Research

16 pages, 3178 KiB

Open AccessArticle

Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation

by Seungwon Jung, Jihoon Moon, Sungwoo Park, Seungmin Rho, Sung Wook Baik and Eenjun Hwang

Sensors 2020, 20(6), 1772; https://doi.org/10.3390/s20061772 - 23 Mar 2020

Cited by 36 | Viewed by 3246

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to [...] Read more.

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods. Full article

(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)

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26 pages, 1354 KiB

Open AccessArticle

FDIPP: False Data Injection Prevention Protocol for Smart Grid Distribution Systems

by Hosam Hittini, Atef Abdrabou and Liren Zhang

Sensors 2020, 20(3), 679; https://doi.org/10.3390/s20030679 - 26 Jan 2020

Cited by 9 | Viewed by 3539

Abstract

In this paper, a false data injection prevention protocol (FDIPP) for smart grid distribution systems is proposed. The protocol is designed to work over a novel hierarchical communication network architecture that matches the distribution system hierarchy and its vast number of entities. The [...] Read more.

In this paper, a false data injection prevention protocol (FDIPP) for smart grid distribution systems is proposed. The protocol is designed to work over a novel hierarchical communication network architecture that matches the distribution system hierarchy and its vast number of entities. The proposed protocol guarantees both system and data integrity via preventing packet injection, duplication, alteration, and rogue node access. Therefore, it prevents service disruption or damaging power network assets due to drawing the wrong conclusions about the current operating status of the power grid. Moreover, the impact of the FDIPP protocol on communication network performance is studied using intensive computer simulations. The simulation study shows that the proposed communication architecture is scalable and meets the packet delay requirements of inter-substation communication as mandated by IEC 61850-90-1 with a minimal packet loss while the security overhead of FDIPP is taken into account. Full article

(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)

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20 pages, 3765 KiB

Open AccessArticle

Optimal Energy Resources Allocation Method of Wireless Sensor Networks for Intelligent Railway Systems

by Sheng Bin and Gengxin Sun

Sensors 2020, 20(2), 482; https://doi.org/10.3390/s20020482 - 15 Jan 2020

Cited by 57 | Viewed by 3255

Abstract

The rapid increase of train speed has brought greater challenges to the safety and reliability of railway systems. Therefore, it is necessary to monitor the operation status of trains, infrastructure, and their operating environment in real time. Because the operation environment of railway [...] Read more.

The rapid increase of train speed has brought greater challenges to the safety and reliability of railway systems. Therefore, it is necessary to monitor the operation status of trains, infrastructure, and their operating environment in real time. Because the operation environment of railway systems is complex, the construction cost of wired monitoring systems is high, and it is difficult to achieve full coverage in the operation area of harsh environments, so wireless sensor networks are suitable for the status monitoring of railway systems. Energy resources of nodes are the basis of ensuring the lifecycle of wireless sensor networks, but severely restrict the sustainability of wireless sensor networks. A construction method of special wireless sensor networks for railway status monitoring, and an optimal energy resources allocation method of wireless sensor networks for intelligent railway systems are proposed in this paper. Through cluster head selection and rotating probability model, clustering generation and optimization model, and partial coverage model, the energy consumption of nodes can be minimized and balanced. The result of simulation experiment proved that the lifetime of wireless sensor networks can be maximized by the optimal energy resources allocation method based on clustering optimization and partial coverage model, based on polynomial time algorithm. Full article

(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)

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17 pages, 2491 KiB

Open AccessArticle

Evaluation of Long-Term Performance of a Solar Home System (SHS) Monitoring System on Harsh Environments

by Ascensión López-Vargas, Manuel Fuentes and Marta Vivar

Sensors 2019, 19(24), 5462; https://doi.org/10.3390/s19245462 - 11 Dec 2019

Cited by 2 | Viewed by 2648

Abstract

Dataloggers installed in rural regions of developing countries need to be autonomous, robust, and have good recording capacity as they may be exposed to harsh environmental conditions. An extremely hot, dry, and dusty climate can imply additional wear and tear toequipment. A test [...] Read more.

Dataloggers installed in rural regions of developing countries need to be autonomous, robust, and have good recording capacity as they may be exposed to harsh environmental conditions. An extremely hot, dry, and dusty climate can imply additional wear and tear toequipment. A test procedurewas designed and run in a confined space to control climate conditions to test the datalogger. An outdoor campaign lasting more than three years was performed at the Instituto Madrileño de Estudios Avanzados (IMDEA) Water Institute, in Alcalá de Henares (Madrid, Spain) and at the Escuela Politécnica Superior (EPS) Linares (Jaén, Spain) to test the low-cost datalogger under real conditions. The results demonstrated that it was robust and endured extreme weather conditions. In order to avoid the loss of data, a new version with a redundant system based on an SD card was implemented and tested under real conditions. Full article

(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)

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17 pages, 4811 KiB

Open AccessArticle

Prediction of Motor Failure Time Using An Artificial Neural Network

by Gustavo Scalabrini Sampaio, Arnaldo Rabello de Aguiar Vallim Filho, Leilton Santos da Silva and Leandro Augusto da Silva

Sensors 2019, 19(19), 4342; https://doi.org/10.3390/s19194342 - 08 Oct 2019

Cited by 40 | Viewed by 12144

Abstract

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this [...] Read more.

