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Sensor Data Fusion for IoT and Industrial Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 33187

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Dr. Natividad Duro Carralero

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Department of Computer Sciences and Automatic Control, UNED, C/Juan del Rosal, 16, 28040 Madrid, Spain
Interests: sensor data fusion; industry applications; machine learning; data analysis algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

All devices and systems that we use nowadays have many different sensors. With these new technologies, we can collect all the information obtained by them and use all this data to enable more reliable and accurate decision-making without human intervention. Data fusion is an effective way for the optimum utilization of large volumes of data from multiple sources. This combination of multiple data sources usually yields more relevant, accurate, and useful information than is provided when using an individual data source. Computational intelligence plays a key role in the process of integrating and analyzing this information, since it is essential to the mathematical methods and techniques used for this.

There are some emerging areas that would greatly benefit from sensors data fusion such as Internet of Things (IoT), autonomous vehicles, deep learning for data fusion, smart cities, and many other industrial applications.

Internet of Things (IoT) is the new revolution of the last decade, being one of the most relevant trends in the software industry. The number of objects connected to IoT is growing progressively. The use of IoT implies a fusion between the digital and the physical world, so millions of things or devices of all types and sizes are interconnected between them. This combination of data can be also used in many interesting industrial applications.

This Special Issue encourages authors from academia and industry to submit new research results from the use of multiple sensor data fusion to generate IoT environments or other industrial applications. The Special Issue topics include, but are not limited to the following:

IoT environments using sensor data fusion
Industrial applications using sensor data fusion
Mathematical algorithms for sensor data fusion
Principles and techniques for sensor data fusion
Data preparation techniques for analysis in sensor data fusion

Dr. Natividad Duro Carralero
Guest Editor

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Published Papers (7 papers)

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Research

19 pages, 3831 KiB

Open AccessArticle

Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles

by Vicent Girbés, Daniel Hernández, Leopoldo Armesto, Juan F. Dols and Antonio Sala

Sensors 2019, 19(16), 3515; https://doi.org/10.3390/s19163515 - 11 Aug 2019

Cited by 5 | Viewed by 3622

Abstract

Modelling the dynamic behaviour of heavy vehicles, such as buses or trucks, can be very useful for driving simulation and training, autonomous driving, crash analysis, etc. However, dynamic modelling of a vehicle is a difficult task because there are many subsystems and signals that affect its behaviour. In addition, it might be hard to combine data because available signals come at different rates, or even some samples might be missed due to disturbances or communication issues. In this paper, we propose a non-invasive data acquisition hardware/software setup to carry out several experiments with an urban bus, in order to collect data from one of the internal communication networks and other embedded systems. Subsequently, non-conventional sampling data fusion using a Kalman filter has been implemented to fuse data gathered from different sources, connected through a wireless network (the vehicle’s internal CAN bus messages, IMU, GPS, and other sensors placed in pedals). Our results show that the proposed combination of experimental data gathering and multi-rate filtering algorithm allows useful signal estimation for vehicle identification and modelling, even when data samples are missing. Full article

(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)

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12 pages, 1090 KiB

Open AccessArticle

Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

by Félix Hernández-del-Olmo, Elena Gaudioso, Natividad Duro and Raquel Dormido

Sensors 2019, 19(14), 3139; https://doi.org/10.3390/s19143139 - 17 Jul 2019

Cited by 39 | Viewed by 4543

Abstract

Control of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems. Full article

(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)

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21 pages, 1941 KiB

Open AccessArticle

A Remote Control Strategy for an Autonomous Vehicle with Slow Sensor Using Kalman Filtering and Dual-Rate Control

by Ángel Cuenca, Wei Zhan, Julián Salt, José Alcaina, Chen Tang and Masayoshi Tomizuka

Sensors 2019, 19(13), 2983; https://doi.org/10.3390/s19132983 - 6 Jul 2019

Cited by 13 | Viewed by 4852

Abstract

This work presents a novel remote control solution for an Autonomous Vehicle (AV), where the system structure is split into two sides. Both sides are assumed to be synchronized and linked through a communication network, which introduces time-varying delays and packet disorder. An Extended Kalman Filter (EKF) is used to cope with the non-linearities that appear in the global model of the AV. The EKF fuses the data provided by the sensing devices of the AV in order to estimate the AV state, reducing the noise effect. Additionally, the EKF includes an h-step-ahead state prediction stage, which, together with the consideration of a packet-based control strategy, enables facing the network-induced delays. Since the AV position is provided by a camera, which is a slow sensing device, a dual-rate controller is required to achieve certain desired (nominal) dynamic control performance. The use of a dual-rate control framework additionally enables saving network bandwidth and deals with packet disorder. As the path-tracking control algorithm, pure pursuit is used. Application results show that, despite existing communication problems and slow-rate measurements, the AV is able to track the desired path, keeping the nominal control performance. Full article

(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)

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18 pages, 11893 KiB

Open AccessArticle

A Deep Learning Approach for Fusing Sensor Data from Screw Compressors

by Serafín Alonso, Daniel Pérez, Antonio Morán, Juan José Fuertes, Ignacio Díaz and Manuel Domínguez

