Special Issue "Ubiquitous Sensing"
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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: 30 June 2012
Special Issue Editor
Guest Editor
Dr. Feng Xia
School of Software, Dalian University of Technology, Road No. 8, Development Zone, Dalian 116620, China
Website: http://www.fengxia.net/
E-Mail: f.xia@ieee.org
Phone: +86 411 87571582
Interests: cyber-physical systems; internet of things; mobile computing; social computing; intelligent systems
Special Issue Information
Dear Colleagues,
The era of ubiquitous sensing has begun recently thanks to the proliferation of wireless sensors. For instance, modern smartphones are equipped with a variety of sensors that can be used to continuously monitor activities and locations of the users, and a rapidly growing number of sensors are embedded into the physical world for monitoring of our living environments. Ubiquitous sensing promises to enhance awareness of the cyber, physical, and social contexts of our daily activities, thus providing supports for services and applications that will change our lives.
This special issue aims to collect recent research results that address key issues and topics related to ubiquitous sensing. We recommend authors provide as much details as possible, extended long research papers or comprehensive reviews (tutorial and survey papers approximately 30-50 pages each are particularly welcome.
In addition to open submissions, authors of selected papers published in the 2011 IEEE International Conference on Cyber, Physical and Social Computing (CPSCom 2011; http://cpscom.org) and the 2011 IEEE International Conference on Internet of Things (iThings 2011; http://ieee-iot.org) will be invited to submit extended versions of their papers to this special issue for consideration. Manuscripts from the conferences must have at least 40% extension compared with the conference versions.
Dr. Feng Xia
Guest Editor
Submission
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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 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 1600 CHF (Swiss Francs).
Keywords
- participatory sensing
- human-centric sensing
- sensing with smartphones
- mobile sensing
- ubiquitous sensing with RFID
- sensing for social computing
- community sensing
- wireless sensor networks
- cyber-physical systems
- Internet of Things
- platforms for ubiquitous sensing
- ubiquitous sensing services and applications
Published Papers (7 papers)
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Received: 24 December 2011; in revised form: 29 January 2012 / Accepted: 30 January 2012 / Published: 6 February 2012
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Abstract: This article presents a novel closed loop control architecture based on audio channels of several types of computing devices, such as mobile phones and tablet computers, but not restricted to them. The communication is based on an audio interface that relies on the exchange of audio tones, allowing sensors to be read and actuators to be controlled. As an application example, the presented technique is used to build a low cost mobile robot, but the system can also be used in a variety of mechatronics applications and sensor networks, where smartphones are the basic building blocks.
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Received: 31 January 2012; in revised form: 10 February 2012 / Accepted: 10 February 2012 / Published: 14 February 2012
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Abstract: Current mobile phones come with several sensors and powerful video cameras. These video cameras can be used to capture good quality scenes, which can be complemented with the information gathered by the sensors also embedded in the phones. For example, the surroundings of a beach recorded by the camera of the mobile phone, jointly with the temperature of the site can let users know via the Internet if the weather is nice enough to swim. In this paper, we present a system that tags the video frames of the video recorded from mobile phones with the data collected by the embedded sensors. The tagged video is uploaded to a video server, which is placed on the Internet and is accessible by any user. The proposed system uses a semantic approach with the stored information in order to make easy and efficient video searches. Our experimental results show that it is possible to tag video frames in real time and send the tagged video to the server with very low packet delay variations. As far as we know there is not any other application developed as the one presented in this paper.
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Received: 14 January 2012; in revised form: 24 February 2012 / Accepted: 27 February 2012 / Published: 6 March 2012
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Abstract: This paper presents a feasibility study on a real-time in field pest classification system design based on Blackfin DSP and 3G wireless communication technology. This prototype system is composed of remote on-line classification platform (ROCP), which uses a digital signal processor (DSP) as a core CPU, and a host control platform (HCP). The ROCP is in charge of acquiring the pest image, extracting image features and detecting the class of pest using an Artificial Neural Network (ANN) classifier. It sends the image data, which is encoded using JPEG 2000 in DSP, to the HCP through the 3G network at the same time for further identification. The image transmission and communication are accomplished using 3G technology. Our system transmits the data via a commercial base station. The system can work properly based on the effective coverage of base stations, no matter the distance from the ROCP to the HCP. In the HCP, the image data is decoded and the pest image displayed in real-time for further identification. Authentication and performance tests of the prototype system were conducted. The authentication test showed that the image data were transmitted correctly. Based on the performance test results on six classes of pests, the average accuracy is 82%. Considering the different live pests’ pose and different field lighting conditions, the result is satisfactory. The proposed technique is well suited for implementation in field pest classification on-line for precision agriculture.
