2023 International Conference on Smart Applications, Communications and Networking (SmartNets)最新文献_第5页

Optimizing Latency for Real-time Traffic and Road Safety Applications through MEC-based V2X System 基于mec的V2X系统优化实时交通和道路安全应用的延迟

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215515

Annu, Rajalakshmi Pachamuthu, Praveen Tammana

引用次数: 0

ML-Based Service Type Priority Decision Method Using Ambient Information for 5GB 基于ml的5GB环境信息的业务类型优先级决策方法

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215712

Umut Altunan, Halenur Sazak, Ahmet Yazar

引用次数: 0

Distributed Personal OS Environments – exploring Cooperative Fog Computing 分布式个人操作系统环境-探索协作雾计算

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10216015

M. Boussard, P. Peloso, Vincent Verdot, R. Douville, N. L. Sauze

引用次数: 0

Resource-Constrained Device Characterization for Detecting Sleep Apnea Using Machine Learning 使用机器学习检测睡眠呼吸暂停的资源受限设备表征

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10216211

Sayani Mallick, Pranav Ruparel, Shubhangi K. Gawali, Neena Goveas

{"title":"Resource-Constrained Device Characterization for Detecting Sleep Apnea Using Machine Learning","authors":"Sayani Mallick, Pranav Ruparel, Shubhangi K. Gawali, Neena Goveas","doi":"10.1109/SmartNets58706.2023.10216211","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10216211","url":null,"abstract":"Automation of measurement and analysis of continuous human body parameters like ECG is a first step towards establishing accessible and distributed medical infrastructure. Currently, the cost of medical devices and use of expertise for analysis puts this out of reach of many patients. Unless their condition becomes life-threatening most patients will avoid going through this process, losing out on the benefits of early detection and treatment of their illness. In this work, we propose the use of cost-effective devices for making a complete self-contained pipeline which includes measurement of ECG signals, cleaning and pre-processing of signals and use of machine learning techniques to analyse them on the device. We have used as a case study, detection of Sleep Apnea using ECG signals. We compare resource-constrained hardware with varying price and capability ranges to study their effectiveness in detecting Sleep Apnea. We propose the use of an artificial neural network model developed using TensorFlow Lite on resource-constrained devices for detection of Sleep Apnea. We report that the results from resource-constrained devices are comparable to more advanced and expensive devices for detection of Sleep Apnea using ECG signals.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133879662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Indoor Navigation Assistance System for Visually Impaired with Semantic Segmentation using EdgeTPU 基于EdgeTPU语义分割的视障室内导航辅助系统

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215643

Victor Tran, K. Sood, Kayhan Bakian, Aneesh Reddy Sannapu

{"title":"Indoor Navigation Assistance System for Visually Impaired with Semantic Segmentation using EdgeTPU","authors":"Victor Tran, K. Sood, Kayhan Bakian, Aneesh Reddy Sannapu","doi":"10.1109/SmartNets58706.2023.10215643","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215643","url":null,"abstract":"Recognizing the limited supply and high cost of alternative solutions for visual assistance, such as guides, it is critical to create an affordable and accessible means of recognizing walkable paths for visually impaired individuals. In this paper, we assess deep learning and traditional machine learning models for image segmentation and object detection to identify walkable, indoor paths in real-time, given low-resolution images from a camera. Specifically, we leverage the processing capabilities of Google’s EdgeTPU chip, which accelerates inferences of light-weight TensorFlow models deployed in embedded devices. We retrain the MobileNet computer vision model on the ADE MIT Scene Parsing Benchmark Dataset, and improve the performance accuracy by consolidating 150 categories into just two categories. The segmentation model is post-quantize-aware trained and co-compiled with a light-weight object detection model. The resulting model is capable of simultaneous semantic segmentation and object detection with inference times of 65 milliseconds. Our approach lays the foundation for transforming the level of assistance for the visually impaired to sense the world through a wearable device assistance system for indoor navigation.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132179711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

'AI-Teacher' Assistant System: A Smart Attendance and Participation tracking system for students “人工智能教师”助理系统:学生智能出勤和参与跟踪系统

