2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)最新文献_第2页

Sensing on The Edge: Smartening up Sensors 边缘传感:智能传感器

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/fmec57183.2022.10062641

E. Kanjo

{"title":"Sensing on The Edge: Smartening up Sensors","authors":"E. Kanjo","doi":"10.1109/fmec57183.2022.10062641","DOIUrl":"https://doi.org/10.1109/fmec57183.2022.10062641","url":null,"abstract":"Between the sensors gathering data and the cloud computing services processing data, there is an array of new technologies emerging. These technologies allow for complex processing and storage, closer to where data is collected – right at the edge. While Cloud Computing has received tremendous attention from both academia and industry for connecting many devices to the internet, the ever-growing number of IoT and mobile devices has created the demand for lower network latency and processing capability closer to the users. Edge computing provides an opportunity for wearable devices to access more resources without violating the constraints on weight, size, and sensing capabilities. It involves placing computing resources closer to where data originates (i.e. heart rate monitor, pumps, step counter, or other sensors) – or at the “edge.” Furthermore, edge computing provides many required on-device processing capabilities, which can then help in protecting users' private data as raw personal data (such as images and videos) don't need to be shared remotely. These computing resources may be located in the devices themselves or in hyper-local, small-scale data centres. This talk will look at the potential of edge computing for multimodal sensing while protecting users' privacy and will showcase several examples from recent work at the Smart Sensing Lab in this area.","PeriodicalId":129184,"journal":{"name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130300794","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: 1

Integrating Cognitive Radio in NOMA-based B5G Networks: Architecture and Research Challenges 在基于noma的B5G网络中集成认知无线电:架构和研究挑战

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/FMEC57183.2022.10062748

Sharief Abdel-Razeq, H. Salameh, H. Al-Obiedollah

引用次数: 0

FLIoDT: A Federated Learning Architecture from Privacy by Design to Privacy by Default over IoT FLIoDT:从设计隐私到物联网默认隐私的联邦学习架构

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/FMEC57183.2022.10062661

Feras M. Awaysheh, Sadi Alawadi, Sawsan Al-Zubi

{"title":"FLIoDT: A Federated Learning Architecture from Privacy by Design to Privacy by Default over IoT","authors":"Feras M. Awaysheh, Sadi Alawadi, Sawsan Al-Zubi","doi":"10.1109/FMEC57183.2022.10062661","DOIUrl":"https://doi.org/10.1109/FMEC57183.2022.10062661","url":null,"abstract":"The Internet of Things (IoT) realized exponential growth of smart devices with decent capabilities, promising an era of Edge Intelligence. This paradigm creates a timely need to shift many computations closer to the data source at the network's edge. Data privacy is paramount, as security breaches can severely impact such an environment with its vast attack surface. The advent of Federated learning (FL), a privacy-by-design with decentralized machine learning (ML), enables participants to collaboratively train a model without sharing their sensitive data. Nevertheless, privacy implications are a glaring concern and perrier for widening the utilization of FL approaches and their mass adoption over IoT applications. This paper introduces the notion of FL over the Internet of Disconnected Things (FLIoDT), a functionality separation of concerns following the air-gapped networks. FLIoDT provides a practical methodology to mitigate Data threats/attacks in the FL domain. FLIoDT proves a practical architectural approach to mitigate several attacks in an Edge environment. Data dredging and adversarial attacks, like data poisoning, to name some. This study investigates human activity recognition of health monitoring patient data over edge computing to validate FLIoDT.","PeriodicalId":129184,"journal":{"name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130157582","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: 2

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) 2022第七届雾与移动边缘计算国际会议(FMEC)

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/fmec57183.2022.10062821

引用次数: 0

Quantum Vs Classical Computing: a Comparative Analysis 量子与经典计算:比较分析

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/FMEC57183.2022.10062753

Maria Belkhir, Haroun Benkaouha, E. Benkhelifa

引用次数: 0

Performance Comparison of Big Data Processing Utilizing SciDB and Apache Accumulo Databases 基于SciDB和Apache Accumulo数据库的大数据处理性能比较

