scholarly journals Fog Computing Framework for Smart City Design

2020 ◽  
Vol 14 (01) ◽  
pp. 109
Author(s):  
Mais Haj Qasem ◽  
Alaa Abu-Srhan ◽  
Hutaf Natoureah ◽  
Esra Alzaghoul
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Network Architecture ◽  
Data Exchange ◽  
Fog Computing ◽  
Computing Paradigm ◽  
Network Usage ◽  
The Cost

Fog-computing is a new network architecture and computing paradigm that uses user or near-users devices (network edge) to carry out some processing tasks. Accordingly, it extends the cloud computing with more flexibility the one found in the ubiquitous networks. A smart city based on the concept of fog-computing with flexible hierarchy is proposed in this paper. The aim of the proposed design is to overcome the limitations of the previous approaches, which depends on using various network architectures, such as cloud-computing, autonomic network architecture and ubiquitous network architecture. Accordingly, the proposed approach achieves a reduction of the latency of data processing and transmission with enabled real-time applications, distribute the processing tasks over edge devices in order to reduce the cost of data processing and allow collaborative data exchange among the applications of the smart city. The design is made up of 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 connection layer, real-time processing layer, neighborhood linking layer, main-processing layer, data server layer. A case study of a novel smart public car parking, traveling and direction advisor is implemented using IFogSim and the results showed that reduce the delay of real-time application significantly, reduce the cost and network usage compared to the cloud-computing paradigm. Moreover, the proposed approach, although, it increases the scalability and reliability of the users’ access, it does not sacrifice much time, nor cost and network usage compared to fixed fog-computing design.

scholarly journals DM-MQTT: An Efficient MQTT Based on SDN Multicast for Massive IoT Communications

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3071 ◽  
Author(s):  
Jun-Hong Park ◽  
Hyeong-Su Kim ◽  
Won-Tae Kim
Keyword(s):  
Cloud Computing ◽  
Traffic Congestion ◽  
Data Exchange ◽  
Data Transfer ◽  
Edge Computing ◽  
Computing Paradigm ◽  
Transfer Delay ◽  
Network Usage ◽  
Iot Devices ◽  
Edge Networks

Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.

Processing IoT Data

2021 ◽  
pp. 920-943
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Aditya Brahmachari ◽  
Prasenjit Choudhury
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Fog Computing ◽  
Batch Processing ◽  
Time Response ◽  
Iot Applications

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them.

Fog Computing

Fog Computing ◽  
2018 ◽  
pp. 198-207 ◽  
Author(s):  
Chintan M. Bhatt ◽  
C. K. Bhensdadia
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Real Time ◽  
Fog Computing ◽  
The Internet ◽  
Research Issues ◽  
Computing Paradigm ◽  
Time Interaction ◽  
Edge Network ◽  
The Internet Of Things

The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.

scholarly journals An Alertness-Adjustable Cloud/Fog IoT Solution for Timely Environmental Monitoring Based on Wildfire Risk Forecasting

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3693
Author(s):  
Athanasios Tsipis ◽  
Asterios Papamichail ◽  
Ioannis Angelis ◽  
George Koufoudakis ◽  
Georgios Tsoumanis ◽  
...  
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Fog Computing ◽  
Fire Risk ◽  
Hybrid Network ◽  
Case Scenario ◽  
Wildfire Risk ◽  
Computing Paradigm ◽  
Risk Forecasting ◽  
Burning Index

Internet of Things (IoT) appliances, especially those realized through wireless sensor networks (WSNs), have been a dominant subject for heavy research in the environmental and agricultural sectors. To address the ever-increasing demands for real-time monitoring and sufficiently handle the growing volumes of raw data, the cloud/fog computing paradigm is deemed a highly promising solution. This paper presents a WSN-based IoT system that seamlessly integrates all aforementioned technologies, having at its core the cloud/fog hybrid network architecture. The system was intensively validated using a demo prototype in the Ionian University facilities, focusing on response time, an important metric of future smart applications. Further, the developed prototype is able to autonomously adjust its sensing behavior based on the criticality of the prevailing environmental conditions, regarding one of the most notable climate hazards, wildfires. Extensive experimentation verified its efficiency and reported on its alertness and highly conforming characteristics considering the use-case scenario of Corfu Island’s 2019 fire risk severity. In all presented cases, it is shown that through fog leveraging it is feasible to contrive significant delay reduction, with high precision and throughput, whilst controlling the energy consumption levels. Finally, a user-driven web interface is highlighted to accompany the system; it is capable of augmenting the data curation and visualization, and offering real-time wildfire risk forecasting based on Chandler’s burning index scoring.

scholarly journals Fog Computing Mitigate Limitations of Cloud Computing

2020 ◽  
Vol 8 (5) ◽  
pp. 1732-1736
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Real Time ◽  
Fog Computing ◽  
Public Cloud ◽  
Time Data ◽  
Security Issues ◽  
Computing Model ◽  
Real Time Data ◽  
Real Time Data Processing

In today’s world, cloud computing is the most exciting and advanced technology. It came into existence with lots of advantages, but cloud-only computing has some disadvantages also like latency in real-time data processing, network congestion, less bandwidth utilization, fault tolerance, and security issues in public cloud. To address the issue of real-time data-processing and security in public cloud new computing model are used which is known as Fog Computing. It is nearer to the client or edge so that it can reduce the latency in real time data-processing and security in public cloud using techniques like user profiling and decoying technique. Fog Computing help us to overcome the latency and security issues of cloud computing. It reduces cloud latency in real time data-processing because fog computing model is nearer to the edge devices. It also improves cloud security in the public cloud.

