Design a Distributed Fog Computing Scheme to Enhance Processing Performance in Real-Time IoT Applications

2019 ◽  
pp. 99-112 ◽  
Author(s):  
Shin-Jer Yang ◽  
Wan-Lin Lu
Keyword(s):  
Real Time ◽  
Fog Computing ◽  
Iot Applications ◽  
Computing Scheme

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.

Design a D-Fog Scheme to Enhance Processing Performance of Real-Time IoT Applications in Fog Computing

2021 ◽  
Vol 22 (6) ◽  
pp. 1335-1345
Author(s):  
Shin-Jer Yang Shin-Jer Yang ◽  
Wen-Hwa Liao Shin-Jer Yang ◽  
Wan-Lin Lu Wen-Hwa Liao
Keyword(s):  
Real Time ◽  
Fog Computing ◽  
Iot Applications

scholarly journals An experimental study of fog and cloud computing in CEP-based Real-Time IoT applications

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Giovanny Mondragón-Ruiz ◽  
Alonso Tenorio-Trigoso ◽  
Manuel Castillo-Cara ◽  
Blanca Caminero ◽  
Carmen Carrión
Keyword(s):  
Real Time ◽  
Event Detection ◽  
Low Cost ◽  
Fog Computing ◽  
Iot Applications ◽  
Local Resources ◽  
Latency Analysis ◽  
Real Time Applications ◽  
Time Required ◽  
Processing Architecture

AbstractInternet of Things (IoT) has posed new requirements to the underlying processing architecture, specially for real-time applications, such as event-detection services. Complex Event Processing (CEP) engines provide a powerful tool to implement these services. Fog computing has raised as a solution to support IoT real-time applications, in contrast to the Cloud-based approach. This work is aimed at analysing a CEP-based Fog architecture for real-time IoT applications that uses a publish-subscribe protocol. A testbed has been developed with low-cost and local resources to verify the suitability of CEP-engines to low-cost computing resources. To assess performance we have analysed the effectiveness and cost of the proposal in terms of latency and resource usage, respectively. Results show that the fog computing architecture reduces event-detection latencies up to 35%, while the available computing resources are being used more efficiently, when compared to a Cloud deployment. Performance evaluation also identifies the communication between the CEP-engine and the final users as the most time consuming component of latency. Moreover, the latency analysis concludes that the time required by CEP-engine is related to the compute resources, but is nonlinear dependent of the number of things connected.

Evolution of Fog Computing and Its Role in IoT Applications

2018 ◽  
pp. 33-52 ◽  
Author(s):  
R. Alageswaran ◽  
S. Miruna Joe Amali
Keyword(s):  
Decision Making ◽  
Cloud Computing ◽  
Real Time ◽  
Fog Computing ◽  
Low Latency ◽  
Location Awareness ◽  
Reasonable Time ◽  
Iot Applications ◽  
Time Interaction ◽  
Geographically Distributed

Fog computing is an evolving technology that brings the benefits achieved by cloud computing to the periphery of the network devices for faster data analytics. This has triggered the usage of fog computing for enabling a new breed of applications and services that require localized and faster decision making. Fog computing has attributes such as location awareness, edge deployment and a large number of geographically distributed nodes, heterogeneity through which fog computing offers better performance in terms of mobility, low latency, and real-time interaction. They can also gracefully handle enormous data flow and provide analytics in reasonable time. Due to these additional attributes, fog computing is considered as the appropriate platform for many applications and especially suited for internet of things (IoT). Fog computing also provides an intelligent platform to manage the distributed and real-time nature of emerging IoT applications and infrastructures. With the increase in the number of connected objects, the development of fog computing is tremendous and has promising technological future growth.

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.

scholarly journals Towards smarter cities taking advantage of the Fog Computing paradigm

2018 ◽  
Vol 16 (45) ◽  
Author(s):  
Shouddy Tárano León ◽  
Tatiana Delgado Fernández ◽  
Alejandro Luar Pérez Colomé
Keyword(s):  
Real Time ◽  
Recommendation System ◽  
Smart Cities ◽  
Fog Computing ◽  
Decision Making Process ◽  
End User ◽  
Latency Reduction ◽  
Iot Applications ◽  
Computing Paradigm ◽  
The Internet Of Things

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 tourists based in fog computing embraced in a large smart city project located in Havana.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

scholarly journals Goals and measures for analyzing power consumption data in manufacturing enterprises

2021 ◽  
Vol 3 (1) ◽  
pp. 65-82
Author(s):  
Sören Henning ◽  
Wilhelm Hasselbring ◽  
Heinz Burmester ◽  
Armin Möbius ◽  
Maik Wojcieszak
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Manufacturing Industry ◽  
Fog Computing ◽  
Electrical Power ◽  
Time Data ◽  
Manufacturing Enterprises ◽  
Computing Paradigm ◽  
Consumption Data ◽  
Analyzing Power

AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.

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