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/FMEC57183.2022.10062513

Mohammad Abu Mhana, Alá F. Khalifeh, S. Alouneh

{"title":"Performance Comparison of Big Data Processing Utilizing SciDB and Apache Accumulo Databases","authors":"Mohammad Abu Mhana, Alá F. Khalifeh, S. Alouneh","doi":"10.1109/FMEC57183.2022.10062513","DOIUrl":"https://doi.org/10.1109/FMEC57183.2022.10062513","url":null,"abstract":"Big data deals with processing massive, complex data sets and data volumes that incorporate a tremendous amount of information. Therefore, researchers created several methods, models, and databases to deal with such big data, among them is the Apache Accumulo database, which is considered an in-storage database reliant on the Hadoop processing framework to give the ability to analyze and process the data. Another big data database that is widely used in the research community is SciDB which stands for the scientific database. SciDB utilizes a PostgreSQL connection, to establish a reliable link with the database. In this paper, we will analyze and evaluate the performance of these two database systems that are specialized in handling big data and storing them for further processing and analysis. The databases' performance will be analyzed in terms of several metrics such as CPU utilization, data storing/retrieval delay, disk utilization, and the number of data ingestions per second. Furthermore, the setup and integration of the two databases are investigated. Our performance evaluation revealed the advantages and disadvantages of each database structure. Where it has been found that Apache Accumulo DB has the best performance compared with SciDB in terms of average ingestion execution time, the number of ingestions per second, and CPU utilization. Whereas, SciDB has the lowest disk utilization compared to Apache Accumulo.","PeriodicalId":129184,"journal":{"name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114216512","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

Resource Allocation and Scheduling of Real-Time Workflow Applications in an IoT-Fog-Cloud Environment 物联网-雾云环境下实时工作流应用的资源分配与调度

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/FMEC57183.2022.10062849

Georgios L. Stavrinides, H. Karatza

{"title":"Resource Allocation and Scheduling of Real-Time Workflow Applications in an IoT-Fog-Cloud Environment","authors":"Georgios L. Stavrinides, H. Karatza","doi":"10.1109/FMEC57183.2022.10062849","DOIUrl":"https://doi.org/10.1109/FMEC57183.2022.10062849","url":null,"abstract":"The explosive growth of the Internet of Things (IoT) has led to the emergence of the IoT-fog-cloud continuum, in an attempt to facilitate the real-time processing of IoT data. In such multi-tier environments, it is crucial to adopt an efficient resource allocation and scheduling scheme, in order to provide effective load balancing and timeliness for the real-time workload. A load balancing approach that has been proven to be efficient and effective in traditional distributed environments, is the power of two choices – or $d$ choices, in its general form. Only recently has this technique been examined in multi-tier environments, without considering, however, important aspects of such frameworks. To this end, in this paper we propose and investigate three resource allocation and scheduling heuristics for real-time workflow jobs in an IoT-fog-cloud environment. The first strategy, performs exhaustive search at each scheduling step in order to find the most suitable resource in the fog and cloud layers for the workload assignment. On the other hand, the two other policies adopt the power of two choices approach. The simulation results shed light on interesting insights regarding the performance and applicability of each method.","PeriodicalId":129184,"journal":{"name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124240589","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: 1

EdgePub: A Self-Adaptable Distributed MQTT Broker Overlay for the Far-Edge EdgePub:用于远边缘的自适应分布式MQTT代理覆盖

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/FMEC57183.2022.10062858

Chamseddine Bouallegue, Julien Gascon-Samson

{"title":"EdgePub: A Self-Adaptable Distributed MQTT Broker Overlay for the Far-Edge","authors":"Chamseddine Bouallegue, Julien Gascon-Samson","doi":"10.1109/FMEC57183.2022.10062858","DOIUrl":"https://doi.org/10.1109/FMEC57183.2022.10062858","url":null,"abstract":"The MQTT protocol, based on a topic-based publish/subscribe paradigm, plays an important role in the Internet of Things (IoT), as it enables flexible and highly decoupled communications between the different entities of an IoT system. Further, several IoT applications require low latencies (e.g., tele-surgery, connected vehicles) – hence, a centralized MQTT (publish/subscribe) infrastructure can be impractical. In this paper, we present EdgePub, a dynamic, highly distributed, and self-adaptable edge-based publish/subscribe middleware that provides drop-in compatibility with existing MQTT-based client applications and brokers. EdgePub transparently builds a one-hop dissemination overlay over embedded MQTT brokers deployed at the far-edge (i.e., on the client devices themselves), and provides a load balancing strategy that continuously minimizes the average publication latency, while ensuring that the bandwidth constraints of the edge client devices are met. We provide an implementation through the form of an MQTT.js-compatible Node.JS library, and we evaluate EdgePub over different deployment scenarios (i.e., local to world-wide deployments), over a test-bed of Raspberry Pi devices. We report 18%-77% lower average latencies compared to centralized edge and cloud-based deployments, without exceeding the limited bandwidth constraints of the edge brokers.","PeriodicalId":129184,"journal":{"name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129037664","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

