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نتیجه جستجو - Fog computing

تعداد مقالات یافته شده: 26
ردیف عنوان نوع
1 Challenges and recommended technologies for the industrial internet of things: A comprehensive review
چالش ها و فن آوری های پیشنهادی برای اینترنت اشیا صنعتی: مرور جامع-2020
Physical world integration with cyber world opens the opportunity of creating smart environments; this new paradigm is called the Internet of Things (IoT). Communication between humans and objects has been extended into those between objects and objects. Industrial IoT (IIoT) takes benefits of IoT communications in business applications focusing in interoperability between machines (i.e., IIoT is a subset from the IoT). Number of daily life things and objects connected to the Internet has been in increasing fashion, which makes the IoT be the dynamic network of networks. Challenges such as heterogeneity, dynamicity, velocity, and volume of data, make IoT services produce inconsistent, inaccurate, incomplete, and incorrect results, which are critical for many applications especially in IIoT (e.g., health-care, smart transportation, wearable, finance, industry, etc.). Discovering, searching, and sharing data and resources reveal 40% of IoT benefits to cover almost industrial applications. Enabling real-time data analysis, knowledge extraction, and search techniques based on Information Communication Technologies (ICT), such as data fusion, machine learning, big data, cloud computing, blockchain, etc., can reduce and control IoT and leverage its value. This research presents a comprehensive review to study state-of-the-art challenges and recommended technologies for enabling data analysis and search in the future IoT presenting a framework for ICT integration in IoT layers. This paper surveys current IoT search engines (IoTSEs) and presents two case studies to reflect promising enhancements on intelligence and smartness of IoT applications due to ICT integration.
Keywords: Industrial IoT (IIoT) | Searching and indexing | Blockchain | Big data | Data fusion Machine learning | Cloud and fog computing
مقاله انگلیسی
2 STEP-ONE: Simulated testbed for Edge-Fog processes based on the Opportunistic Network Environment simulator
STEP-ONE: بستر آزمون شبیه سازی شده برای فرایندهای Edge-Fog بر اساس شبیه ساز Opportunistic Network Environment-2020
The Internet of Things (IoT) has evolved from a cloud-based architecture towards multi-layer architectures such as Fog computing, where network edge devices perform processing and messaging tasks. Researchers studied managing and scheduling applications in these architectures extensively, however some issues, especially the effect of mobility, have been less explored. Additionally, evaluation of these mechanisms in realistic scenarios is difficult, as real-world experiments are costly and building composite applications with existing simulation tools is laborious. We call such composite applications which entail functions for handling connectivity and mobility disruptions in the edge network, Edge Processes. In this paper, we present STEP-ONE - a set of tools developed to simulate IoT systems where the management and application composition is handled with Business Process Model and Notation (BPMN) related standards. We demonstrate STEP-ONE with a smart-city scenario, where an application modeled as a choreography of processes is executed between different resources - cloud, fog and moving edge devices. We show how STEP-ONE enables constructing such a scenario and how it can be extended with algorithms and decision-mechanisms to adaptively drive the execution of such processes. We also present how detailed performance metrics can be extracted from the discrete event simulations run with STEP-ONE.
Keywords: Internet of Things testbed | Discrete event simulation | BPMN 2.0 | Fog computing | Edge process management | Smart city
مقاله انگلیسی
3 Design and application of fog computing and Internet of Things service platform for smart city
طراحی و استفاده از سیستم عامل محاسبات مه و اینترنت اشیا برای شهر هوشمند-2020
Fog computing and Internet of Things technology play a prominent role in the construction of smart cities, which can greatly promote the exchange and management of urban information. Emerging network technologies such as fog computing and the Internet of Things can be used to make it easier to build smart cities, which is conducive to the development of urban business, industry and other industries, as well as tourism and transportation management. Therefore, the realization of a smart city will greatly enhance the comprehensive development strength of the city. We analyze the advantages of fog computing and propose an IoT architecture based on fog computing, which effectively solves the problems of big data processing and network scalability. On this basis, a layered fog computing network architecture is proposed to make the city’s operation more coordinated, efficient and harmonious through various intelligent perceptions, information processing and network transmission means.© 2020 Elsevier B.V. All rights reserved.
