Special Issue "Applications of Internet of Things"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 December 2016).

Special Issue Editors

Guest Editor
Prof. Dr. Chi-Hua Chen Website E-Mail
College of Mathematics and Computer Science, Fuzhou University, Fuzhou City, Fujian Province, China
Interests: deep learning; big data; Internet of Things; cellular networks
Guest Editor
Dr. Kuen-Rong Lo E-Mail
Telecommunication Laboratories, Chunghwa Telecom Co. Ltd., Yangmei District, Taoyuan City 32661, Taiwan
Interests: internet of things; smart city; intelligent transportation system

Special Issue Information

Dear Colleagues,

In recent years, the techniques of Internet of Things (IoT) and mobile communication have been developed to detect human and environment information (e.g., geo-information, weather information, bio-information, human behaviours, etc.) for a variety of intelligent services and applications. The three layers in IoT are sensor, networking, and application layers. For sensor and networking layers, the rise of mobile technology advancements (e.g., wireless sensor networking, Wi-Fi, Bluetooth, smart mobile device, and Long Term Evolution (LTE)) has led to a new wave of machine-to-machine (M2M), machine-to-human (M2H), human-to-human (H2H), and human-to-machine (H2M) communications. For the application layer, several IoT applications, which include energy, enterprise, healthcare, public services, residency, retail, and transportation, have been designed and implemented to detect environmental changes and send instant updates to a cloud computing server farm via mobile communications and middleware for big geo-data analyses. For instance, on-board units in cars can instantly detect and share information about the geolocation of the car, speed, following distance, and gaps with other neighboring cars. While the area of IoT applications and mobile communication is a rapidly expanding field of scientific research, several open research questions still need to be discussed and studied. This Special Issue will solicit papers on various disciplines of IoT. Potential topics include, but are not limited to:

  • IoT Applications of Agriculture
  • IoT Applications of Energy
  • IoT Applications of Enterprise
  • IoT Applications of Finance
  • IoT Applications of Healthcare
  • IoT Applications of Industry
  • IoT Applications of Public Services
  • IoT Applications of Residency
  • IoT Applications of Retail
  • IoT Applications of Transportation
  • Sensing Techniques for IoT
  • Communication Techniques for IoT
  • Middleware Techniques for IoT
  • Data Analysis Techniques for IoT

Dr. Chi-Hua Chen
Dr. Kuen-Rong Lo
Guest Editor

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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1000 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 (8 papers)

