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

{"title":"Load-Aware Resource Scheduling in Fog Computing Based Delay-Sensitive IoT Networks","authors":"L. R. Lakshmi","doi":"10.1109/SmartNets58706.2023.10216219","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10216219","url":null,"abstract":"Internet of Things (IoT), with its capability to connect anything, anywhere, anytime, bringing in revolutionary changes in data collection, processing and utilization. IoT networks generate a huge amount of data, which is difficult to process and store on IoT devices. Cloud computing with its huge availability of resources, can process and store these huge volumes of data. However, owing to the long distance between the cloud and IoT devices, the cloud computing suffers the issues like high latency, network congestion and unreliability. To address these issues, fog computing, which is a decentralized computing paradigm has been proposed as an extension to cloud computing. To utilize the fog resources efficiently and to avoid over-utilization and under-utilization of resources, this paper proposes a resource scheduling algorithm which is capable of balancing the load on fog nodes while satisfying the delay requirements of IoT applications. Extensive simulations are conducted to evaluate the performance of the proposed method. The simulation results establish that the load is almost equally distributed among the fog nodes in the network in most of the traffic arrival scenarios.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"24 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":"128109442","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}

{"title":"A Web-Based Platform for Real-Time Speech Emotion Recognition using CNN","authors":"Damir Kabdualiyev, Askar Madiyev, Adil Rakhaliyev, Balgynbek Dikhan, Kassymzhan Gizhduan, Hashim Ali","doi":"10.1109/SmartNets58706.2023.10215937","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215937","url":null,"abstract":"This pilot study presents a web-based real-time speech emotion recognition platform using a convolutional neural network algorithm. The study aims to develop a reliable tool for predicting emotions in speech with a user-friendly design to enable easy access and display of recognition results. The platform recognizes seven emotions (angry, disgust, fear, happy, neutral, sad, and surprise) and has two functionalities: static and real-time speech signals analysis. The static analysis allows users to upload pre-recorded audio files for analysis, while the real-time analysis provides continuous audio processing as it is being recorded. The study also focuses on developing a reliable model with minimal features to predict emotions while accurately identifying various emotions detected in speech. The algorithmic performance of the model was evaluated using publicly available datasets (RAVDESS, TESS, and SAVEE). It achieved an accuracy of 86.46% in static analysis using the selected spectral feature: i.e., MFCC. The performance of the real-time analysis was validated through a user study involving 20 participants. It achieved an accuracy of 65% in recognizing emotions in real-time due to possible known factors. An interesting finding was the discrepancy between how individuals perceived their emotions and those detected by the ML model. The accuracy of the ML model was higher in pre-recorded audio recognition and about the same in real-time recognition compared to previous works. The user-friendly design and CNN algorithm make it a promising solution to address challenges in emotion recognition and highlight the importance of further research in this field.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"29 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132193847","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}

{"title":"Generalized framework for protecting privacy in the smart grid environment and measuring the efficacy of privacy attacks *","authors":"Mohammad Sahinur Hossen, Dongwan Shin","doi":"10.1109/SmartNets58706.2023.10216075","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10216075","url":null,"abstract":"One of the most complex cyber-physical infrastructures is the smart grid, which integrates electricity production, transmission, and consumption with customer realms and millions of connected endpoints. This technology generates a large amount of data and has collected and stored highly sensitive personal information. For this reason, protecting the privacy of data collected by smart grids is important, as it often contains personally identifiable information. Because of this, it is important to give consumers a privacy solution that lets them decide how much information they want to share and what might happen if they do. In this paper, we extend data categorization and sensitivity leveling while simultaneously providing each data attribute with a numerical value. We also propose a generalized methodology based on user-chosen data openness for safeguarding privacy in the context of the smart grid and assessing the effectiveness of privacy attacks. In the end, we developed two algorithms to assess the efficacy of privacy attacks and create a table displaying the findings.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"36 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":"115685689","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}

