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Special Issue "Intelligent Systems in Sensor Networks and Internet of Things"

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

Deadline for manuscript submissions: closed (30 March 2020).

Special Issue Editors

Prof. Dr. Francesco Palmieri
Website
Guest Editor
Department of Computer Science, University of Salerno, Via Giovanni Paolo II 132, I-84084, Fisciano (SA), Italy
Interests: High performance networking protocols and architectures, routing algorithms, network security
Special Issues and Collections in MDPI journals
Dr. Gianni D’Angelo
Website
Guest Editor
Department of Computer Science, University of Salerno,Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy
Interests: Soft Computing algorithms; Data Mining and Machine Learning; Deep Learning; Knowledge Discovery; Optimization Problems; Pervasive Computing; Trustworthiness modeling; High Performance Machines, Parallel Computing, Big data analytics
Special Issues and Collections in MDPI journals
Prof. Dr. Chang Choi
Website
Guest Editor
Computer Engineering, Gachon University, Sungnam, Korea
Interests: Intelligent Information Processing; Information Security; Smart Sensor Networks
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, the rapid developments in hardware, software, and communication technologies have facilitated the spread of sensors, actuators and heterogeneous devices connected via the Internet (referred to as the Internet of Things, IoT), which collect and exchange huge amounts of data to offer a new class of advanced services characterized by being available anywhere, at any time and to anyone. Nevertheless, without intelligence, the IoT systems and, in general, sensor networks (SN) can act only as ordinary information systems based on predefined rules. On the contrary, adding artificial intelligence (AI) to the mix may allow services to be provided according to users’ habits, activities, and real-world contexts. Combining AI with the IoT opens the world to unlimited technological potential.

The intelligent processing of IoT data, and the building of intelligent systems able to make autonomous decisions are the keys to developing smart IoT applications and services. The combination of different scientific fields that uses data mining (DM), machine learning (ML), and other AI techniques have proven to be effective in exploring and handling the huge amount of data generated by IoT systems and SNs. In addition, other intelligences based on heuristic approaches, such as simulated annealing, genetic algorithms, evolutionary algorithms, ant colony optimization, and particle swarm optimization, have also proven to be effective in making the IoT systems and SNs aware of events and contexts, especially when dealing with large amounts of incomplete or inconsistent data.

The central theme of this Special Issue is to investigate novel methodologies, theories, systems, and applications for the creation of such intelligence in IoT systems and SNs.

Topics of Interest:

This Special Issue aims to present the most important and relevant advances in creating high-performance and intelligent IoT systems and sensor networks.

We seek original and high quality submissions related to, but not limited to, one or more of the following topics:

  • Data Mining for IoT and Sensor Networks
  • Intelligent Data Analysis in IoT and Sensor Networks
  • Intelligent Real-Time Data Processing of IoT and Sensor Networks
  • Soft Computing Applications for IoT and Sensor Networks
  • Machine-Learning and Artificial Intelligence for IoT and Sensors Networks
  • Nature-Inspired Evolutionary Algorithms and Systems for Pattern Recognition, Data Analysis, and Modeling in IoT and Sensor Networks
  • Heuristic Algorithms for IoT and Sensor Networks
  • Pattern Recognition and Classification for Multivariate Time Series
  • Intelligent Network Technologies for IoT and Sensor Networks
  • Intelligent Applications of IoT and Sensor Networks
  • Intelligent Decision-Making in IoT and Sensor Networks
  • Algorithms and Optimization Technologies for IoT and Sensor Network Scenarios
  • Adaptive Heterogeneous Sensor Networks
  • Cognitive Applications and Intelligence in IoT and Sensor Networks
  • Intelligent Intrusion Detection Techniques for IoT and Sensor Networks
  • Disaster Recovery for IoT and Sensor Networks
  • Learning from Data Streams in IoT and Sensor Networks
  • Adaptive Quality of Service (QoS) Provisioning in IoT and Sensor Networks
  • Deep Learning-Based Solutions for IoT Systems and Sensor Networks
  • Applications and Use-Cases of Smart IoT Systems, and Real-Time Sensor Networks
  • Distributed Computing Frameworks for IoT and Sensor Networks
  • Tools and Frameworks for Designing, Deploying and Maintaining Intelligent IoT Infrastructure

