Internet of Things Laboratory Test Bed

2015 ◽  
pp. 485-494 ◽  
Author(s):  
Ruslan Kirichek ◽  
Andrey Koucheryavy
Keyword(s):  
Internet Of Things ◽  
Laboratory Test ◽  
Test Bed

An integrated laboratory test bed for rocket engine health monitoring experimentation

1988 ◽  
Author(s):  
KEITH NGUYEN ◽  
DARROW COLE ◽  
JOHN PERRY ◽  
ARNIE NORMAN
Keyword(s):  
Laboratory Test ◽  
Health Monitoring ◽  
Rocket Engine ◽  
Test Bed

A Big Data Test-bed for Analyzing Data Generated by an Air Pollution Sensor Network

2016 ◽  
Vol 13 (4) ◽  
pp. 19-35 ◽  
Author(s):  
Lídice García Ríos ◽  
José Alberto Incera Diéguez
Keyword(s):  
Air Pollution ◽  
Big Data ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Sensor Network ◽  
Proof Of Concept ◽  
Test Bed ◽  
Pollution Levels ◽  
Wide Range ◽  
Military Applications

Sensor networks have perceived an extraordinary growth in the last few years. From niche industrial and military applications, they are currently deployed in a wide range of settings as sensors are becoming smaller, cheaper and easier to use. Sensor networks are a key player in the so-called Internet of Things, generating exponentially increasing amounts of data. Nonetheless, there are very few documented works that tackle the challenges related with the collection, manipulation and exploitation of the data generated by these networks. This paper presents a proposal for integrating Big Data tools (in rest and in motion) for gathering, storage and analysis of data generated by a sensor network that monitors air pollution levels in a city. The authors provide a proof of concept that combines Hadoop and Storm for data processing, storage and analysis, and Arduino-based kits for constructing their sensor prototypes.

Multi-Machine Synchronous Islanding Achieved in a Laboratory Test Bed Utilizing PMUs

2020 ◽  
Author(s):  
Mats-Robin Jacobsen ◽  
David Laverty ◽  
Robert J. Best ◽  
John C. Hastings
Keyword(s):  
Laboratory Test ◽  
Test Bed

scholarly journals Optimization and Design of a Cable Driven Upper Arm Exoskeleton

2009 ◽  
Author(s):  
Sunil K. Agrawal ◽  
Venketesh N. Dubey ◽  
John J. Gangloff ◽  
Elizabeth Brackbill ◽  
Vivek Sangwan
Keyword(s):  
Laboratory Test ◽  
Parameter Optimization ◽  
Kinetic Models ◽  
Arm Movements ◽  
Stroke Survivors ◽  
Research Experience ◽  
Test Bed ◽  
Joint Angles ◽  
Upper Arm ◽  
Wearable Exoskeleton

This paper presents the design of a wearable upper arm exoskeleton that can be used to assist and train arm movements of stroke survivors or subjects with weak musculature. In the last ten years, a number of upper-arm training devices have emerged. However, due to their size and weight, their use is restricted to clinics and research laboratories. Our proposed wearable exoskeleton builds upon our extensive research experience in wire driven manipulators and design of rehabilitative systems. The exoskeleton consists of three main parts: (i) an inverted U-shaped cuff that rests on the shoulder, (ii) a cuff on the upper arm, and (iii) a cuff on the forearm. Six motors, mounted on the shoulder cuff, drive the cuffs on the upper arm and forearm, using cables. In order to assess the performance of this exoskeleton, prior to use on humans, a laboratory test-bed has been developed where this exoskeleton is mounted on a model skeleton, instrumented with sensors to measure joint angles and transmitted forces to the shoulder. This paper describes design details of the exoskeleton and addresses the key issue of parameter optimization to achieve useful workspace based on kinematic and kinetic models.

Internet of Things (IoTs) Security: Intrusion Detection using Deep Learning

2021 ◽  
Author(s):  
Ozgur Koray Sahingoz ◽  
Ugur Cekmez ◽  
Ali Buldu
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Recent Literature ◽  
Imbalanced Data ◽  
Industrial Applications ◽  
Test Bed ◽  
Detection Mechanism ◽  
Network Attacks ◽  
Negative Effect ◽  
Augmentation Techniques

