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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: closed (28 February 2019) | Viewed by 41826

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Special Issue Editors

Prof. Paul S. Pang

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Department of Computing, Unitec Institute of Technology, Mount Albert, Auckland 1025, New Zealand
Interests: applied data analytics; deep learning applications, cybersecurity and cloud computing

Prof. Dr. Keiichi Yasumoto

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Guest Editor

Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan
Interests: ubiquitous computing; mobile computing; sensor networks
Special Issues, Collections and Topics in MDPI journals

Prof. Takuya Yoshihiro

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Faculty of Systems Engineering, Wakayama University, 930 Sakaedani, Wakayama, Japan
Interests: internet routing; wireless ad-hoc networks; graph algorithms; bio informatics

Dr. Xuyun Zhang

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Guest Editor

Department of Electrical & Computer Engineering, University of Auckland, New Zealand
Interests: big IoT data analytics with cloud computing infrastructure (specifically in outlier/anomaly detection); parallel and distributed algorithms design; scalable machine learning and data mining; big data privacy preservation

Special Issue Information

Dear Colleagues,

The Special Issue will be associated with, though not exclusively, the 11th International Conference on Mobile Computing and Ubiquitous Networking (ICMU2018). The authors of papers accepted to ICMU 2018 are invited to submit extended versions to this Special Issue.

This Special Issue focuses on the research and development of mobile communications, applications, algorithms, and systems, as well as ubiquitous services and computing. Through these efforts, we expect to help advance technologies for next-generation distributed and ubiquitous computing, where humans, networked sensors, connected devices, and the environment are involved. Examples of such technologies are IoT, human-centric sensing, energy-efficient mobile systems, social-networking, machine-to-machine communications, mobile cloud computing and mobile social P2P. Not only these issues, but also fundamental algorithms and theories for mobile system privacy, security, reliability, and robustness, are also topics of interest.

In this Special Issue, potential topics include, but are not limited to:

Wireless access technologies
Networked sensing, and applications
Mobile device architectures
Mobile systems and applications
Mobile data management and analytics
Mobile multimedia
Mobile user interfaces and interaction technologies
Mobile user experience
Toolkit, and languages for mobile computing
Energy aware mobile computing
Mobile cloud computing
Semantic web technologies
Localization and tracking
Internet of things
Crowdsourcing
Participatory sensing
Social network applications to mobile computing
Wearable computing
Context and location aware applications and services
Body area networks
Trust, security and privacy
Edge computing
Machine learning for mobile and ubiquitous computing

Prof. Paul S. Pang
Prof. Keiichi Yasumoto
Prof. Takuya Yoshihiro
Dr. Xuyun Zhang
Guest Editors

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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 submissions that pass pre-check are 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.

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Published Papers (9 papers)

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Research

18 pages, 1283 KiB

Open AccessArticle

ThermalWrist: Smartphone Thermal Camera Correction Using a Wristband Sensor †

by Hiroki Yoshikawa, Akira Uchiyama and Teruo Higashino

Sensors 2019, 19(18), 3826; https://doi.org/10.3390/s19183826 - 4 Sep 2019

Cited by 18 | Viewed by 5689

Abstract

Thermal images are widely used for various healthcare applications and advanced research. However, thermal images captured by smartphone thermal cameras are not accurate for monitoring human body temperature due to the small body that is vulnerable to temperature change. In this paper, we propose ThermalWrist, a dynamic offset correction method for thermal images captured by smartphone thermal cameras. We fully utilize the characteristic that is specific to thermal cameras: the relative temperatures in a single thermal image are highly reliable, although the absolute temperatures fluctuate frequently. To correct the offset error, ThermalWrist combines thermal images with a reliable absolute temperature obtained by a wristband sensor based on the above characteristic. The evaluation results in an indoor air-conditioned environment shows that the mean absolute error and the standard deviation of face temperature measurement error decrease by 49.4% and 64.9%, respectively. In addition, Pearson’s correlation coefficient increases by 112%, highlighting the effectiveness of ThermalWrist. We also investigate the limitation with respect to the ambient temperature where ThermalWrist works effectively. The result shows ThermalWrist works well in the normal office environment, which is 22.91 °C and above. Full article

(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)

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21 pages, 1249 KiB

Open AccessArticle

A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks

by Daichi Araki and Takuya Yoshihiro

Sensors 2019, 19(12), 2688; https://doi.org/10.3390/s19122688 - 14 Jun 2019

Cited by 6 | Viewed by 2966

Abstract

Vehicular Ad hoc NETworks (VANET) has been well studied for a long time as a means to exchange information among moving vehicles. As vehicular networks do not always have connected paths, vehicular networks can be regarded as a kind of delay-tolerant networks (DTNs) when the density of vehicles is not high enough. In this case, packet delivery ratio degrades significantly so that reliability of networks as an information infrastructure is hardly held. Past studies such as SADV (Static-node Assisted Data dissemination protocol for Vehicular networks) and RDV (Reliable Distance-Vector routing) showed that the assistance of low-cost unwired static nodes located at intersections, which work as routers to provide distance-vector or link-state routing functions, significantly improves the communication performance. However, they still have problems: SADV does not provide high-enough delivery ratio and RDV suffers from traffic concentration on the shortest paths. In this paper, we propose MP-RDV (Multi-Path RDV) by extending RDV with multiple paths utilization to improve performance against both of those problems. In addition, we apply a delay routing metric, which is one of the major metrics in this field, to RDV to compare performance with the traffic-volume metric, which is a built-in metric of RDV. Evaluation results show that MP-RDV achieves high load-balancing performance, larger network capacity, lower delivery delay, and higher fault tolerance against topology changes compared to RDV. As for routing metrics, we showed that the traffic-volume metric is better than the delay one in RDV because delay measurement is less stable against traffic fluctuation. Full article