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Received: 6 March 2012; in revised form: 10 April 2012 / Accepted: 11 April 2012 / Published: 26 April 2012
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Abstract: Activity monitoring of a person for a long-term would be helpful for controlling lifestyle associated diseases. Such diseases are often linked with the way a person lives. An unhealthy and irregular standard of living influences the risk of such diseases in the later part of one’s life. The symptoms and the initial signs of these diseases are common to the people with irregular lifestyle. In this paper, we propose a novel healthcare framework to manage lifestyle diseases using long-term activity monitoring. The framework recognizes the user’s activities with the help of the sensed data in runtime and reports the irregular and unhealthy activity patterns to a doctor and a caregiver. The proposed framework is a hierarchical structure that consists of three modules: activity recognition, activity pattern generation and lifestyle disease prediction. We show that it is possible to assess the possibility of lifestyle diseases from the sensor data. We also show the viability of the proposed framework.
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Received: 3 April 2012; in revised form: 23 April 2012 / Accepted: 24 April 2012 / Published: 4 May 2012
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Abstract: An ultimate goal for Ubiquitous Computing is to enable people to interact with the surrounding electrical devices using their habitual body gestures as they communicate with each other. The feasibility of such an idea is demonstrated through a wearable gestural device named Magic Ring (MR), which is an original compact wireless sensing mote in a ring shape that can recognize various finger gestures. A scenario of wireless multiple appliances control is selected as a case study to evaluate the usability of such a gestural interface. Experiments comparing the MR and a Remote Controller (RC) were performed to evaluate the usability. From the results, only with 10 minutes practice, the proposed paradigm of gestural-based control can achieve a performance of completing about six tasks per minute, which is in the same level of the RC-based method.
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Received: 1 April 2012; in revised form: 8 May 2012 / Accepted: 14 May 2012 / Published: 16 May 2012
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Abstract: The extensions of the environment with the integration of sensing systems in any space, in conjunction with ubiquitous computing are enabling the so-called Smart Space Sensor Networks. This new generation of networks are offering full connectivity with any object, through the Internet of Things (IoT) and/or the Web, i.e., the Web of Things. These connectivity capabilities are making it feasible to sense the behaviours of people at home and act accordingly. These sensing systems must be integrated within typical elements found at home such as furniture. For that reason, this work considers furniture as an interesting element for the transparent location of sensors. Furniture is a ubiquitous object, i.e., it can be found everywhere at home or the office, and it can integrate and hide the sensors of a network. This work addresses the lack of an exhaustive study of the effect of furniture on signal losses. In addition an easy-to-use tool for estimating the robustness of the communication channel among the sensor nodes and gateways is proposed. Specifically, the losses in a sensor network signal due to the materials found within the communication link are evaluated. Then, this work proposes a software tool that gathers the obtained results and is capable of evaluating the impact of a given set of materials on the communications. This tool also provides a mechanism to optimize the sensor network deployments during the definition of smart spaces. Specifically, it provides information such as: maximum distances between sensor nodes, most suitable type of furniture to integrate sensors, or battery life of sensor nodes. This tool has been validated empirically in the lab, and it is currently being used by several enterprise partners of the Technological Centre of Furniture and Wood in the southeast of Spain.
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Received: 29 March 2012; in revised form: 26 April 2012 / Accepted: 26 April 2012 / Published: 25 May 2012
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Abstract: Target tracking is an important application of wireless sensor networks. The networks’ ability to locate and track an object is directed linked to the nodes’ ability to locate themselves. Consequently, localization systems are essential for target tracking applications. In addition, sensor networks are often deployed in remote or hostile environments. Therefore, density control algorithms are used to increase network lifetime while maintaining its sensing capabilities. In this work, we analyze the impact of localization algorithms (RPE and DPE) and density control algorithms (GAF, A3 and OGDC) on target tracking applications. We adapt the density control algorithms to address the k-coverage problem. In addition, we analyze the impact of network density, residual integration with density control, and k-coverage on both target tracking accuracy and network lifetime. Our results show that DPE is a better choice for target tracking applications than RPE. Moreover, among the evaluated density control algorithms, OGDC is the best option among the three. Although the choice of the density control algorithm has little impact on the tracking precision, OGDC outperforms GAF and A3 in terms of tracking time.
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Last update: 18 May 2012