Prof. Dr. Francesco Palmieri
Dr. Gianni D’Angelo
Dr. Chang Choi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (15 papers)

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Editorial

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Open AccessEditorial
Special Issue on Intelligent Systems in Sensor Networks and Internet of Things
Sensors 2020, 20(11), 3182; https://doi.org/10.3390/s20113182 - 03 Jun 2020
Abstract
This Special Issue aims at collecting several original state-of-the-art research experiences in the area of intelligent applications in the IoT and Sensor networks environment, by analyzing several open issues and perspectives associated with such scenarios, in order to explore novel potentialities and solutions [...] Read more.
This Special Issue aims at collecting several original state-of-the-art research experiences in the area of intelligent applications in the IoT and Sensor networks environment, by analyzing several open issues and perspectives associated with such scenarios, in order to explore novel potentialities and solutions and face with the emerging challenges. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)

Research

Jump to: Editorial

Open AccessArticle
CoDR: Correlation-Based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data
Sensors 2020, 20(6), 1677; https://doi.org/10.3390/s20061677 - 17 Mar 2020
Cited by 1
Abstract
A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily [...] Read more.
A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduce data volume by considering the correlations among heterogeneous driving data. Among heterogeneous datasets, CoDR first chooses one set as a pivot data. Then, according to the objective of data collection, it identifies data ranges relevant to the objective from the pivot dataset. Finally, it investigates correlations among sets, and reduces data volume by eliminating irrelevant data from not only the pivot set but also other remaining datasets. CoDR gathers four heterogeneous driving datasets: two videos for front view and driver behavior, OBD-II and GPS data. We show that CoDR decreases data volume by up to 91%. We also present diverse analytical results that reveal the correlations among the four datasets, which can be exploited usefully for edge computing to reduce data volume on the spot. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Accurate and Practical Energy Detection over α-μ Fading Channels
Sensors 2020, 20(3), 754; https://doi.org/10.3390/s20030754 - 29 Jan 2020
Cited by 2
Abstract
In this study, a novel and exact closed-form expression for detection probability of energy detection (ED) in terms of Meijer’s G-function over α-μ generalized fading channels was derived. It is more accurate and practical than the existing exact expressions and has wide application [...] Read more.
In this study, a novel and exact closed-form expression for detection probability of energy detection (ED) in terms of Meijer’s G-function over α-μ generalized fading channels was derived. It is more accurate and practical than the existing exact expressions and has wide application prospects in the performance evaluations in various areas of wireless communications, especially in the wireless sensor network (WSN) and the cognitive radio network (CRN). Furthermore, an exact and simple analytical solution for the sample size meeting the desired detection performance in terms of the probability mass function of a Poisson distribution was also solved. Simulations verified the detection performance and accuracy of our derived expressions with a small sample size compared to the existing exact expressions and approximations. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection
Sensors 2020, 20(1), 137; https://doi.org/10.3390/s20010137 - 24 Dec 2019
Cited by 7
Abstract
As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of [...] Read more.
As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images
Sensors 2019, 19(19), 4244; https://doi.org/10.3390/s19194244 - 29 Sep 2019
Cited by 7
Abstract
Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, [...] Read more.
Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
An Adaptation Multi-Group Quasi-Affine Transformation Evolutionary Algorithm for Global Optimization and Its Application in Node Localization in Wireless Sensor Networks
Sensors 2019, 19(19), 4112; https://doi.org/10.3390/s19194112 - 23 Sep 2019
Cited by 8
Abstract
Developing metaheuristic algorithms has been paid more recent attention from researchers and scholars to address the optimization problems in many fields of studies. This paper proposes a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm for global optimization. Enhanced population diversity for [...] Read more.
Developing metaheuristic algorithms has been paid more recent attention from researchers and scholars to address the optimization problems in many fields of studies. This paper proposes a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm for global optimization. Enhanced population diversity for adaptation multi-group quasi-affine transformation evolutionary algorithm is implemented by randomly dividing its population into three groups. Each group adopts a mutation strategy differently for improving the efficiency of the algorithm. The scale factor F of mutations is updated adaptively during the search process with the different policies along with proper parameter to make a better trade-off between exploration and exploitation capability. In the experimental section, the CEC2013 test suite and the node localization in wireless sensor networks were used to verify the performance of the proposed algorithm. The experimental results are compared results with three quasi-affine transformation evolutionary algorithm variants, two different evolution variants, and two particle swarm optimization variants show that the proposed adaptation multi-group quasi-affine transformation evolutionary algorithm outperforms the competition algorithms. Moreover, analyzed results of the applied adaptation multi-group quasi-affine transformation evolutionary for node localization in wireless sensor networks showed that the proposed method produces higher localization accuracy than the other competing algorithms. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Seasonal Time Series Forecasting by F1-Fuzzy Transform
