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16 pages, 2687 KiB

Open AccessArticle

A Study on the Design of Fog Computing Architecture Using Sensor Networks

by Hyun-Jong Cha, Ho-Kyung Yang and You-Jin Song

Sensors 2018, 18(11), 3633; https://doi.org/10.3390/s18113633 - 26 Oct 2018

Cited by 44 | Viewed by 5343

It is expected that the number of devices connecting to the Internet-of-Things (IoT) will increase geometrically in the future, with improvement of their functions. Such devices may create a huge amount of data to be processed in a limited time. Under the IoT [...] Read more.

It is expected that the number of devices connecting to the Internet-of-Things (IoT) will increase geometrically in the future, with improvement of their functions. Such devices may create a huge amount of data to be processed in a limited time. Under the IoT environment, data management should play the role of an intermediate level between objects and devices that generate data and applications that access to the data for analysis and the provision of services. IoT interactively connects all communication devices and allows global access to the data generated by a device. Fog computing manages data and computation at the edge of the network near an end user and provides new types of applications and services, with low latency, high frequency bandwidth and geographical distribution. In this paper, we propose a fog computing architecture for efficiently and reliably delivering IoT data to the corresponding IoT applications while ensuring time sensitivity. Based on fog computing, the proposed architecture provides efficient power management in IoT device communication between sensors and secure management of data to be decrypted based on user attributes. The functional effectiveness and the safe data management of the method proposed are compared through experiments. Full article

(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)

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14 pages, 524 KiB

Open AccessArticle

Estimation of Driver’s Danger Level when Accessing the Center Console for Safe Driving

by Hyun-Soon Lee, Sunyoung Oh, Daeseong Jo and Bo-Yeong Kang

Sensors 2018, 18(10), 3392; https://doi.org/10.3390/s18103392 - 10 Oct 2018

Cited by 3 | Viewed by 2997

Abstract

This paper proposes a system for estimating the level of danger when a driver accesses the center console of a vehicle while driving. The proposed system uses a driver monitoring platform to measure the distance between the driver’s hand and the center console [...] Read more.

This paper proposes a system for estimating the level of danger when a driver accesses the center console of a vehicle while driving. The proposed system uses a driver monitoring platform to measure the distance between the driver’s hand and the center console during driving, as well as the time taken for the driver to access the center console. Three infrared sensors on the center console are used to detect the movement of the driver’s hand. These sensors are installed in three locations: the air conditioner or heater (temperature control) button, wind direction control button, and wind intensity control button. A driver’s danger level is estimated to be based on a linear regression analysis of the distance and time of movement between the driver’s hand and the center console, as measured in the proposed scenarios. In the experimental results of the proposed scenarios, the root mean square error of driver H using distance and time of movement between the driver’s hand and the center console is 0.0043, which indicates the best estimation of a driver’s danger level. Full article

(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)

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17 pages, 3141 KiB

Open AccessArticle

Deep Learning-Based Caution Area Traffic Prediction with Automatic Identification System Sensor Data

by Kwang-Il Kim and Keon Myung Lee

Sensors 2018, 18(9), 3172; https://doi.org/10.3390/s18093172 - 19 Sep 2018

Cited by 54 | Viewed by 6731

Abstract

In a crowded harbor water area, it is a major concern to control ship traffic for assuring safety and maximizing the efficiency of port operations. Vessel Traffic Service (VTS) operators pay much attention to caution areas like ship route intersections or traffic congestion [...] Read more.

In a crowded harbor water area, it is a major concern to control ship traffic for assuring safety and maximizing the efficiency of port operations. Vessel Traffic Service (VTS) operators pay much attention to caution areas like ship route intersections or traffic congestion area in which there are some risks of ship collision. They want to control the traffic of the caution area at a proper level to lessen risk. Inertial ship movement makes swift changes in direction and speed difficult. It is hence important to predict future traffic of the caution area earlier on so as to get enough time for control actions on ship movements. In the harbor area, VTS stations collect a large volume of Automatic Identification Service (AIS) sensor data, which contain information about ship movement and ship attributes. This paper proposes a new deep neural network model called Ship Traffic Extraction Network (STENet) to predict the medium-term traffic and long-term traffic of the caution area. The STENet model is trained with AIS sensor data. The STENet model is organized into a hierarchical architecture in which the outputs of the movement and contextual feature extraction modules are concatenated and fed into a prediction module. The movement module extracts the features of overall ship movements with a convolutional neural network. The contextual modules consist of five separated fully-connected neural networks, each of which receives an associated attribute. The separation of feature extraction modules at the front phase helps extract the effective features by preventing unrelated attributes from crosstalking. To evaluate the performance of the proposed model, the developed model is applied to a real AIS sensor dataset, which has been collected over two years at a Korean port called Yeosu. In the experiments, four methods have been compared including two new methods: STENet and VGGNet-based models. For the real AIS sensor dataset, the proposed model has shown 50.65% relative performance improvement on average for the medium-term predictions and 57.65% improvement on average for the long-term predictions over the benchmark method, i.e., the SVR-based method. Full article

(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)

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16 pages, 3900 KiB

Open AccessArticle

Complex Event Processing for Sensor Stream Data

by Kyoungsoo Bok, Daeyun Kim and Jaesoo Yoo

Sensors 2018, 18(9), 3084; https://doi.org/10.3390/s18093084 - 13 Sep 2018

Cited by 12 | Viewed by 4261

Abstract

As a large amount of stream data are generated through sensors over the Internet of Things environment, studies on complex event processing have been conducted to detect information required by users or specific applications in real time. A complex event is made by [...] Read more.