Keywords: Cloud computing | Fog computing | Smart city | Internet of Things
مقاله انگلیسی
4 Digital evidence in fog computing systems
شواهد دیجیتال در سیستم های محاسباتی مه-2020
Fog Computing provides a myriad of potential societal benefits: personalized healthcare, smart cities, automated vehicles, Industry 4.0, to name just a few. The highly dynamic and complex nature of Fog Computing with its low latency communication networks connecting sensors, devices and actuators facilitates ambient computing at scales previously unimaginable. The combination of Machine Learning, Data Mining, and the Internet of Things, sup- ports endless innovation in our data driven society. Fog computing incurs new threats to security and privacy since these become more difficult when there are an increased number of connected devices, and such devices (for example sensors) typically have limited capacity for in-built security. For law enforcement agencies, the existing models for digital forensic investigations are ill suited to the emerging fog paradigm. In this paper we examine the procedural, technical, legal, and geopolitical challenges associated with digital forensic investigations in Fog Computing. We highlight areas that require further development, and posit a framework to stimulate further consideration and discussion around the challenges associated with extracting digital evidence from Fog Computing systems.© 2021 R. Hegarty and M. Taylor. Published by Elsevier Ltd. All rights reserved.
Keywords: Digital evidence | Fog computing | Cyber crime
مقاله انگلیسی
5 Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach
صرفه جویی در وقت و هزینه در برنامه ریزی کاربردهای اینترنت اشیا-مبتنی بر مه با استفاده از روش یادگیری تقویتی عمیق-2020
Due to the rapid growth of intelligent devices and the Internet of Things (IoT) applications in recent years, the volume of data that is generated by these devices is increasing ceaselessly. Hence, moving all of these data to cloud datacenters would be impossible and would lead to more bandwidth usage, latency, cost, and energy consumption. In such cases, the fog layer would be the best place for data processing. In the fog layer, the computing equipment dedicates parts of its limited resources to process the IoT application tasks. Therefore, efficient utilization of computing resources is of great importance and requires an optimal and intelligent strategy for task scheduling. In this paper, we have focused on the task scheduling of fog-based IoT applications with the aim of minimizing long-term service delay and computation cost under the resource and deadline constraints. To address this problem, we have used the reinforcement learning approach and have proposed a Double Deep Q-Learning (DDQL)-based scheduling algorithm using the target network and experience replay techniques. The evaluation results reveal that our proposed algorithm outperforms some baseline algorithms in terms of service delay, computation cost, energy consumption and task accomplishment and also handles the Single Point of Failure (SPoF) and load balancing challenges.
Keywords: Fog computing | Task scheduling | Deep reinforcement learning | Double Q-Learning | Service delay | Computation cost
مقاله انگلیسی
6 Multi-objective scheduling of extreme data scientific workflows in Fog
زمانبندی چند هدفه گردش کار علمی علمی شدید در مه-2020
The concept of ‘‘extreme data’’ is a recent re-incarnation of the ‘‘big data’’ problem, which is distinguished by the massive amounts of information that must be analyzed with strict time requirements. In the past decade, the Cloud data centers have been envisioned as the essential computing architectures for enabling extreme data workflows. However, the Cloud data centers are often geographically distributed. Such geographical distribution increases offloading latency, making it unsuitable for processing of workflows with strict latency requirements, as the data transfer times could be very high. Fog computing emerged as a promising solution to this issue, as it allows partial workflow processing in lower-network layers. Performing data processing on the Fog significantly reduces data transfer latency, allowing to meet the workflows’ strict latency requirements. However, the Fog layer is highly heterogeneous and loosely connected, which affects reliability and response time of task offloading. In this work, we investigate the potential of Fog for scheduling of extreme data workflows with strict response time requirements. Moreover, we propose a novel Pareto-based approach for task offloading in Fog, called Multi-objective Workflow Offloading (MOWO). MOWO considers three optimization objectives, namely response time, reliability, and financial cost. We evaluate MOWO workflow scheduler on a set of real-world biomedical, meteorological and astronomy workflows representing examples of extreme data application with strict latency requirements.
