Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Castaño, Fernando; Strzelczak, Stanisław; Villalonga, Alberto; Haber, Rodolfo E.; Kossakowska, Joanna

doi:10.3390/rs11192252

Cited by 51 publications

(38 citation statements)

References 90 publications

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“…Because of the fast growth and the range of interesting applications for IoT devices, more accurate and efficient deep learning models are essential. Moreover, a recent case study [12] especially focused on sensor reliability relating to LiDAR sensors with IoT capabilities. Their work pointed out that such types of sensor devices are becoming widespread.…”

Section: Related Workmentioning

confidence: 99%

IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Lawrence

Zhang

2019

Sensors

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Two main approaches exist when deploying a Convolutional Neural Network (CNN) on resource-constrained IoT devices: either scale a large model down or use a small model designed specifically for resource-constrained environments. Small architectures typically trade accuracy for computational cost by performing convolutions as depth-wise convolutions rather than standard convolutions like in large networks. Large models focus primarily on state-of-the-art performance and often struggle to scale down sufficiently. We propose a new model, namely IoTNet, designed for resource-constrained environments which achieves state-of-the-art performance within the domain of small efficient models. IoTNet trades accuracy with computational cost differently from existing methods by factorizing standard 3 × 3 convolutions into pairs of 1 × 3 and 3 × 1 standard convolutions, rather than performing depth-wise convolutions. We benchmark IoTNet against state-of-the-art efficiency-focused models and scaled-down large architectures on data sets which best match the complexity of problems faced in resource-constrained environments. We compare model accuracy and the number of floating-point operations (FLOPs) performed as a measure of efficiency. We report state-of-the-art accuracy improvement over MobileNetV2 on CIFAR-10 of 13.43% with 39% fewer FLOPs, over ShuffleNet on Street View House Numbers (SVHN) of 6.49% with 31.8% fewer FLOPs and over MobileNet on German Traffic Sign Recognition Benchmark (GTSRB) of 5% with 0.38% fewer FLOPs.

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Section: Related Workmentioning

confidence: 99%

IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Lawrence

Zhang

2019

Sensors

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“…Nowadays, several types of sensors can be used to detect obstacles. For example, in [38,39], light detection and ranging (LiDAR) sensors or stereoscopic cameras are used in obstacle avoidance control of robots. In [37,40], sonar is used as the sensor to detect obstacles for obstacle avoidance control of underwater robots.…”

Section: Obstacle Detection and Calculation Of The Penalty Term For Omentioning

confidence: 99%

Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Yao

Wang

et al. 2020

Sensors

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This paper studies three-dimensional (3D) straight line path following and obstacle avoidance control for an underactuated autonomous underwater vehicle (AUV) without lateral and vertical driving forces. Firstly, the expected angular velocities are designed by using two different methods in the kinematic controller. The first one is a traditional method based on Line-of-sight (LOS) guidance law, and the second one is an improved method based on model predictive control (MPC). At the same time, a penalty item is designed by using the obstacle information detected by onboard sensors, which can realize the real-time obstacle avoidance of the unknown obstacle. Then, in order to overcome the uncertainty of the dynamics model and the saturation of actual control input, the dynamic controller is designed by using sliding mode control (SMC) technology. Finally, in the simulation experiment, the performance of the improved control method is verified by comparison with two traditional control methods based on LOS guidance law. Since the constraint of an AUV’s angular velocities are considered in MPC, simulation results show that the improved control method uses MPC, and SMC not only improves the tracking quality of the AUV when switching paths near the waypoints and realizes real-time obstacle avoidance but also effectively reduces the mean square error (MSE) and saturation rate of the rudder angle. Therefore, this control method is more conducive to the system stability and saves energy.

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“…The data from their study was utilized in current investigation (details of the fluid -Appendix B). The uncertainty in the data and its reliability for scientific analysis is crucial for algorithmic realization of a physical process, and numerous methods were proposed recently [60][61][62] which deal with analyzing the unreliability of the data. In the presented study water test were conducted to evaluate and verify the reliability of instrumentation and data acquisition system, followed by Yield Power Law (YPL) fluids after establishing strong confidence on measurement.…”

Section: Description Of Experimental Datamentioning

confidence: 99%

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

et al. 2020

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Accurate measurement of pressure drop in energy sectors especially oil and gas exploration is a challenging and crucial parameter for optimization of the extraction process. Many empirical and analytical solutions have been developed to anticipate pressure loss for non-Newtonian fluids in concentric and eccentric pipes. Numerous attempts have been made to extend these models to forecast pressure loss in the annulus. However, there remains a void in the experimental and theoretical studies to establish a model capable of estimating it with higher accuracy and lower computation. Rheology of fluid and geometry of system cumulatively dominate the pressure gradient in an annulus. In the present research, the prediction for Herschel-Bulkley fluids is analyzed by Bayesian Neural Network (BNN), random forest (RF), artificial neural network (ANN), and support vector machines (SVM) for pressure loss in the concentric and eccentric annulus. This study emphasizes on the performance evaluation of given algorithms and their pitfalls in predicting accurate pressure drop. The predictions of BNN and RF exhibit the least mean absolute error of 3.2% and 2.57%, respectively, and both can generalize the pressure loss calculation. The impact of each input parameter affecting the pressure drop is quantified using the RF algorithm.

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Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Cited by 51 publications

References 90 publications

IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation

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