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.

scholarly journals Real-time Neural Networks Implementation Proposal for Microcontrollers

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1597
Author(s):  
Caio José B. V. Guimarães ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Intelligent Systems ◽  
Implementation Strategy ◽  
Processing Time ◽  
Mean Squared Error ◽  
Low Cost ◽  
Real Time Applications ◽  
Time Required ◽  
Process Classification

The adoption of intelligent systems with Artificial Neural Networks (ANNs) embedded in hardware for real-time applications currently faces a growing demand in fields such as the Internet of Things (IoT) and Machine to Machine (M2M). However, the application of ANNs in this type of system poses a significant challenge due to the high computational power required to process its basic operations. This paper aims to show an implementation strategy of a Multilayer Perceptron (MLP)-type neural network, in a microcontroller (a low-cost, low-power platform). A modular matrix-based MLP with the full classification process was implemented as was the backpropagation training in the microcontroller. The testing and validation were performed through Hardware-In-the-Loop (HIL) of the Mean Squared Error (MSE) of the training process, classification results, and the processing time of each implementation module. The results revealed a linear relationship between the values of the hyperparameters and the processing time required for classification, also the processing time concurs with the required time for many applications in the fields mentioned above. These findings show that this implementation strategy and this platform can be applied successfully in real-time applications that require the capabilities of ANNs.

scholarly journals Implementation of asymmetric processing on multi core processors to implement IOT applications on GNU/Linux framework

2018 ◽  
Vol 7 (2.7) ◽  
pp. 710
Author(s):  
Poonam Jain. S ◽  
Pooja S ◽  
Sripath Roy. K ◽  
Abhilash K ◽  
Arvind B V
Keyword(s):  
Real Time ◽  
Large Data ◽  
Fair Share ◽  
Iot Applications ◽  
Multiple Threads ◽  
Real Time Applications ◽  
The One ◽  
Processing Architecture ◽  
Dedicated Processors ◽  
Dedicated Processor

Internet of Things brought in a bigger computing challenges where there came a need for running tasks in a multi-sensor and large data processing is involved. In order to implement this requirement multiprocessors are being used for implementation of IoT Gateways. There comes a need for specific tasks having a resource dedicated for its job. To fulfill this we face a hurdle in choosing dedicated processor or shared processor in a Symmetric Processing Architecture. Dedicated processor are the one in which all the tasks are being processed on a single core where as in fair share processors specific processes are assigned to specific cores. Symmetric processing makes use of dedicated processors where as Asymmetric processor makes use of shared processors. Asymmetric Multi Processing can be used in real time applications in order to solve real time problems, one such platform is IOT. In this paper we have evaluated Asymmetric processing on GNU/Linux Platform to test multiple threads running on different multi-core processors architectures to realize the same for running IOT applications having higher computational requirements in the future. 

Expounding the Edge/Fog Computing Infrastructures for Data Science

2018 ◽  
pp. 1-32 ◽  
Author(s):  
Pethuru Raj ◽  
Pushpa J.
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Analytical Methods ◽  
Data Analytics ◽  
Data Science ◽  
Storage Systems ◽  
Fog Computing ◽  
Next Generation ◽  
Distributed Source ◽  
Real Time Applications

Data is the new fuel for any system to deliver smart and sophisticated services. Data is being touted as the strategic asset for any organization to plan ahead and provide next-generation capabilities with all the clarity and confidence. Whether data is internally sourced or aggregated from different and distributed source, it is essential for all kinds of data to be continuously and consciously collected, transmitted, cleansed, and hosted on storage systems. There are several types of analytical methods and machines to do deeper and decisive analytics on those curated and consolidated data to extract actionable insights in real-time. Precise and concise analytics guarantee perfect decision-making and action. We need competent and highly integrated analytics platform for speeding up, simplifying and streamlining data analytics, which is becoming a hard nut to crack due to the multi-structured and massive quantities of data. On the infrastructure front, we need highly optimized compute, storage and network infrastructure for achieving data analytics with ease. Another noteworthy point is that there are batch, real-time, and interactive processing of data. Most of the personal and professional applications need real-time insights in order to produce real-time applications. That is, real-time capture, processing, and decision-making are being insisted and hence the edge or fog computing concept has become very popular. This chapter is exclusively designed in order to tell all on how to accomplish real-time analytics on fog devices data.

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.

scholarly journals LOCOFloat: A Low-Cost Floating-Point Format for FPGAs.: Application to HIL Simulators

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 81 ◽  
Author(s):  
Alberto Sanchez ◽  
Angel de Castro ◽  
Maria Sofía Martínez-García ◽  
Javier Garrido
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Power Converter ◽  
Buck Converter ◽  
Digital Design ◽  
Floating Point ◽  
Hardware In The Loop ◽  
Time Simulation ◽  
Real Time Applications ◽  
Plant Power

One of the main decisions when making a digital design is which arithmetic is going to be used. The arithmetic determines the hardware resources needed and the latency of every operation. This is especially important in real-time applications like HIL (Hardware-in-the-loop), where a real-time simulation of a plant—power converter, mechanical system, or any other complex system—is accomplished. While a fixed-point gets optimal implementations, using considerably fewer resources and allowing smaller simulation steps, its use is very restricted to very specific applications, as its design effort is quite high. On the other side, IEEE-754 floating-point may have resolution problems in case of the 32-bit version, and excessive hardware usage in case of the 64-bit version. This paper presents LOCOFloat, a low-cost floating-point format designed for FPGA applications. Its key features are soft normalization of the results, using significand and exponent fields in two’s complement. This paper shows the implementation of addition, subtraction and multiplication of the proposed format. Both IEEE-754 versions and LOCOFloat are compared in this paper, implementing a HIL model of a buck converter. Although the application example is a HIL simulator, other applications could take benefit from the proposed format. Results show that LOCOFloat is as accurate as 64-bit floating-point, while reducing the use of DSPs blocks by 84 % .

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