Keywords: Scheduling | Scientific workflows | Fog computing | Task offloading | Monte-Carlo simulation | Multi-objective optimization
مقاله انگلیسی
7 HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments
HealthFog: یک سیستم هوشمند درمانی هوشمند مبتنی بر یادگیری عمیق برای تشخیص خودکار بیماری های قلبی در محیط های IoT و محاسبات مه-2020
Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.
Keywords: Fog computing | Internet of things | Healthcare | Deep learning | Ensemble learning | Heart patient analysis
مقاله انگلیسی
8 AI-Enabled Reliable Channel Modeling Architecture for Fog Computing Vehicular Networks
معماری مدل قابل اطمینان کانال دارای قابلیت هوش مصنوعی برای شبکه های خودروی محاسباتی مه-2020
Artificial intelligence (AI)-driven fog computing (FC) and its emerging role in vehicular networks is playing a remarkable role in revolutionizing daily human lives. Fog radio access networks are accommodating billions of Internet of Things devices for real-time interactive applications at high reliability. One of the critical challenges in today’s vehicular networks is the lack of standard wireless channel models with better quality of service (QoS) for passengers while enjoying pleasurable travel (i.e., highly visualized videos, images, news, phone calls to friends/relatives). To remedy these issues, this article contributes significantly in four ways. First, we develop a novel AI-based reliable and interference-free mobility management algorithm (RIMMA) for fog computing intra-vehicular networks, because traffic monitoring and driver’s safety management are important and basic foundations. The proposed RIMMA in association with FC significantly improves computation, communication, cooperation, and storage space. Furthermore, its self-adaptive, reliable, intelligent, and mobility-aware nature, and sporadic contents are monitored effectively in highly mobile vehicles. Second, we propose a reliable and delay-tolerant wireless channel model with better QoS for passengers. Third, we propose a novel reliable and efficient multi-layer fog driven inter-vehicular framework. Fourth, we optimize QoS in terms of mobility, reliability, and packet loss ratio. Also, the proposed RIMMA is compared to an existing competitive conventional method (i.e., baseline). Experimental results reveal that the proposed RIMMA outperforms the traditional technique for intercity vehicular networks
مقاله انگلیسی
9 Crowd V-IoE: Visual Internet of Everything Architecture in AI-Driven Fog Computing
Crowd V-IoE:معماری اینترنت بصری همه چیز در محاسبات مه هوش مصنوعی محور -2020
Fog computing has emerged as a unifying platform to provide computing, communication, and storage for a variety of mobile applications. That helps achieve high bandwidth, high intelligence, low latency, and low energy consumption in handling massive networking devices and emerging rich multimedia services in 5G networks. Current prominence and future promises are changing from the Internet of Things (IoT) to the Internet of Everything (IoE), which is a union of people, process, data, and things. However, the development of fog radio access networks (F-RANs) is challenged by the diversity of IoE, ultra-high-definition videos on demand from users, and low-latency requirement of heterogeneous IoT devices. In this article, we present an architecture of visual IoE (V-IoE) in F-RANs. We systemically analyze the key challenges of V-IoE from the perspective of F-RANs, and propose a crowd V-IoE architecture. Through experimental results, we demonstrate that our proposed architecture exhibits better performance with lower bandwidth requirement, lower energy consumption, and lower latency in F-RANs. Finally, we conclude with a discussion of potential directions.
مقاله انگلیسی
10 Blockchain leveraged decentralized IoT eHealth framework
چارچوب سلامت الکترونیک اینترنت اشیا غیر متمرکز اهرمی بلاکچین-2019
Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data.
Keywords: Internet of Things | Blockch | ain Consensus mechanism | Proof of stake | Patient agent | Fog computing | Edge computing | Cloud | Patient monitoring | eHealth | Fuzzy inference process | Task offloading
مقاله انگلیسی
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