With the development of sensor and communication technologies, the use of connected devices in industrial applications has been common for a long time. Reduction of costs during this period and the definition of Internet of Things (IoTs) concept have expanded the application area of small connected devices to the level of end-users. This paved the way for IoT technology to provide a wide variety of application alternative and become a part of daily life. Therefore, a poorly protected IoT network is not sustainable and has a negative effect on not only devices but also the users of the system. In this case, protection mechanisms which use conventional intrusion detection approaches become inadequate. As the intruders’ level of expertise increases, identification and prevention of new kinds of attacks are becoming more challenging. Thus, intelligent algorithms, which are capable of learning from the natural flow of data, are necessary to overcome possible security breaches. Many studies suggesting models on individual attack types have been successful up to a point in recent literature. However, it is seen that most of the studies aiming to detect multiple attack types cannot successfully detect all of these attacks with a single model. In this study, it is aimed to suggest an all-in-one intrusion detection mechanism for detecting multiple intrusive behaviors and given network attacks. For this aim, a custom deep neural network is designed and implemented to classify a number of different types of network attacks in IoT systems with high accuracy and F1-score. As a test-bed for comparable results, one of the up-to-date dataset (CICIDS2017), which is highly imbalanced, is used and the reached results are compared with the recent literature. While the initial propose was successful for most of the classes in the dataset, it was noted that achievement was low in classes with a small number of samples. To overcome imbalanced data problem, we proposed a number of augmentation techniques and compared all the results. Experimental results showed that the proposed methods yield highest efficiency among observed literature.

scholarly journals An IoT Architecture for Water Resource Management in Agroindustrial Environments: A Case Study in Almería (Spain)

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 596 ◽  
Author(s):  
Manuel Muñoz ◽  
Juan Gil ◽  
Lidia Roca ◽  
Francisco Rodríguez ◽  
Manuel Berenguel
Keyword(s):  
Internet Of Things ◽  
Water Resource Management ◽  
Public Utility ◽  
Operating Costs ◽  
Control Technique ◽  
Agricultural System ◽  
Test Bed ◽  
Efficient Management ◽  
Mediterranean Countries

The current agricultural water panorama in many Mediterranean countries is composed by desalination facilities, wells (frequently overexploited), the water public utility network, and several consumer agents with different water needs. This distributed water network requires centralized management methods for its proper use, which are difficult to implement as the different agents are usually geographically separated. In this sense, the use of enabling technologies such as the Internet of Things can be essential to the proper operation of these agroindustrial systems. In this paper, an Internet of Things cloud architecture based on the FIWARE standard is proposed for interconnecting the several agents that make up the agroindustrial system. In addition, this architecture includes an efficient management method based on a model predictive control technique, which is aimed at minimizing operating costs. A case study inspired by three real facilities located in Almería (southeast of Spain) is used as the simulation test bed. The obtained results show how around 75% of the total operating costs can be saved with the application of the proposed approach, which could be very significant to decrease the costs of desalinated water and, therefore, to maintain the sustainability of the agricultural system.

scholarly journals Internet of Things (IoT) Platform for Structure Health Monitoring

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Ahmed Abdelgawad ◽  
Kumar Yelamarthi
Keyword(s):  
Mathematical Model ◽  
Internet Of Things ◽  
Health Monitoring ◽  
Structural Damage ◽  
Piezoelectric Sensors ◽  
Raspberry Pi ◽  
Test Bed ◽  
Digital To Analog Converter ◽  
Analog To Digital ◽  
Structural Health

Increase in the demand for reliable structural health information led to the development of Structural Health Monitoring (SHM). Prediction of upcoming accidents and estimation of useful life span of a structure are facilitated through SHM. While data sensing is the core of any SHM, tracking the data anytime anywhere is a prevailing challenge. With the advancement in information technology, the concept of Internet of Things (IoT) has made it possible to integrate SHM with Internet to track data anytime anywhere. In this paper, a SHM platform embedded with IoT is proposed to detect the size and location of damage in structures. The proposed platform consists of a Wi-Fi module, a Raspberry Pi, an Analog to Digital Converter (ADC), a Digital to Analog Converter (DAC), a buffer, and piezoelectric (PZT) sensors. The piezoelectric sensors are mounted as a pair in the structure. Data collected from the piezoelectric sensors will be used to detect the size and location of damage using a proposed mathematical model. Implemented on a Raspberry Pi, the proposed mathematical model will estimate the size and location of structural damage, if any, and upload the data to Internet. This data will be stored and can be checked remotely from any mobile device. The system has been validated using a real test bed in the lab.

scholarly journals Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

2021 ◽  
Vol 11 (8) ◽  
pp. 3662
Author(s):  
Cosmas Ifeanyi Nwakanma ◽  
Fabliha Bushra Islam ◽  
Mareska Pratiwi Maharani ◽  
Jae-Min Lee ◽  
Dong-Seong Kim
Keyword(s):  
Internet Of Things ◽  
Human Activity ◽  
Real Life ◽  
Vibration Sensor ◽  
Smart Factory ◽  
Occupational Accidents ◽  
Shop Floor ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor

Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.

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