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries. Full article

(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)

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16 pages, 10827 KiB

Open AccessArticle

A Further Exploration of Multi-Slot Based Spectrum Sensing

by Jia Zhu, Hongsong Cao and Junsheng Mu

Sensors 2019, 19(16), 3497; https://doi.org/10.3390/s19163497 - 09 Aug 2019

Cited by 1 | Viewed by 2274

Abstract

Spectrum sensing (SS) exhibits its advantages in the era of Internet of Things (IoT) due to limited spectrum resource and a lower utilization rate of authorized spectrum. In consequence, the performance improvement of SS seems a matter of great significance for the development [...] Read more.

Spectrum sensing (SS) exhibits its advantages in the era of Internet of Things (IoT) due to limited spectrum resource and a lower utilization rate of authorized spectrum. In consequence, the performance improvement of SS seems a matter of great significance for the development of wireless communication and IoT. Motivated by this, this paper is devoted to multi-slot based SS in specialty and several important conclusions are drawn. Firstly, SS with one slot outperforms those with multiple slots if decision fusion rule is considered for multi-slot based SS. Secondly, multi-slot based SS is conducive to the performance improvement of SS when instantaneous strong noise occurs in the radio environment. Thirdly, for multi-slot based cooperative spectrum sensing (CSS), majority voting rule among multiple nodes obtains the optimal sensing performance. Both theoretical analysis and simulation experiment validate the conclusions drawn in this paper. Full article

(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)

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28 pages, 1451 KiB

Open AccessArticle

Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview

by Wesllen Sousa Lima, Eduardo Souto, Khalil El-Khatib, Roozbeh Jalali and Joao Gama

Sensors 2019, 19(14), 3213; https://doi.org/10.3390/s19143213 - 21 Jul 2019

Cited by 165 | Viewed by 12243

Abstract

The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial [...] Read more.

The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones. Full article

(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)

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23 pages, 9058 KiB

Open AccessArticle

Identification and Statistical Analysis of Impulse-Like Patterns of Carbon Monoxide Variation in Deep Underground Mines Associated with the Blasting Procedure

by Justyna Hebda-Sobkowicz, Sebastian Gola, Radosław Zimroz and Agnieszka Wyłomańska

Sensors 2019, 19(12), 2757; https://doi.org/10.3390/s19122757 - 19 Jun 2019

Cited by 21 | Viewed by 3687

Abstract

The quality of the air in underground mines is a challenging issue due to many factors, such as technological processes related to the work of miners (blasting, air conditioning, and ventilation), gas release by the rock mass and geometry of mine corridors. However, [...] Read more.

The quality of the air in underground mines is a challenging issue due to many factors, such as technological processes related to the work of miners (blasting, air conditioning, and ventilation), gas release by the rock mass and geometry of mine corridors. However, to allow miners to start their work, it is crucial to determine the quality of the air. One of the most critical parameters of the air quality is the carbon monoxide (CO) concentration. Thus, in this paper, we analyze the time series describing CO concentration. Firstly, the signal segmentation is proposed, then segmented data (daily patterns) is visualized and statistically analyzed. The method for blasting moment localization, with no prior knowledge, has been presented. It has been found that daily patterns differ and CO concentration values reach a safe level at a different time after blasting. The waiting time to achieve the safe level after blasting moment (with a certain probability) has been calculated based on the historical data. The knowledge about the nature of the CO variability and sources of a high CO concentration can be helpful in creating forecasting models, as well as while planning mining activities. Full article

(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)

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16 pages, 1402 KiB

Open AccessArticle

A Secure Mutual Batch Authentication Scheme for Patient Data Privacy Preserving in WBAN

by Martin Konan and Wenyong Wang

Sensors 2019, 19(7), 1608; https://doi.org/10.3390/s19071608 - 03 Apr 2019

Cited by 18 | Viewed by 3324

Abstract

The current advances in cloud-based services have significantly enhanced individual satisfaction in numerous modern life areas. Particularly, the recent spectacular innovations in the wireless body area networks (WBAN) domain have made e-Care services rise as a promising application field, which definitely improves the [...] Read more.

The current advances in cloud-based services have significantly enhanced individual satisfaction in numerous modern life areas. Particularly, the recent spectacular innovations in the wireless body area networks (WBAN) domain have made e-Care services rise as a promising application field, which definitely improves the quality of the medical system. However, the forwarded data from the limited connectivity range of WBAN via a smart device (e.g., smartphone) to the application provider (AP) should be secured from an unapproved access and alteration (attacker) that could prompt catastrophic consequences. Therefore, several schemes have been proposed to guarantee data integrity and privacy during their transmission between the client/controller (C) and the AP. Thereby, numerous effective cryptosystem solutions based on a bilinear pairing approach are available in the literature to address the mentioned security issues. Unfortunately, the related solution presents security shortcomings, where AP can with ease impersonate a given C. Hence, this existing scheme cannot fully guarantee C’s data privacy and integrity. Therefore, we propose our contribution to address this data security issue (impersonation) through a secured and efficient remote batch authentication scheme that genuinely ascertains the identity of C and AP. Practically, the proposed cryptosystem is based on an efficient combination of elliptical curve cryptography (ECC) and bilinear pairing schemes. Furthermore, our proposed solution reduces the communication and computational costs by providing an efficient data aggregation and batch authentication for limited device’s resources in WBAN. These additional features (data aggregation and batch authentication) are the core improvements of our scheme that have great merit for limited energy environments like WBAN. Full article

(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)

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