scholarly journals Integration of Fog and IoT with Multiensor Data Gathering

2019 ◽  
Vol 8 (4) ◽  
pp. 11785-11787
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Fog Computing ◽  
Data Gathering ◽  
Low Latency ◽  
Computing Paradigm ◽  
City System ◽  
Computing Framework ◽  
The City

In the already existing system number of internet connected devices rapidly increase, this increased demand real-time, for the standard cloud computing framework, low latency services proving to be always a challenge. While In the proposed System, fog computing paradigm serves the demands of the latency sensitive applications in the context of IOT. The IOT is rely on cloud computing by passing information about sensor. This is a decentralized process to gather the information from each and every region of the city. System will check the energy and location of every server. Because whenever server uploads the sensor details it can degrade their energy on every time. So we have to migrate the data by allocating another server which contains the energy to send

scholarly journals A Smart Water Metering Deployment Based on the Fog Computing Paradigm

2020 ◽  
Vol 10 (6) ◽  
pp. 1965 ◽  
Author(s):  
Dimitrios Amaxilatis ◽  
Ioannis Chatzigiannakis ◽  
Christos Tselios ◽  
Nikolaos Tsironis ◽  
Nikos Niakas ◽  
...  
Keyword(s):  
Smart City ◽  
Data Privacy ◽  
Data Exchange ◽  
Fog Computing ◽  
Cloud Services ◽  
Security And Privacy ◽  
Smart Water ◽  
Computing Paradigm ◽  
Computational Performance ◽  
Water Metering

In this paper, we look into smart water metering infrastructures that enable continuous, on-demand and bidirectional data exchange between metering devices, water flow equipment, utilities and end-users. We focus on the design, development and deployment of such infrastructures as part of larger, smart city, infrastructures. Until now, such critical smart city infrastructures have been developed following a cloud-centric paradigm where all the data are collected and processed centrally using cloud services to create real business value. Cloud-centric approaches need to address several performance issues at all levels of the network, as massive metering datasets are transferred to distant machine clouds while respecting issues like security and data privacy. Our solution uses the fog computing paradigm to provide a system where the computational resources already available throughout the network infrastructure are utilized to facilitate greatly the analysis of fine-grained water consumption data collected by the smart meters, thus significantly reducing the overall load to network and cloud resources. Details of the system’s design are presented along with a pilot deployment in a real-world environment. The performance of the system is evaluated in terms of network utilization and computational performance. Our findings indicate that the fog computing paradigm can be applied to a smart grid deployment to reduce effectively the data volume exchanged between the different layers of the architecture and provide better overall computational, security and privacy capabilities to the system.

SONATA

2017 ◽  
pp. 278-299
Author(s):  
Burak Kantarci ◽  
Kevin G. Carr ◽  
Connor D. Pearsall
Keyword(s):  
Cloud Computing ◽  
Social Network ◽  
Smart City ◽  
Network Architecture ◽  
Mobile Cloud ◽  
Detection Techniques ◽  
Dynamic Social Network ◽  
Computing Paradigm ◽  
Data Trustworthiness ◽  
Performance Results

With the advent of mobile cloud computing paradigm, mobile social networks (MSNs) have become attractive tools to share, publish and analyze data regarding everyday behavior of mobile users. Besides revealing information about social interactions between individuals, MSNs can assist smart city applications through crowdsensing services. In presence of malicious users who aim at misinformation through manipulation of their sensing data, trustworthiness arises as a crucial issue for the users who receive service from smart city applications. In this paper, the authors propose a new crowdsensing framework, namely Social Network Assisted Trustworthiness Assurance (SONATA) which aims at maximizing crowdsensing platform utility and minimizing the manipulation probability through vote-based trustworthiness analysis in dynamic social network architecture. SONATA adopts existing Sybil detection techniques to identify malicious users who aim at misinformation/disinformation at the crowdsensing platform. The authors present performance evaluation of SONATA under various crowdsensing scenarios in a smart city setting. Performance results show that SONATA improves crowdsensing utility under light and moderate arrival rates of sensing task requests when less than 7% of the users are malicious whereas crowdsensing utility is significantly improved under all task arrival rates if the ratio of malicious users to the entire population is at least 7%. Furthermore, under each scenario, manipulation ratio is close to zero under SONATA while trustworthiness unaware recruitment of social network users leads to a manipulation probability of 2.5% which cannot be tolerated in critical smart city applications such as disaster management or public safety.

Processing IoT Data

2018 ◽  
pp. 124-148 ◽  
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Aditya Brahmachari ◽  
Prasenjit Choudhury
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Fog Computing ◽  
Batch Processing ◽  
Time Response ◽  
Iot Applications

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them.

Fog Computing

2017 ◽  
Vol 9 (4) ◽  
pp. 105-113 ◽  
Author(s):  
Chintan Bhatt ◽  
C. K. Bhensdadia
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Real Time ◽  
Fog Computing ◽  
The Internet ◽  
Research Issues ◽  
Computing Paradigm ◽  
Time Interaction ◽  
Edge Network ◽  
The Internet Of Things

The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.

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