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215586

Mouza Alzaabi, Mahra Almeheiri, Shuaa Alqubaisi, Ahmed Shuhaiber

{"title":"'AI-Teacher' Assistant System: A Smart Attendance and Participation tracking system for students","authors":"Mouza Alzaabi, Mahra Almeheiri, Shuaa Alqubaisi, Ahmed Shuhaiber","doi":"10.1109/SmartNets58706.2023.10215586","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215586","url":null,"abstract":"Students and teachers are connected in an orderly way to attain learning outcomes, which is the foundation of attendance systems. Academic institutions, however, encounter poor strategies for these parties' participation. In order to address the low attendance and participation among students and faculty in a university community, a university-wide ‘AI-Teacher’ system will be built in one of the educational institutions in the Gulf region. The system was prepared for by planning, gathering, modelling, and analyzing the requirements, designing the interfaces and databases, and then coding and testing. The AI-Teacher assistant system is approachable and simple to use. A warning is sent to parents after a student misses a predetermined number of classes, along with an automatic or manual attendance record, alerts, and notifications. Also, the application system, ‘AI-Teacher’ which works with iOS and Android devices, can improve connections between \"students and instructor\". The study concluded with some recommendations for further research and development.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"15 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132285372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-Aware Task Scheduling for Digital Twin Edge Networks in 6G 6G数字双边缘网络的能量感知任务调度

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215892

Elif Bozkaya, T. Bilen, Müge Erel-Özçevı̇k, Yusuf Özçevik

{"title":"Energy-Aware Task Scheduling for Digital Twin Edge Networks in 6G","authors":"Elif Bozkaya, T. Bilen, Müge Erel-Özçevı̇k, Yusuf Özçevik","doi":"10.1109/SmartNets58706.2023.10215892","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215892","url":null,"abstract":"With the recent surge in the Internet of Things (IoT) devices and applications, computation offloading services in Mobile Edge Computing (MEC) have provided the significant potential to upcoming 6G networks for a better Quality of Service (QoS). However, IoT devices are typically resource and energy-constrained, so this challenge can be compensated by incorporating energy-efficient approaches into the solution. Digital Twin is a candidate technology to reshape the future of the industry and energy-efficiently manage tremendous growth in data traffic at the network edge. Thus, we propose a Digital Twin Edge Network (DTEN) architecture for energy-aware task scheduling. More specifically, we formulate an energy optimization problem and identify a set of computation strategies to minimize both the task processing time and energy consumption. Due to being NP-hard, we compare it by Warehouse Location Problem (WLP) and solve it with the genetic algorithm-based approach in an energy and time-efficient manner. To achieve these, we present our digital twin-assisted energy-aware task scheduling algorithm by using both real-time and historical data in virtualization and service layers. After this, IoT devices can compute their tasks locally or offload to the edge/cloud server with the assistance of digital twins of the physical assets. Simulations are carried out to show the superiority of the proposed energy-aware task scheduling algorithm in terms of the task processing time and consumed energy in DTEN.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132849199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Portable AI-powered spice recognition system using an eNose based on metal oxide gas sensors 便携式人工智能香料识别系统，使用基于金属氧化物气体传感器的eNose

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215915

Montaser N. A. Ramadan, M. Alkhedher, B. T. Akgün, Sina Alp

{"title":"Portable AI-powered spice recognition system using an eNose based on metal oxide gas sensors","authors":"Montaser N. A. Ramadan, M. Alkhedher, B. T. Akgün, Sina Alp","doi":"10.1109/SmartNets58706.2023.10215915","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215915","url":null,"abstract":"In our daily lives, we use spices and herbs. There are thousands of different sorts of spices that surround words. And occasionally it’s difficult to distinguish between them. Furthermore, without specialized knowledge it is impossible to determine whether they are fresh or not. A challenging algorithm and highly sensitive sensors are needed to predict the labels and freshness of spices and herbs based primarily on their smell. In this paper, we present AI-powered spice recognition system (AISRS), which is made up of an array of 8 inexpensive BME688 digital tiny sensors are exploited to classify four different types of herbs and spices: clove, cinnamon, anise, and chamomile. The proposed eNose measures temperature, humidity, pressure, and gas concentrations for various types of spices and condiments. For every sort of class, we keep track of more than 10,000 readings. Through the use of assessment indexes at each level, we were able to determine whether or not algorithms such as k-NN, Random Forest, SVM, MLP, DT, and AdaBoost were successful. The Random Forest instantaneous classification algorithm performed the best among others where the success rate for predicting and differentiating between the four classes was better than 97 percent according to the validation data. These validation findings plus the eNose’s low power consumption (0.05 W) make it possible for it to be improved and used in portable and battery-operated applications in the future.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132476114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Privacy based Face Anonymization for Smart Cities 基于深度隐私的智能城市人脸匿名化

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215996

Muhammad Umair Hassan, Magnus Stava, I. Hameed

引用次数: 0

Machine Learning with Bitcoin Heist Ransomware 机器学习与比特币抢劫勒索软件

2023 International Conference on Smart Applications, Communications and Networking (SmartNets) Pub Date : 2023-07-25 DOI: 10.1109/SmartNets58706.2023.10215732

Nurhaliza Hassan, K. Sood, Gabriel Suzuki

引用次数: 0