{"title":"The application and comparison of Deep Learning models for the prediction of chest cancer prognosis","authors":"Mahadi Hasan, Miraz Al Mamun, M. Das, Musaab Hasan, Asm Mohaimenul Islam","doi":"10.1109/SmartNets58706.2023.10216201","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10216201","url":null,"abstract":"Lung cancers of all varieties, esophageal cancers, and cancers of the mediastinum (the area between the lungs), pleura (the membrane lining the chest cavity and surrounding the lungs), trachea, thymus gland, and heart are all classified as chest cancers, often known as thoracic cancers. Chest cancer can also spread from cancers that start in other places of the body. Chest pain is one of the usual signs of chest cancer, including hemoptysis or a cough that produces blood. Also, Coughing that hurts or a cough that does not go away is a sign of chest cancer. Mesothelioma, a cancer that begins in the lining of the chest or abdomen, frequently affects the lungs and other thoracic organs and tissues, which has prompted us to continue with this disease so that this research will aid in early detection. Chest X-rays and computed tomography (CT) pictures are the two diagnostic techniques that are most frequently utilized for these disorders. This study suggests a multiclassification deep learning model for detecting chest cancer using a dataset of chest CT-Scan pictures. While a chest CT scan is helpful even before symptoms show up and precisely detects the aberrant features that are found in images, a chest X-ray is less effective in the early stages of the disease.Furthermore, employing these kinds of photos will improve classification precision. To the best of our knowledge, no deep learning model in the literature can choose between these disorders. The current work considers the effectiveness of three architectures— CNN, ResNet50, and DenseNet121—. A thorough assessment of various deep learning architectures is performed using publicly available digital CT datasets with four classifications (Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and Normal). The study’s findings revealed that the DenseNet121 model performs better than the three other suggested models. CNN demonstrated 56.19% accuracy, whereas ResNet50 demonstrated 56.51% accuracy. The DenseNet121 model demonstrated 71.74% accuracy (ACC). We intend to investigate further deep learning models with large datasets.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"13 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":"116759472","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}

{"title":"On the Implementation of a Secure and Energetically Efficient NOC for Mobile Networks","authors":"Luis A. Mata, Sinan Wannous, D. Duarte, Eva Maia, Pedro Vieira, Isabel Praça","doi":"10.1109/SmartNets58706.2023.10215763","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215763","url":null,"abstract":"Over the past decade, the technological evolution of mobile networks has contributed to the global success and democratization of internet connectivity, notably relying on 4G and 5G deployments. Digital services have grown exponentially, resulting in high traffic volumes and continuous requirements to expand the network footprint. The problem is that, despite the investment in network expansion and upgrade, the revenues of the major Mobile Network Operators (MNOs) have presented anaemic growth, whilst other factor costs have risen, notably the energy prices. This challenging outlook raises concerns over the sector’s long-term sustainability and calls MNOs to adopt smart operative strategies in network management, aiming at ensuring sustainable energy consumption levels. This new paradigm essentially leverages artificial intelligence capabilities to evolve the current reactive approach of Network Operations Centres (NOCs) towards proactive and preventive models relying on network data. In particular, energy consumption data can be used to detect abnormal network behaviours, either caused by unintentional disruptions or by a cyber/physical attack. This paper contributes with a novel multi-domain NOC that combines performance, efficiency and security as an integral part of network optimization. Additionally, as a concrete use case example using live data from a 4G mobile network, a new methodology is proposed to optimize the trade-off between spectral and energy efficiency. The preliminary results show that up to 13% of improvement in energy consumption could be achieved using the proposed methodology to detect the worse performing sites and their root cause factors.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"46 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":"123723307","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}

{"title":"Intelligent Mission Critical Services over Beyond 5G Networks: Control Loop and Proactive Overload Detection","authors":"S. Spantideas, A. Giannopoulos, Marta Amor Cambeiro, Ó. Trullols-Cruces, E. Atxutegi, P. Trakadas","doi":"10.1109/SmartNets58706.2023.10216134","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10216134","url":null,"abstract":"In the new 5G and beyond (B5G) connectivity era, Mission Critical Services (MCS) are expected to leverage secure and reliable communication to the end-users. However, diverse network conditions, along with emergency collision events (e.g., abrupt depletion of network or service resources) necessitate a flexible deployment of the MCS, coupled with an efficient management of the associated resources. This work presents a technical solution of a proactive MCS overload detection architecture and methodology, based on the intelligence loop between the MCS and typical 5G core network components. In this context, the monitoring metrics provided by the MCS server are used by the telemetry module for real-time inference using the potency of a pre-trained Machine Learning (ML) model, targeting at forecasting service requirements and providing overload alarms. The automated scalability functionalities of the proposed solution are demonstrated considering a resource overload prediction scenario, so as to intelligently provide notifications about the upcoming needs for resource scaling. To ensure continuous MCS availability in the presence of collision events, the corrective actions by the network Orchestrator include the MCS service scaling by deploying additional pods and providing load balancing capabilities. The regulation of the Deep Neural Network (DNN) hyperparameters and performance comparison against baseline schemes are quantitatively outlined. Conclusively, results provided evidence related to the ML-drivel intelligence loop embracing a successful monitoring of MCS, thereby boosting the reliability and self-configuration in critical conditions.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"2 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":"124178934","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}