Sensors 2019, 19(16), 3611; https://doi.org/10.3390/s19163611 - 19 Aug 2019
Cited by 1
Abstract
We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The [...] Read more.
We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series’ trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System
Sensors 2019, 19(15), 3423; https://doi.org/10.3390/s19153423 - 04 Aug 2019
Cited by 2
Abstract
As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge [...] Read more.
As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things
Sensors 2019, 19(12), 2804; https://doi.org/10.3390/s19122804 - 22 Jun 2019
Cited by 3
Abstract
In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors [...] Read more.
In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors and monitor the working conditions of such sensors and of the overall environment, as well as detect unexpected system events in a timely and accurate manner. Monitoring large amounts of unstructured data generated by IoT devices and collected by the big-data analytics systems is a challenging task. Furthermore, detecting anomalies within the vast amount of data collected in real time by a centralized monitoring system is an even bigger challenge. In the context of the industrial use of the IoT, solutions for monitoring anomalies in distributed data flow need to be explored. In this paper, a low-power distributed data flow anomaly-monitoring model (LP-DDAM) is proposed to mitigate the communication overhead problem. As the data flow monitoring system is only interested in anomalies, which are rare, and the relationship among objects in terms of the size of their attribute values remains stable within any specific period of time, LP-DDAM integrates multiple objects as a complete set for processing, makes full use of the relationship among the objects, selects only one “representative” object for continuous monitoring, establishes certain constraints to ensure correctness, and reduces communication overheads by maintaining the overheads of constraints in exchange for a reduction in the number of monitored objects. Experiments on real data sets show that LP-DDAM can reduce communication overheads by approximately 70% when compared to an equivalent method that continuously monitors all objects under the same conditions. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
A Practical Neighbor Discovery Framework for Wireless Sensor Networks
Sensors 2019, 19(8), 1887; https://doi.org/10.3390/s19081887 - 20 Apr 2019
Cited by 3
Abstract
Neighbor discovery is a crucial operation frequently executed throughout the life cycle of a Wireless Sensor Network (WSN). Various protocols have been proposed to minimize the discovery latency or to prolong the lifetime of sensors. However, none of them have addressed that all [...] Read more.
Neighbor discovery is a crucial operation frequently executed throughout the life cycle of a Wireless Sensor Network (WSN). Various protocols have been proposed to minimize the discovery latency or to prolong the lifetime of sensors. However, none of them have addressed that all the critical concerns stemming from real WSNs, including communication collisions, latency constraints and energy consumption limitations. In this paper, we propose Spear, the first practical neighbor discovery framework to meet all these requirements. Spear offers two new methods to reduce communication collisions, thus boosting the discovery rate of existing neighbor discovery protocols. Spear also takes into consideration latency constraints and facilitates timely adjustments in order to reduce the discovery latency. Spear offers two practical energy management methods that evidently prolong the lifetime of sensor nodes. Most importantly, Spear automatically improves the discovery results of existing discovery protocols, on which no modification is required. Beyond reporting details of different Spear modules, we also present experiment evaluations on several notable neighbor discovery protocols. Results show that Spear greatly improves the discovery rate from 33.0% to 99.2%, and prolongs the sensor nodes lifetime up to 6.47 times. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks
Sensors 2019, 19(5), 1215; https://doi.org/10.3390/s19051215 - 10 Mar 2019
Cited by 11
Abstract
With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization [...] Read more.
With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
Sensors 2019, 19(5), 1040; https://doi.org/10.3390/s19051040 - 28 Feb 2019
Cited by 5
Abstract
In wireless sensor networks, clustering routing algorithms have been widely used owing to their high energy-efficiency and scalability. In clustering schemes, the nodes are organized in the form of clusters, and each cluster is governed by a cluster head. Once the cluster heads [...] Read more.