Sensors 2019, 19(13), 2868; https://doi.org/10.3390/s19132868 - 28 Jun 2019

Cited by 9 | Viewed by 4830

Abstract

Chillers are commonly used for thermal regulation to maintain indoor comfort in medium and large buildings. However, inefficiencies in this process produce significant losses, and optimization tasks are limited because of accessibility to the system. Data analysis techniques transform measurements coming from several sensors into useful information. Recent deep learning approaches have achieved excellent results in many applications. These techniques can be used for computing new data representations that provide comprehensive information from the device. This allows real-time monitoring, where information can be checked with current working operation to detect any type of anomaly in the process. In this work, a model based on a 1D convolutional neural network is proposed for fusing data in order to predict four different control stages of a screw compressor in a chiller. The evaluation of the method was performed using real data from a chiller in a hospital building. Results show a satisfactory performance and acceptable training time in comparison with other recent methods. In addition, the model is capable of predicting control states of other screw compressors different than the one used in the training. Furthermore, two failure cases are simulated, providing an early alarm detection when a continuous wrong classification is performed by the model. Full article

(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)

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22 pages, 5822 KiB

Open AccessFeature PaperArticle

Simulation Tool for the Analysis of Cooperative Localization Algorithms for Wireless Sensor Networks

by Mario L. Ruz, Juan Garrido, Jorge Jiménez, Reino Virrankoski and Francisco Vázquez

Sensors 2019, 19(13), 2866; https://doi.org/10.3390/s19132866 - 27 Jun 2019

Cited by 8 | Viewed by 4534

Abstract

Within the context of the Internet of Things (IoT) and the Location of Things (LoT) service, this paper presents an interactive tool to quantitatively analyze the performance of cooperative localization techniques for wireless sensor networks (WSNs). In these types of algorithms, nodes help each other determine their location based on some signal metrics such as time of arrival (TOA), received signal strength (RSS), or a fusion of them. The developed tool is intended to provide researchers and designers a fast way to measure the performance of localization algorithms considering specific network topologies. Using TOA or RSS models, the Crámer-Rao lower bound (CRLB) has been implemented within the tool. This lower bound can be used as a benchmark for testing a particular algorithm for specific channel characteristics and WSN topology, which allows determination if the necessary accuracy for a specific application is possible. Furthermore, the tool allows us to consider independent characteristics for each node in the WSN. This feature allows the avoidance of the typical “disk graph model,” which is usually applied to test cooperative localization algorithms. The tool allows us to run Monte-Carlo simulations and generate statistical reports. A set of basic illustrative examples are described comparing the performance of different localization algorithms and showing the capabilities of the presented tool. Full article

(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)

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26 pages, 1447 KiB

Open AccessArticle

ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants

by Ivan Pisa, Ignacio Santín, Jose Lopez Vicario, Antoni Morell and Ramon Vilanova

Sensors 2019, 19(6), 1280; https://doi.org/10.3390/s19061280 - 13 Mar 2019

Cited by 68 | Viewed by 6237

Abstract

Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water’s pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies have been adopted by WWTPs to avoid violating these limits; however, some violations still occur. For that reason, this work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium (

S_{N H}

) and total nitrogen (

S_{N t o t}

S_{N t o t}

is a limiting nutrient and can therefore cause eutrophication, while nitrogen in the

S_{N H}

form is toxic to aquatic life. These parameters are used by control strategies to allow actions to be taken in advance and only when violations are predicted. Since predictions complement control strategies, the evaluation of the ANN-based soft sensor was carried out using the Benchmark Simulation Model N.2. (BSM2) and three different control strategies (from low to high control complexity). Results show that our proposed method is able to predict nitrogen-derived products with good accuracy: the probability of detecting violations of BSM2’s limits is 86–94%. Moreover, the prediction accuracy can be improved by calibrating the soft sensor; for example, perfect prediction of all future violations can be achieved at the expense of increasing the false positive rate. Full article

(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)

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18 pages, 3773 KiB

Open AccessArticle

A Soft–Hard Combination Decision Fusion Scheme for a Clustered Distributed Detection System with Multiple Sensors

by Junhai Luo and Xiaoting He

Sensors 2018, 18(12), 4370; https://doi.org/10.3390/s18124370 - 10 Dec 2018

Cited by 10 | Viewed by 3270

Abstract

In the distributed detection system with multiple sensors, there are two ways for local sensors to deliver their local decisions to the fusion center (FC): a one-bit hard decision and a multiple-bit soft decision. Compared with the soft decision, the hard decision has worse detection performance due to the loss of sensing information but has the main advantage of smaller communication costs. To get a tradeoff between communication costs and detection performance, we propose a soft–hard combination decision fusion scheme for the clustered distributed detection system with multiple sensors and non-ideal communication channels. A clustered distributed detection system is configured by a fuzzy logic system and a fuzzy c-means clustering algorithm. In clusters, each local sensor transmits its local multiple-bit soft decision to its corresponding cluster head (CH) under the non-ideal channel, in which a simple and efficient soft decision fusion method is used. Between clusters, the fusion center combines all cluster heads’ one-bit hard decisions into a final global decision by using an optimal fusion rule. We show that the clustered distributed system with the proposed scheme has a good performance that is close to that of the centralized system, but it consumes much less energy than the centralized system at the same time. In addition, the system with the proposed scheme significantly outperforms the conventional distributed detection system that only uses a hard decision fusion. Using simulation results, we also show that the detection performance increases when more bits are delivered in the soft decision in the distributed detection system. Full article

(This article belongs to the Special Issue Sensor Data Fusion for IoT and Industrial Applications)

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