A clogging resistant secure authentication scheme for fog computing services

Today, there exists a growing demand for Internet of Things (IoT) services
 in the form of vehicle networks, smart cities, augmented reality, virtual reality, positioning systems, and so on. Due to the considerable distance between the IoT devices and the central cloud, using this option may no longer be a suitable solution for delay-constraint tasks. To overcome these drawbacks, a complementary solution called fog computing, also known as the cloud at the edge is used. In this solution, nodes at the edge of the network provide resources for IoT applications. Although offloading tasks on the fog nodes save energy on IoT devices, it increases task response time. Therefore, making a trade-off between energy consumption and latency is crucial for IoT devices. Because offloading falls into the category of NP-hard knapsack problems, metaheuristic methods have been widely used in recent years. In this paper, we formulate the problem of joint optimization of energy consumption and latency in the form of a multi-objective problem and solve it using the non-dominant sorting genetic algorithm (NSGA-II) and Bees algorithm (BA). Also, to improve the quality of solutions, we combine each of these methods with a robust type of differential evolution approach called minimax differential evolution (MMDE). This combination moves the solutions to better areas and increases the convergence speed. The simulation results show that NSGA-based methods have remarkable robustness compared to BA-based methods in terms of significant criteria such as energy consumption, time delay, and so on. Our statistical analysis shows that both NSGA-based and BA-based metaheuristic methods not only do not significantly increase energy consumption but also drastically reduce response time.

Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm

There is an exponential increase in the use of Industrial Internet of Things (IIoT) devices for controlling and monitoring the machines in an automated manufacturing industry. Different temperature sensors, pressure sensors, audio sensors, and camera devices are used as IIoT devices for pipeline monitoring and machine operation control in the industrial environment. But, monitoring and identifying the machine malfunction in an industrial environment is a challenging task. In this article, we consider machines fault diagnosis based on their operating sound using the fog computing architecture in the industrial environment. The different computing units, such as industrial controller units or micro data center are used as the fog server in the industrial environment to analyze and classify the machine sounds as normal and abnormal. The linear prediction coefficients and Mel-frequency cepstral coefficients are extracted from the machine sound to develop and deploy supervised machine learning (ML) models on the fog server to monitor and identify the malfunctioning machines based on the operating sound. The experimental results show the performance of ML models for the machines sound recorded with different signal-to-noise ratio levels for normal and abnormal operations.

Fog-Based Intelligent Machine Malfunction Monitoring System for Industry 4.0

A Middleware Platform for Intelligent Automation: An Industrial Prototype Implementation

https://onlinelibrary.wiley.com/doi/pdf/10.1002/spy2.145

Fog computing security and privacy for the Internet of Thing applications: State-of-the-art

Fog computing at industrial level, architecture, latency, energy, and security: A review

Induction motors are widely used at industrial level and many researches have been conducted to predict faults and ensure their continuous operation. The present study shows a fault detection system based on the Motor Current Signature Analysis (MCSA) technique which is a non-invasive method, and the Fast Fourier Transform (FFT) algorithm to perform spectral analysis on the current to detect specific components that characterize faults in different conditions such as damaged bearings, shorted winding in turns, and broken bars. The tests were conducted using a motor test bench and the results show a spectral value obtained by FFT in the frequency of 300 Hz for faults in bearings of the motors 1 and 2. Results also reveal coincidence of 96.9% and 99.46% compared to the results obtained through the theoretical equations included in the MCSA technique, representing values that are within the fault frequency bands on 1x and 2x respectively.

Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis

Comparative Analysis of High Frequencies for the Broken Bar Fault Diagnosis Using MCSA and Park’s Vector Demodulation

Augmented Reality for Learning Sign Language Using Kinect Tool

Technology provides a great variety of opportunities in different educational fields from virtual libraries to the use of state-of-the-art equipment and sensors such as the Leap Motion Controller, which highlights due to its precision when detecting gestures made with hands and fingers. In this study, this sensor was utilized together with Unity to develop an application called VISDOM motor, which enables consolidating the learning of electric motors in an entertaining and dynamic manner, operating a fluent and easy to use interface, with gestures that are natural and adaptable to both right-handed and left-handed people. Functionality and satisfaction tests were carried out to validate the application, which exhibited good results with an average score of 94% in the functionality of the application.

William Oñate

Papers

Fog computing at industrial level, architecture, latency, energy, and security: A review

Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis

Comparative Analysis of High Frequencies for the Broken Bar Fault Diagnosis Using MCSA and Park’s Vector Demodulation

Augmented Reality for Learning Sign Language Using Kinect Tool

Interactive Application for Learning Electric Motors Using Leap Motion Controller