for meeting network QoS SLOs as a Kubernetes scheduler and evaluate it using a realistic traffic analysis and hazard detection use case.

关键词： Edge computing Microservices Scheduling Service Level Objectives Network Quality of Service

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Capacity-Based Trust System in Untrusted MEC Environments 9

Capacity-Based Trust System in Untrusted MEC Environments

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9th IEEE international conference on Internet of things - Systems, Management and Security (IOTSMS)

作者： Le, Van thanh El Ioini, Nabil Pahl, Claus Elahi, Mehdi Free Univ Bozen Bolzano Bolzano Italy Univ Bergen Bergen Norway

ISBN: (纸本)9798350320459

With the concept of Mobile Edge computing or Multi-access Edge computing (MEC), computation resources can be located closer to the users in proximity. MEC can operate specific services for the user requests to reduce overhead in the main cloud. the MEC infrastructure also facilitates service migration and relocation to follow the user movement in order to maintain service connection and therefore reduce the service downtime. However, since most MEC components are meant to be deployed on constrained IoT devices, they inherit many of the limitations that come with IoT, especially in terms of limited resources and security. In this paper, we focus on the capacity aspect of the MEC infrastructure to ensure the services have enough resources during the migration process. We note that the distributed and decentralized nature of MEC networks requires the additional endorsement of the capacity information reported by the network nodes, which is especially critical since MEC nodes can be hacked or tampered with, leading to failures during the migration process. To address this issue, we propose a machine learning-based solution that applies historical resources metric data to predict potential node behavior under attack scenarios. Our experiments showed that artificial neural network (ANN) is the best model for the production version that will be built as an extension in Kubernetes.

关键词： edge computing trust capacity resource kubernetes

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Tomato Leaf Disease Classification with Lightweight Federated Learning using Identically distributed Images 9

Tomato Leaf Disease Classification with Lightweight Federate...

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9th international conference on Signal Processing and Communication, ICSC 2023

作者： Trivedi, Naresh Kumar Maheshwari, Himani Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Gautam, Vinay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Department of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9798350383201

Tomatoes grown and consumed extensively in Asia are an essential source of protein (22% per Tomato) and energy (18% per Tomato). Bacterial, fungal, and other microbial diseases severely impact plant health and productivity for tomato farmers. Manual diagnosis of many diseases is challenging, especially in places that need access to crop protection experts. Automated disease identification is crucial for Tomato leaf protection and reduced crop loss. Unfortunately, no one method of categorizing leaf diseases in tomatoes has proven to be consistently accurate enough for widespread usage. the present study offers a streamlined federated deep learning framework to classify tomato leaf diseases while ensuring the preservation of sensitive data. this framework safeguards client information with a decentralized client-server architecture and verifies federated deep learning models with IID. the researchers piloted the framework with a single and several clients in both conventional and federated learning environments. After extracting features from numerous pre-trained models, AlexNet is selected as baseline because of its superior 99.53% accuracy. Next, IID datasets were put through their paces using federal learning (FL). the extra capabilities of the framework make it the ideal option for early Tomato leaf disease categorization. © 2023 IEEE.

关键词： Image classification

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Topology Construction with Minimum Total Time for Geo-distributed Decentralized Federated Learning 23

Topology Construction with Minimum Total Time for Geo-Distri...

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9th international conference on computing and Artificial Intelligence, ICCAI 2023

作者： Du, Chunhui Xiao, Jianhang Guo, Wei Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781450399029

Decentralized federated learning (DFL) enables multiple clients to collaboratively train a machine learning model by exchanging parameters with each other while protecting privacy. Clients may be deployed in geo-distributed sites and communicate through varying significantly wide area networks (WANs). After the topology is constructed, the clients iteratively update the local model and exchange it with neighboring clients until the required accuracy is achieved. A more connected topology usually has fewer iterations but may suffer from a longer time per iteration. How to construct the topology with minimum total time by making a trade-off between the number of iterations and the time per iteration is a crucial issue to be studied. In this paper, we evaluate the number of iterations and the time per iteration of arbitrary topology and then propose a heuristic algorithm to construct the best one by evaluating a series of possible topologies. Our extensive experiments have shown that the other common topologies require 1.88× to 4.53× the total time to achieve the same accuracy compared to the topology obtained by our algorithm. © 2023 Copyright held by the owner/author(s).