In wireless sensor networks, clustering routing algorithms have been widely used owing to their high energy-efficiency and scalability. In clustering schemes, the nodes are organized in the form of clusters, and each cluster is governed by a cluster head. Once the cluster heads are selected, they form a backbone network to periodically collect, aggregate, and forward data to the base station using minimum energy (cost) routing. This approach significantly improves the network lifetime. Therefore, a new cluster head selection method that uses a weighted sum method to calculate the weight of each node in the cluster and compare it with the standard weight of that particular cluster is proposed in this paper. The node with a weight closest to the standard cluster weight becomes the cluster head. This technique balances the load distribution and selects the nodes with highest residual energy in the network. Additionally, a data routing scheme is proposed to determine an energy-efficient path from the source to the destination node. This algorithm assigns a weight function to each link on the basis of a fuzzy membership function and intra-cluster communication cost within a cluster. As a result, a minimum weight path is selected using Dijkstra’s algorithm that improves the energy efficiency of the overall system. The experimental results show that the proposed algorithm shows better performance than some existing representative methods in the aspects of energy consumption, network lifetime, and system throughput. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics
Sensors 2019, 19(4), 935; https://doi.org/10.3390/s19040935 - 22 Feb 2019
Cited by 11
Abstract
This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been [...] Read more.
This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%—0.27% higher than AlexNet or GoogLeNet on PTB-ECG—and the ResNet was 0.94%—0.12% higher than AlexNet or GoogLeNet on CU-ECG. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
An Intelligent Driver Training System Based on Real Cars
Sensors 2019, 19(3), 630; https://doi.org/10.3390/s19030630 - 02 Feb 2019
Cited by 2
Abstract
In driver training, the correct observation of the trainees’ operation is the key to ensure the training quality. The operation of the vehicle can be expressed by the vehicle state changes. This paper proposes a driver training model based on a multiple-embedded-sensor net. [...] Read more.
In driver training, the correct observation of the trainees’ operation is the key to ensure the training quality. The operation of the vehicle can be expressed by the vehicle state changes. This paper proposes a driver training model based on a multiple-embedded-sensor net. Six vehicle state parameters are identified as the critical features of the reverse parking machine learning model and represented quantitatively. A multiple-embedded-sensor net-based system mounted on a real vehicle is developed to collect the actual data of the six critical features. The data collected at the same time are bound together and encapsulated into a vector and sequenced by time with a label given by the multiple-embedded-sensor net. All vectors are evaluated by subjective assessment conclusions from experienced driving instructors and the positive ones are used as the training data of the model. The trained model can remind the driver of the next correct operation during training, and can also analyze the improvements after the training. The model has achieved good results in practical application. The experiments prove the validity and reliability of the proposed driver training model. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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Open AccessArticle
Wireless Network for Assessing Temperature Load of Large-Scale Structures Under Fire Hazards
Sensors 2019, 19(1), 65; https://doi.org/10.3390/s19010065 - 25 Dec 2018
Cited by 2
Abstract
While the construction of high-rise buildings has become popular in big cities, an average of over 15,000 structure fires in those buildings are being reported in the United States. Especially because the fire in a building can result in a failure or even [...] Read more.
While the construction of high-rise buildings has become popular in big cities, an average of over 15,000 structure fires in those buildings are being reported in the United States. Especially because the fire in a building can result in a failure or even the collapse of the structure, assessing its integrity during and after the fire is of importance. Thus, in this paper, a framework with temperature sensors using wireless communication technology has been proposed. Associated hardware and software are carefully chosen and developed to provide an easy and effective solution for measuring fire load on large-scale structures during a fire. With an autonomous measurement system enabled, the key functions of the framework have been validated in a fire testing laboratory, using a real-scale steel column subject to standard fire. Unlike existing solutions of wireless temperature networks, the proposed solution can provide the user definable sampling frequencies based on the surface temperature and the means to assess the load redistribution of the structure due to fire loading in real-time. The results of the study show the great potential of using the developed framework for monitoring fire in a structure, allowing more accurate estimations of fire load in the design criteria, and advancing fire safety engineering. Full article
(This article belongs to the Special Issue Intelligent Systems in Sensor Networks and Internet of Things)
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