{"title":"Attack Detection in IoT-Based Healthcare Networks Using Hybrid Federated Learning","authors":"M. Itani, Hanaa S. Basheer, Fouad Eddine","doi":"10.1109/SmartNets58706.2023.10216144","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10216144","url":null,"abstract":"Cybercrimes are increasing rapidly throughout the world, leading to financial losses and compromising the integrity and confidentiality of private data. Statistics showed that cybercrimes led to losses of around $6 trillion in 2021 based on a survey by Cybersecurity Ventures. Knowing that IoT networks are considered a source of identifiable data for vicious attackers to carry out criminal actions using automated processes, machine learning (ML)-assisted methods for IoT security have gained much attention in recent years. While conventional ML relies on a single server to store all of its data, which makes it a less desirable option for domains concerned about user privacy, the Federated Learning (FL)-based anomaly detection technique, which utilizes decentralized on-device data to identify IoT network intrusions, represents the proposed solution to the aforementioned problem. We propose a framework to train and test IoT data from health network using different classical machine learning algorithms and an enhanced federated learning model. FL is a framework that learns continuously in an iterative manner by training locally at the client side with the clientś individual data, and then updating the central server by forwarding the required data. We evaluated the performance of different algorithms based on accuracy, precision, recall and F1-score via different iterations. To develop a strong detection system, we used multiple datasets and generated different results. These results show decent and promising accuracy hence a promising solution towards telehealth application using machine learning techniques in detecting threats on IoT networks.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"19 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":"131355934","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}

{"title":"Blockchain for Ridesharing: A Systematic Literature Review","authors":"Khadija Rammeh Houerbi, Dorra Machfar, Hella Kaffel Ben Ayed","doi":"10.1109/SmartNets58706.2023.10215951","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215951","url":null,"abstract":"The use of blockchain technologies allows to shift ridesharing services from centralized to decentralized systems. In order to emphasize the blockchain's role in ridesharing and ride-hailing services and how it could boost the efficiency of these systems, we conduct a systematic literature review using the PRISMA method. For each selected paper, we discussed its key contribution and the main issue/challenge solved by blockchain. We then classified papers according to the type of blockchain used and implementation details. According to our sample of publications, most proposed solutions have been limited to using blockchain as a traceable, auditable, and tamper-proof ledger for completed rides and hadn’t implemented the whole process of ridesharing.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"87 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":"130155816","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}

{"title":"Fading Channel Classification with Walsh-Hadamard Transform and Convolutional Neural Network","authors":"G. Baldini, Fausto Bonavitacola, J. Chareau","doi":"10.1109/SmartNets58706.2023.10215941","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215941","url":null,"abstract":"Fading channel classification is a useful function in the design of wireless communications because the knowledge of the channel state information can help the subsequent steps in the wireless communication processing including the information symbols extraction from the received signal. This paper proposes the application of the Walsh-Hadamard Transform (WHT) in combination with Convolutional Neural Network (CNN) for the problem of fading channel classification. WHT belongs to the generalized class of Fourier transforms and it is a non-sinusoidal, orthogonal transformation technique that decomposes a signal into a set of Walsh functions. WHT has been used in image processing but less in the wireless communication domain. CNN has been recently used in many wireless communications problems including fading channel classification, where it has shown to outperform ’shallow’ machine learning algorithms. This paper presents the novel combination of WHT with CNN for the problem of channel classification. The approach is applied to a data set of chirp signals derived from the technical specification of the radar altimeter, which is submitted to different fading conditions in a channel emulator implemented with FPGA in a radio frequency laboratory. The results show that the proposed approach is able to significantly outperform (especially in presence of noise) the application of CNN on the original time-based representation of the signal or the spectral domain representation based on the use of the Fourier transform and Wavelet transform.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"35 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":"128828714","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}

{"title":"Data Offloading from LTE to Wi-Fi","authors":"Mustafa Aljerbi, A. Ashur, N. Naas","doi":"10.1109/SmartNets58706.2023.10215972","DOIUrl":"https://doi.org/10.1109/SmartNets58706.2023.10215972","url":null,"abstract":"One of the alternate ways of solving the problems of mobile network congestion in a congested urban area such as at Tripoli University (Tripoli City –Libya) is the Wi-Fi offloading technique. This paper shows how Wi-Fi offloading will improve the efficiency of an entire mobile network using the Atoll simulation tool presenting options for the technical integration of Wi-Fi and cellular network. Moreover, it shows the challenges and future works of Wi-Fi offloading at Tripoli University. The different coverage, signal quality, capacity predictions, and simulations were carried out at Tripoli University. The simulation results showed that, after offloading a percentage of 4G data traffic to a Wi-Fi network, has improved the services of both voice and data. One of our main aims is to recommend that mobile operators take alternative ways to solve the current cellular congestions, many different studies demonstrated that Wi-Fi offloading can improve the signal quality and the network capacity by carrying part of data to Wi-Fi beams pointed toward users.","PeriodicalId":301834,"journal":{"name":"2023 International Conference on Smart Applications, Communications and Networking (SmartNets)","volume":"25 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":"121665334","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}