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Research

Open AccessArticle
Efficient Location Privacy-Preserving k-Anonymity Method Based on the Credible Chain
ISPRS Int. J. Geo-Inf. 2017, 6(6), 163; https://doi.org/10.3390/ijgi6060163 - 01 Jun 2017
Cited by 2
Abstract
Currently, although prevalent location privacy methods based on k-anonymizing spatial regions (K-ASRs) can achieve privacy protection by sacrificing the quality of service (QoS), users cannot obtain accurate query results. To address this problem, it proposes a new location privacy-preserving k-anonymity method [...] Read more.
Currently, although prevalent location privacy methods based on k-anonymizing spatial regions (K-ASRs) can achieve privacy protection by sacrificing the quality of service (QoS), users cannot obtain accurate query results. To address this problem, it proposes a new location privacy-preserving k-anonymity method based on the credible chain with two major features. First, the optimal k value for the current user is determined according to the user’s environment and social attributes. Second, rather than forming an anonymizing spatial region (ASR), the trusted third party (TTP) generates a fake trajectory that contains k location nodes based on properties of the credible chain. In addition, location-based services (LBS) queries are conducted based on the trajectory, and privacy level is evaluated by instancing θ privacy. Simulation results and experimental analysis demonstrate the effectiveness and availability of the proposed method. Compared with methods based on ASR, the proposed method guarantees 100% QoS. Full article
(This article belongs to the Special Issue Applications of Internet of Things)
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Open AccessArticle
Camera Coverage Estimation Based on Multistage Grid Subdivision
ISPRS Int. J. Geo-Inf. 2017, 6(4), 110; https://doi.org/10.3390/ijgi6040110 - 05 Apr 2017
Cited by 4
Abstract
Visual coverage is one of the most important quality indexes for depicting the usability of an individual camera or camera network. It is the basis for camera network deployment, placement, coverage-enhancement, planning, etc. Precision and efficiency are critical influences on applications, especially those [...] Read more.
Visual coverage is one of the most important quality indexes for depicting the usability of an individual camera or camera network. It is the basis for camera network deployment, placement, coverage-enhancement, planning, etc. Precision and efficiency are critical influences on applications, especially those involving several cameras. This paper proposes a new method to efficiently estimate superior camera coverage. First, the geographic area that is covered by the camera and its minimum bounding rectangle (MBR) without considering obstacles is computed using the camera parameters. Second, the MBR is divided into grids using the initial grid size. The status of the four corners of each grid is estimated by a line of sight (LOS) algorithm. If the camera, considering obstacles, covers a corner, the status is represented by 1, otherwise by 0. Consequently, the status of a grid can be represented by a code that is a combination of 0s or 1s. If the code is not homogeneous (not four 0s or four 1s), the grid will be divided into four sub-grids until the sub-grids are divided into a specific maximum level or their codes are homogeneous. Finally, after performing the process above, total camera coverage is estimated according to the size and status of all grids. Experimental results illustrate that the proposed method’s accuracy is determined by the method that divided the coverage area into the smallest grids at the maximum level, while its efficacy is closer to the method that divided the coverage area into the initial grids. It considers both efficiency and accuracy. The initial grid size and maximum level are two critical influences on the proposed method, which can be determined by weighing efficiency and accuracy. Full article
(This article belongs to the Special Issue Applications of Internet of Things)
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Open AccessArticle
Detection of Electronic Anklet Wearers’ Groupings throughout Telematics Monitoring
ISPRS Int. J. Geo-Inf. 2017, 6(1), 31; https://doi.org/10.3390/ijgi6010031 - 22 Jan 2017
Cited by 2
Abstract
Ankle bracelets (anklets) imposed by law to track convicted individuals are being used in many countries as an alternative to overloaded prisons. There are many different systems for monitoring individuals wearing such devices, and these electronic anklet monitoring systems commonly detect violations of [...] Read more.
Ankle bracelets (anklets) imposed by law to track convicted individuals are being used in many countries as an alternative to overloaded prisons. There are many different systems for monitoring individuals wearing such devices, and these electronic anklet monitoring systems commonly detect violations of circulation areas permitted to holders. In spite of being able to monitor individual localization, such systems do not identify grouping activities of the monitored individuals, although this kind of event could represent a real risk of further offenses planned by those individuals. In order to address such a problem and to help monitoring systems to be able to have a proactive approach, this paper proposes sensor data fusion algorithms that are able to identify such groups based on data provided by anklet positioning devices. The results from the proposed algorithms can be applied to support risk assessment in the context of monitoring systems. The processing is performed using geographic points collected by a monitoring center, and as result, it produces a history of groups with their members, timestamps, locations and frequency of meetings. The proposed algorithms are validated in various serial and parallel computing scenarios, and the correspondent results are presented and discussed. The information produced by the proposed algorithms yields to a better characterization of the monitored individuals and can be adapted to support decision-making systems used by authorities that are responsible for planning decisions regarding actions affecting public security. Full article
(This article belongs to the Special Issue Applications of Internet of Things)
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Open AccessArticle
Smartphone-Based Pedestrian’s Avoidance Behavior Recognition towards Opportunistic Road Anomaly Detection
ISPRS Int. J. Geo-Inf. 2016, 5(10), 182; https://doi.org/10.3390/ijgi5100182 - 03 Oct 2016
Cited by 3
Abstract
Road anomalies, such as cracks, pits and puddles, have generally been identified by citizen reports made by e-mail or telephone; however, it is difficult for administrative entities to locate the anomaly for repair. An advanced smartphone-based solution that sends text and/or image reports [...] Read more.
Road anomalies, such as cracks, pits and puddles, have generally been identified by citizen reports made by e-mail or telephone; however, it is difficult for administrative entities to locate the anomaly for repair. An advanced smartphone-based solution that sends text and/or image reports with location information is not a long-lasting solution, because it depends on people’s active reporting. In this article, we show an opportunistic sensing-based system that uses a smartphone for road anomaly detection without any active user involvement. To detect road anomalies, we focus on pedestrians’ avoidance behaviors, which are characterized by changing azimuth patterns. Three typical avoidance behaviors are defined, and random forest is chosen as the classifier. Twenty-nine features are defined, in which features calculated by splitting a segment into the first half and the second half and considering the monotonicity of change were proven to be effective in recognition. Experiments were carried out under an ideal and controlled environment. Ten-fold cross-validation shows an average classification performance with an F-measure of 0.89 for six activities. The proposed recognition method was proven to be robust against the size of obstacles, and the dependency on the storing position of a smartphone can be handled by an appropriate classifier per storing position. Furthermore, an analysis implies that the classification of data from an “unknown” person can be improved by taking into account the compatibility of a classifier. Full article