关键词： Topology

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FederatedMesh: Collaborative Federated Learning for Medical Data Sharing in Mesh Networks 19th

FederatedMesh: Collaborative Federated Learning for Medical...

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19th EAI international conference on Collaborative computing: networking, Applications and Worksharing, CollaborateCom 2023

作者： Shkurti, Lamir Selimi, Mennan Besimi, Adrian Faculty of Contemporary Sciences and Technologies South East European University Tetovo Macedonia Max van der Stoel Institute South East European University Tetovo Macedonia

ISBN: (纸本)9783031545306

Edge computing is a paradigm that involves performing local processing on lightweight devices at the edge of networks to improve response times and reduce bandwidth consumption. While machine learning (ML) models can run on smaller computing devices at the edge, training ML models presents challenges for low-capacity devices. this paper aimed to evaluate the performance of Federated Learning (FL) - a distributed ML framework, when training a medical dataset using Raspberry Pi devices as client nodes. the testing accuracy, CPU usage, RAM memory usage and network performance were measured for different number of clients and epochs. the results showed that increasing the number of devices generally improved the testing accuracy, with the greatest improvement observed in the earlier epochs. However, increasing the number of devices also increased the CPU usage, with a significant increase observed in the later epochs. Additionally, the RAM memory usage increased slightly as the number of clients and epochs increased. the findings suggest that FL can be an effective way to train medical models using distributed devices, but careful consideration must be given to the trade-off between accuracy and computational resources. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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A distributed Architecture for Particle Swarm Optimization Meta-heuristic Methods Based on Multi-agent Systems 9th

A Distributed Architecture for Particle Swarm Optimization M...

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9th international conference on Smart City Applications, SCA 2024

作者： Adbi, Said Mouncif, Hicham Laboratory LIMATI Polydisciplinary Faculty Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (纸本)9783031886522

this research presents a novel parallel model for Particle Swarm Optimization (PSO), named Multi-agent Agent-based PSO (MAPSO). the model synergizes multi-agent system principles with parallel computing techniques to enhance optimization processes. By transforming particles into autonomous intelligent agents capable of asynchronous execution and advanced learning, the swarm operates as a cohesive multi-agent system. this innovative approach seeks to improve the efficiency and performance of conventional PSO algorithms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Multi-User Service throughput Estimation for Adaptive Resource Management in 5G-Advanced and Beyond Sliced Networks 9

Multi-User Service Throughput Estimation for Adaptive Resour...

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9th IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2023

作者： Ravindran, Sharvari Pimpalkar, Yogita Bapat, Jyotsna Das, Debabrata Networking and Communication Research Lab International Institute of Information Technology Electronics City Bangalore560 100 India

ISBN: (纸本)9798350334395

Due to the random arrival of traffic in 5G-Advanced networks, optimum slice resource management has explicitly become challenging with an increase in the number of users. Each network slice is designed to map its specific characteristics, such as service demands and bandwidth to performance metrics, such as throughput and delay. these slice-level metrics are continuously monitored during deployment to check whether the resources have to be reconfigured to meet the service level agreements (SLAs) while minimizing resource over-provisioning. thus, developing accurate slice-level performance models has gained interest for cellular network operators. Most of the prior work has relied on pre-negotiated operator-defined SLAs with a focus on fractional resource allocation. While there has been limited work on slice throughput estimation, the effect of multiple access within a slice due to competitive behaviour among users is a complex open research problem. this paper presents a novel comprehensive analysis tackling User Service throughput Estimation (USTE) within and across multiple slices. USTE works in two stages. In the first stage, the channel distributions of users hosting services are modeled, followed by dynamic conditional throughput estimation in the second stage. For new users requesting a slice, the throughput is re-estimated and re-configured across prior users hosting services with the same slice. the proposed method will facilitate the system to detect resource over-provisioning, slice capacity problems and SLA degradations through network dimensioning and re-dimensioning. Analytical and simulation results show that USTE performs scalable user scheduling yielding 55% throughput gains compared to the state-of-the-art. © 2023 IEEE.

关键词： Resource allocation

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