Designed Features for Improving Openness, Scalability and Programmability in the Fog Computing-Based IoT Systems (original) (raw)
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Internet of things (IoT) services have been accepted and accredited globally for the past couple of years and have had increasing interest from researchers. Internet of Things (IoT), requires mobility support and geo-distribution in addition to location awareness and low latency. We argue that a new platform is needed to meet these requirements; a platform we call Fog
Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study
In this paper, we discuss the most significant application opportunities and outline the challenges in performing a real-time and energy-efficient management of the distributed resources available at mobile devices and Internet-to-Data Center. We also present an energy-efficient adaptive scheduler for Vehicular Fog Computing (VFC) that operates at the edge of a vehicular network, connected to the served Vehicular Clients (VCs) through an Infrastructure-to-Vehicular (I2V) over multiple Foglets (Fls). The scheduler optimizes the energy by leveraging the heterogeneity of Fls, where the Fl provider shapes the system workload by maximizing the task admission rate over data transfer and computation. The presented scheduling algorithm demonstrates that the resulting adaptive scheduler allows scalable and distributed implementation.
A survey on fog computing for the Internet of Things
Pervasive and Mobile Computing, 2019
Fog computing has emerged to support the requirements of IoT applications that could not be met by today's solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.
IoT enabled Smart Fog Computing for Vehicular Traffic Control
EAI Endorsed Transactions on Internet of Things, 2019
INTRODUCTION: Internet was initially designed to connect web sites and portals with data packets flowing over the networks for communications at corporate levels. Over time, live video streaming, real-time data and voice is being offered over hosted Clouds for business entertainment. Enterprise applications like Office 365, banking and e-commerce are available over smartphones. With the advent of Fog Computing and Internet of Things, corporate enterprises and non-IT industries see potential in this technology. Billions of Internet-enabled devices, globally distributed nodes, embedded sensor gateways transmit real-time generated over the internet to the cloud data centres. Cloud environments are not designed to handle this level of data that is being generated and Computing limits are being severely tested. Fog Computing has the potential to be the go-to option for Cloud service delivery. OBJECTIVES: This paper reviewed existing research works and presents unique Smart Fog Computing based taxonomy. The authors also implemented experimental setup for Smart Cities using Smart Fog Computing for controlling Vehicular traffic. METHODS: Smart Vehicular Management is viable use case for Fog and IoT technology. The authors designed and implemented two experimental setups. The first setup involves standard Cloud implementation and the second setup employs Fog Computing implemented using IoT Sensor nodes to compare the performance of the Vehicle Management Fog application regarding the Response time and Bandwidth Consumed. The architecture and implementation involved deploying 50 IoT sensors nodes across the university areas and routes. RESULTS: The main results obtained in this paper are the following. As compared to Cloud computing, on deploying Fog Computing and IoT devices: End-to-End Processing time dropped from 29.44 to 6.7 seconds almost 77% less Number of hops traversed reduced from 56 to 4 hops almost 92% less Bandwidth usage dropped from 247 to 8 kbps almost 96.7% less CONCLUSION: From the experimental setups as compared to Cloud computing, the Fog and IoT processes the traffic data locally on the edge devices, which reduces the end-to-end time.
Design, Resource Management, and Evaluation of Fog Computing Systems: A Survey
IEEE Internet of Things Journal, 2021
A steady increase in Internet of Things (IoT) applications needing large-scale computation and long-term storage has lead to an over-reliance on Cloud computing. The resulting network congestion in Cloud, coupled with the distance of Cloud data centres from IoT, contribute to unreliable endto-end response delay. Fog computing has been introduced as an alternative to cloud, providing low-latency service by bringing processing and storage resources to the network edge. In this survey, we sequentially present the phases required in the implementation and realization of practical fog computing systems: (1) design & dimensioning of a fog infrastructure, (2) fog resource provisioning for IoT application use and IoT resource allocation to fog, (3) installation of fog frameworks for fog resource management, and (4) evaluation of fog infrastructure through simulation & emulation. Our focus is determining the implementation aspects required to build a practical large scale fog computing infrastructure to support the general IoT landscape.