Using Siamese Neural Networks for Efficient and Accurate IoT Device Identification 使用连体神经网络实现高效准确的物联网设备识别

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/FMEC57183.2022.10062771

F. Trad, Ali Hussein, A. Chehab

{"title":"Using Siamese Neural Networks for Efficient and Accurate IoT Device Identification","authors":"F. Trad, Ali Hussein, A. Chehab","doi":"10.1109/FMEC57183.2022.10062771","DOIUrl":"https://doi.org/10.1109/FMEC57183.2022.10062771","url":null,"abstract":"With the wide adoption of Internet of Things (IoT) devices, it becomes crucial to identify which IoT devices are connected to the network at a specific time. Previous studies have built machine learning models that can accurately identify IoT devices on a specific network based on their traffic characteristics. However, one limitation of such models is that whenever a new device joins the network, the model has to be retrained from scratch, which adds a lot of computation overhead. In this work, we propose the use of Siamese Neural Networks to reduce the retraining frequency of IoT device identification models. We use a public dataset containing traffic features from 10 devices. To validate the proposed idea, we first compare the performance of classical multi-class classification neural networks with Siamese Networks on the task at hand. We see that both networks perform similarly. Then, we build 10 separate models based on Siamese networks, and we train each of them to recognize a different combination of 9 devices. Then, we use each of the trained models to recognize the device that was not part of the training set. We assess the performance of each model, and we compare the results with the ones achieved by the multi-class classification network. We prove that with the proposed approach, similar or even better outcomes are achieved, with the main advantage of not having to retrain. Finally, we test the proposed approach against 2 other datasets: Aalto and UNSW. We compare the outcomes with previous works, and we prove that Siamese Networks achieve a better performance.","PeriodicalId":129184,"journal":{"name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130173600","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: 1

Evaluating Amazon EC2 Spot Price Prediction Models Using Regression Error Characteristic Curve 用回归误差特征曲线评价Amazon EC2现货价格预测模型

2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC) Pub Date : 2022-12-12 DOI: 10.1109/FMEC57183.2022.10062720

Batool Alkaddah, A. Agarwal

{"title":"Evaluating Amazon EC2 Spot Price Prediction Models Using Regression Error Characteristic Curve","authors":"Batool Alkaddah, A. Agarwal","doi":"10.1109/FMEC57183.2022.10062720","DOIUrl":"https://doi.org/10.1109/FMEC57183.2022.10062720","url":null,"abstract":"Amazon EC2 offers inactive virtual machines (VM) as spot instances at up to 90% discount. In return, the least expensive option requires the customers' usage to be tolerated with a low availability level agreement. Thus, many studies proposed forecasting and prediction mechanisms to asses in finding the best set of maximum prices. In this paper, we study the model's efficiency in predicting spot EC2 prices with focusing on assessing the performance of forecasting algorithms: RFR, XGBoost, k-NNR, and SVR. Model's evaluation is crucial for measuring the accuracy of predicted prices, thus, we select six metrics for evaluating the forecasting results. We used the top implemented metrics in the related work: MAPE, RMSE, MAE, and MSE. In addition, we assessed the spotted models using the Regression Error Characteristics (REC) curve and the Area under the curve (AUC-REC) in comparison to prior measures. Three aspects are considered while building the models: dataset time per year, training window as 1-day or 1-month ahead and instance location. The trained model applies the cross-validation technique to learn the ideal hyper-parameters that achieve the highest accuracy. However, except for the SVR model, our findings indicate it is unnecessary to use this technique to improve the algorithms' accuracy. Our results investigations display the REC curve and AUC-REC as a superior performance measurements for evaluating models over different accuracy-loss thresholds.","PeriodicalId":129184,"journal":{"name":"2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)","volume":"24 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120898701","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