(This article belongs to the Special Issue Applications of Internet of Things)
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Open AccessArticle
Vehicle Positioning and Speed Estimation Based on Cellular Network Signals for Urban Roads
ISPRS Int. J. Geo-Inf. 2016, 5(10), 181; https://doi.org/10.3390/ijgi5100181 - 02 Oct 2016
Cited by 2
Abstract
In recent years, cellular floating vehicle data (CFVD) has been a popular traffic information estimation technique to analyze cellular network data and to provide real-time traffic information with higher coverage and lower cost. Therefore, this study proposes vehicle positioning and speed estimation methods [...] Read more.
In recent years, cellular floating vehicle data (CFVD) has been a popular traffic information estimation technique to analyze cellular network data and to provide real-time traffic information with higher coverage and lower cost. Therefore, this study proposes vehicle positioning and speed estimation methods to capture CFVD and to track mobile stations (MS) for intelligent transportation systems (ITS). Three features of CFVD, which include the IDs, sequence, and cell dwell time of connected cells from the signals of MS communication, are extracted and analyzed. The feature of sequence can be used to judge urban road direction, and the feature of cell dwell time can be applied to discriminate proximal urban roads. The experiment results show the accuracy of the proposed vehicle positioning method, which is 100% better than other popular machine learning methods (e.g., naive Bayes classification, decision tree, support vector machine, and back-propagation neural network). Furthermore, the accuracy of the proposed method with all features (i.e., the IDs, sequence, and cell dwell time of connected cells) is 83.81% for speed estimation. Therefore, the proposed methods based on CFVD are suitable for detecting the status of urban road traffic. Full article
(This article belongs to the Special Issue Applications of Internet of Things)
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Open AccessArticle
Proximity-Based Asynchronous Messaging Platform for Location-Based Internet of Things Service
ISPRS Int. J. Geo-Inf. 2016, 5(7), 116; https://doi.org/10.3390/ijgi5070116 - 14 Jul 2016
Cited by 4
Abstract
The Internet of Things (IoT) opens up tremendous opportunities to provide location-based applications. However, despite the services around a user being physically adjacent, common IoT platforms use a centralized structure, like a cloud-computing architecture, which transfers large amounts of data to a central [...] Read more.
The Internet of Things (IoT) opens up tremendous opportunities to provide location-based applications. However, despite the services around a user being physically adjacent, common IoT platforms use a centralized structure, like a cloud-computing architecture, which transfers large amounts of data to a central server. This raises problems, such as traffic concentration, long service latency, and high communication cost. In this paper, we propose a physical distance-based asynchronous messaging platform that specializes in processing personalized data and location-based messages. The proposed system disperses traffic using a location-based message-delivery protocol, and has high stability. Full article
(This article belongs to the Special Issue Applications of Internet of Things)
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Open AccessArticle
A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
ISPRS Int. J. Geo-Inf. 2016, 5(6), 82; https://doi.org/10.3390/ijgi5060082 - 02 Jun 2016
Cited by 5
Abstract
Commercial vehicle operation (CVO) has been a popular application of intelligent transportation systems. Location determination and route tracing of an on-board unit (OBU) in a vehicle is an important capability for CVO. However, large location errors from global positioning system (GPS) receivers may [...] Read more.
Commercial vehicle operation (CVO) has been a popular application of intelligent transportation systems. Location determination and route tracing of an on-board unit (OBU) in a vehicle is an important capability for CVO. However, large location errors from global positioning system (GPS) receivers may occur in cities that shield GPS signals. Therefore, a highly efficient mobile positioning method is proposed based on the collection and analysis of the cellular network signals of CVO data. Parallel- and cloud-computing techniques are designed into the proposed method to quickly determine the location of an OBU for CVO. Furthermore, this study proposes analytical models to analyze the availability of the proposed mobile positioning method with various outlier filtering criteria. Experimentally, a CVO system was designed and implemented to collect CVO data from Chunghwa Telecom vehicles and to analyze the cellular network signals of CVO data for location determination. A case study found that the average errors of location determination using the proposed method vs. using the traditional cell-ID-based location method were 163.7 m and 521.2 m, respectively. Furthermore, the practical results show that the average location error and availability of using the proposed method are better than using GPS or the cell-ID-based location method for each road type, particularly urban roads. Therefore, this approach is feasible to determine OBU locations for improving CVO. Full article
(This article belongs to the Special Issue Applications of Internet of Things)
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Open AccessArticle
A Method for Traffic Congestion Clustering Judgment Based on Grey Relational Analysis
ISPRS Int. J. Geo-Inf. 2016, 5(5), 71; https://doi.org/10.3390/ijgi5050071 - 18 May 2016
Cited by 18
Abstract
Traffic congestion clustering judgment is a fundamental problem in the study of traffic jam warning. However, it is not satisfactory to judge traffic congestion degrees using only vehicle speed. In this paper, we collect traffic flow information with three properties (traffic flow velocity, [...] Read more.
Traffic congestion clustering judgment is a fundamental problem in the study of traffic jam warning. However, it is not satisfactory to judge traffic congestion degrees using only vehicle speed. In this paper, we collect traffic flow information with three properties (traffic flow velocity, traffic flow density and traffic volume) of urban trunk roads, which is used to judge the traffic congestion degree. We first define a grey relational clustering model by leveraging grey relational analysis and rough set theory to mine relationships of multidimensional-attribute information. Then, we propose a grey relational membership degree rank clustering algorithm (GMRC) to discriminant clustering priority and further analyze the urban traffic congestion degree. Our experimental results show that the average accuracy of the GMRC algorithm is 24.9% greater than that of the K-means algorithm and 30.8% greater than that of the Fuzzy C-Means (FCM) algorithm. Furthermore, we find that our method can be more conducive to dynamic traffic warnings. Full article
(This article belongs to the Special Issue Applications of Internet of Things)
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