Fog Computing Framework for Smart City Design
2020
Fog computing is a new network architecture and computing paradigm that uses user or near-user devices (network edge) to conduct some processing tasks. Accordingly, this network architecture extends cloud computing with more flexibility than that found in ubiquitous networks. A smart city based on the concept of fog computing with flexible hierarchy is proposed in this study. The aim of the proposed design is to overcome the limitations of previous approaches, which depend on using various network architectures, such as cloud computing, autonomic network architecture, and ubiquitous network architecture. Accordingly, the proposed approach reduces the latency of data processing and transmission with enabled real-time applications, distributes processing tasks over edge devices to reduce the cost of data processing, and allows collaborative data exchange among the applications of a smart city. The design comprises five major layers, which can be increased or merged according to the amount of data processing and transmission in each application. The involved layers are as follows: connection, real-time processing, neighborhood linking, main processing, and data server layers. A case study of a novel smart public car parking, traveling, and direction advisor is implemented using iFog-Sim, and results show that the delay of real-time application, cost, and network usage are significantly reduced compared with that of a cloud computing paradigm. Moreover, the proposed approach increases the scalability and reliability of user access without considerably compromising time, cost, and network usage compared with fixed fog computing.
Towards smarter cities taking advantage of the Fog Computing paradigm
Sistemas y Telemática
The fog computing term has achieved importance in the last years due to its effect in the latency reduction that the Internet of Things [IoT] applications have. These applications demand real-time (or nearly real-time) responses and they are characterized by low bandwidth consumption; hence, the fog computing is relevant in achieving these requests because part of the processing is done near the end user devices. For this reason, the cloud computing paradigm is not enough for some applications, since nowadays, the instant need of data and the decision-making process leverage –or somehow discover– a new horizon that demands a complementary variable. This article consists on an approach to the fog computing term, together with the requirements analysis for engineering solutions in the IoT field. Also, its impact in the smart cities and other fields plus its main challenges are addressed. We also present a guideline to implement a recommendation system for sightseeing places for touris...
Fog Function Virtualization: A flexible solution for IoT applications
2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), 2017
The Internet of Things applications must carefully assess certain crucial factors such as the real-time and largely distributed nature of the "things". Fog Computing provides an architecture to satisfy those requirements through nodes located from near the "things" till the edge. The problem comes with the integration of the Fog nodes into current infrastructures. This process requires the development of complex software solutions and prevents Fog growth. In this paper we propose three innovations to enhance Fog: (i) a new orchestration policy, (ii) the creation of constellations of nodes, and (iii) Fog Function Virtualization (FFV). All together will complement Fog to reach its true potential as a generic scalable platform, running multiple IoT applications simultaneously. Deploying a new service is reduced to the development of the application code, fact that brings the democratization of the Fog Computing paradigm through ease of deployment and cost reduction.
A Review-Fog Computing and Its Role in the Internet of Things
Fog computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Dening characteristics of the Fog are: a) Low latency and location awareness; b) Widespread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Het-erogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid , Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
Internet of Things, 2019
INTRODUCTION: Internet was initially designed to connect web sites and portals with data packets flowing over the networks for communications at corporate levels. Over time, live video streaming, real-time data and voice is being offered over hosted Clouds for business entertainment. Enterprise applications like Office 365, banking and e-commerce are available over smartphones. With the advent of Fog Computing and Internet of Things, corporate enterprises and non-IT industries see potential in this technology. Billions of Internet-enabled devices, globally distributed nodes, embedded sensor gateways transmit real-time generated over the internet to the cloud data centres. Cloud environments are not designed to handle this level of data that is being generated and Computing limits are being severely tested. Fog Computing has the potential to be the go-to option for Cloud service delivery. OBJECTIVES: This paper reviewed existing research works and presents unique Smart Fog Computing based taxonomy. The authors also implemented experimental setup for Smart Cities using Smart Fog Computing for controlling Vehicular traffic. METHODS: Smart Vehicular Management is viable use case for Fog and IoT technology. The authors designed and implemented two experimental setups. The first setup involves standard Cloud implementation and the second setup employs Fog Computing implemented using IoT Sensor nodes to compare the performance of the Vehicle Management Fog application regarding the Response time and Bandwidth Consumed. The architecture and implementation involved deploying 50 IoT sensors nodes across the university areas and routes. RESULTS: The main results obtained in this paper are the following. As compared to Cloud computing, on deploying Fog Computing and IoT devices: End-to-End Processing time dropped from 29.44 to 6.7 seconds almost 77% less Number of hops traversed reduced from 56 to 4 hops almost 92% less Bandwidth usage dropped from 247 to 8 kbps almost 96.7% less CONCLUSION: From the experimental setups as compared to Cloud computing, the Fog and IoT processes the traffic data locally on the edge devices, which reduces the end-to-end time.