As a large amount of stream data are generated through sensors over the Internet of Things environment, studies on complex event processing have been conducted to detect information required by users or specific applications in real time. A complex event is made by combining primitive events through a number of operators. However, the existing complex event-processing methods take a long time because they do not consider similarity and redundancy of operators. In this paper, we propose a new complex event-processing method considering similar and redundant operations for stream data from sensors in real time. In the proposed method, a similar operation in common events is converted into a virtual operator, and redundant operations on the same events are converted into a single operator. The event query tree for complex event detection is reconstructed using the converted operators. Through this method, the cost of comparison and inspection of similar and redundant operations is reduced, thereby decreasing the overall processing cost. To prove the superior performance of the proposed method, its performance is evaluated in comparison with existing methods. Full article

(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)

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11 pages, 3200 KiB

Open AccessArticle

A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging

by Kai Tang, Aijia Liu, Wei Wang, Pengfei Li and Xi Chen

Sensors 2018, 18(9), 3050; https://doi.org/10.3390/s18093050 - 12 Sep 2018

Cited by 14 | Viewed by 5782 | Correction

Abstract

In this paper, we propose a new fingerprint sensing technology based on electrostatic imaging, which can greatly improve fingerprint sensing distance. This can solve the problem of the existing capacitive fingerprint identification device being easy to damage due to limited detection distance and [...] Read more.

In this paper, we propose a new fingerprint sensing technology based on electrostatic imaging, which can greatly improve fingerprint sensing distance. This can solve the problem of the existing capacitive fingerprint identification device being easy to damage due to limited detection distance and a protective coating that is too thin. The fingerprint recognition sensor can also be placed under a glass screen to meet the needs of the full screen design of the mobile phone. In this paper, the electric field distribution around the fingerprint is analyzed. The electrostatic imaging sensor design is carried out based on the electrostatic detection principle and MEMS (micro-electro-mechanical system) technology. The MEMS electrostatic imaging array, analog, and digital signal processing circuit structure are designed. Simulation and testing are carried out as well. According to the simulation and prototype test device test results, it is confirmed that our proposed electrostatic imaging-based fingerprint sensing technology can increase fingerprint recognition distance by 46% compared to the existing capacitive fingerprint sensing technology. A distance of more than 439 μm is reached. Full article

(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)

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11 pages, 3151 KiB

Open AccessArticle

Energy-Efficient Forest Fire Prediction Model Based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol

by Jin-Gu Kang, Dong-Woo Lim and Jin-Woo Jung

Sensors 2018, 18(9), 2960; https://doi.org/10.3390/s18092960 - 5 Sep 2018

Cited by 10 | Viewed by 4186

Abstract

This paper proposes an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The Asynchronous sensor network protocol, X-MAC protocol, acquires additional environmental status details from each forest fire monitoring sensor for a given period, and then changes the duty-cycle sleep [...] Read more.

This paper proposes an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The Asynchronous sensor network protocol, X-MAC protocol, acquires additional environmental status details from each forest fire monitoring sensor for a given period, and then changes the duty-cycle sleep interval to efficiently calculate forest fire occurrence risk according to the environment. Performance was verified experimentally, and the proposed ADX-MAC protocol improved throughput by 19% and was 24% more energy efficient compared to the X-MAC protocol. The duty-cycle was shortened as forest fire probability increased, ensuring forest fires were detected at faster cycle rate. Full article

(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)

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26 pages, 8849 KiB

Open AccessArticle

Efficient Force Control Learning System for Industrial Robots Based on Variable Impedance Control

by Chao Li, Zhi Zhang, Guihua Xia, Xinru Xie and Qidan Zhu

Sensors 2018, 18(8), 2539; https://doi.org/10.3390/s18082539 - 3 Aug 2018

Cited by 35 | Viewed by 7695

Abstract

Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve [...] Read more.

Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve the sampling efficiency and decrease the required interactions during the learning process, this paper develops a data-efficient learning variable impedance control method that enables the industrial robots automatically learn to control the contact force in the unstructured environment. To this end, a Gaussian process model is learned as a faithful proxy of the system, which is then used to predict long-term state evolution for internal simulation, allowing for efficient strategy updates. The effects of model bias are reduced effectively by incorporating model uncertainty into long-term planning. Then the impedance profiles are regulated online according to the learned humanlike impedance strategy. In this way, the flexibility and adaptivity of the system could be enhanced. Both simulated and experimental tests have been performed on an industrial manipulator to verify the performance of the proposed method. Full article

(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)

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15 pages, 5495 KiB

Open AccessArticle

Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics

by Kai Tang, Pengfei Li, Chuang Wang, Yifei Wang and Xi Chen

Sensors 2018, 18(6), 1677; https://doi.org/10.3390/s18061677 - 23 May 2018

Cited by 7 | Viewed by 4214

Abstract

Non-contact human-computer interactions (HCI) based on hand gestures have been widely investigated. Here, we present a novel method to locate the real-time position of the hand using the electrostatics of the human body. This method has many advantages, including a delay of less [...] Read more.

Non-contact human-computer interactions (HCI) based on hand gestures have been widely investigated. Here, we present a novel method to locate the real-time position of the hand using the electrostatics of the human body. This method has many advantages, including a delay of less than one millisecond, low cost, and does not require a camera or wearable devices. A formula is first created to sense array signals with five spherical electrodes. Next, a solving algorithm for the real-time measured hand position is introduced and solving equations for three-dimensional coordinates of hand position are obtained. A non-contact real-time hand position sensing system was established to perform verification experiments, and the principle error of the algorithm and the systematic noise were also analyzed. The results show that this novel technology can determine the dynamic parameters of hand movements with good robustness to meet the requirements of complicated HCI. Full article

(This article belongs to the Special Issue Innovative Sensor Technology for Intelligent System and Computing)

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