(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)

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21 pages, 429 KiB

Open AccessArticle

Animations in Cross-Platform Mobile Applications: An Evaluation of Tools, Metrics and Performance

by Andreas Biørn-Hansen, Tor-Morten Grønli and Gheorghita Ghinea

Sensors 2019, 19(9), 2081; https://doi.org/10.3390/s19092081 - 5 May 2019

Cited by 13 | Viewed by 9835

Abstract

Along with the proliferation of high-end and performant mobile devices, we find that the inclusion of visually animated user interfaces are commonplace, but that research on their performance is scarce. Thus, for this study, eight mobile apps have been developed for scrutiny and assessment to report on the device hardware impact and penalties caused by transitions and animations, with an emphasis on apps generated using cross-platform development frameworks. The tasks we employ for animation performance measuring, are those of (i) a complex animation consisting of multiple elements, (ii) the opening sequence of a side menu navigation pattern, and (iii) a transition animation during in-app page navigation. We employ multiple performance profiling tools, and scrutinize metrics including frames per second (FPS), CPU usage, device memory usage and GPU memory usage, all to uncover the impact caused by executing transitions and animations. We uncover important differences in device hardware utilization during animations across the different cross-platform technologies employed. Additionally, Android and iOS are found to differ greatly in terms of memory consumption, CPU usage and rendered FPS, a discrepancy that is true for both the native and cross-platform apps. The findings we report are indeed factors contributing to the complexity of app development. Full article

(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)

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16 pages, 3276 KiB

Open AccessArticle

Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning

by Daisuke Mochizuki, Yu Abiko, Takato Saito, Daizo Ikeda and Hiroshi Mineno

Sensors 2019, 19(7), 1674; https://doi.org/10.3390/s19071674 - 8 Apr 2019

Cited by 7 | Viewed by 3731

Abstract

The demand for mobile data communication has been increasing owing to the diversification of its purposes and the increase in the number of mobile devices accessing mobile networks. Users are experiencing a degradation in communication quality due to mobile network congestion. Therefore, improving the bandwidth utilization efficiency of cellular infrastructure is crucial. We previously proposed a mobile data offloading protocol (MDOP) for improving the bandwidth utilization efficiency. Although this method balances a load of evolved node B by taking into consideration the content delay tolerance, accurately balancing the load is challenging. In this paper, we apply deep reinforcement learning to MDOP to solve the temporal locality of a traffic. Moreover, we examine and evaluate the concrete processing while considering a delay tolerance. A comparison of the proposed method and bandwidth utilization efficiency of MDOP showed that the proposed method reduced the network traffic in excess of the control target value by 35% as compared with the MDOP. Furthermore, the proposed method improved the data transmission ratio by the delay tolerance range. Consequently, the proposed method improved the bandwidth utilization efficiency by learning how to provide the bandwidth to the user equipment when MDOP cannot be used to appropriately balance a load. Full article

(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)

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17 pages, 1792 KiB

Open AccessArticle

MuCHLoc: Indoor ZigBee Localization System Utilizing Inter-Channel Characteristics

by Ryota Kimoto, Shigemi Ishida, Takahiro Yamamoto, Shigeaki Tagashira and Akira Fukuda

Sensors 2019, 19(7), 1645; https://doi.org/10.3390/s19071645 - 6 Apr 2019

Cited by 16 | Viewed by 4061

Abstract

The deployment of a large-scale indoor sensor network faces a sensor localization problem because we need to manually locate significantly large numbers of sensors when Global Positioning System (GPS) is unavailable in an indoor environment. Fingerprinting localization is a popular indoor localization method relying on the received signal strength (RSS) of radio signals, which helps to solve the sensor localization problem. However, fingerprinting suffers from low accuracy because of an RSS instability, particularly in sensor localization, owing to low-power ZigBee modules used on sensor nodes. In this paper, we present MuCHLoc, a fingerprinting sensor localization system that improves the localization accuracy by utilizing channel diversity. The key idea of MuCHLoc is the extraction of channel diversity from the RSS of Wi-Fi access points (APs) measured on multiple ZigBee channels through fingerprinting localization. MuCHLoc overcomes the RSS instability by increasing the dimensions of the fingerprints using channel diversity. We conducted experiments collecting the RSS of Wi-Fi APs in a practical environment while switching the ZigBee channels, and evaluated the localization accuracy. The evaluations revealed that MuCHLoc improves the localization accuracy by approximately 15% compared to localization using a single channel. We also showed that MuCHLoc is effective in a dynamic radio environment where the radio propagation channel is unstable from the movement of objects including humans. Full article

(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)

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21 pages, 1845 KiB

Open AccessArticle

A Game-Theoretic Framework to Preserve Location Information Privacy in Location-Based Service Applications

by Mulugeta Kassaw Tefera and Xiaolong Yang

Sensors 2019, 19(7), 1581; https://doi.org/10.3390/s19071581 - 1 Apr 2019

Cited by 8 | Viewed by 3346

Abstract

Recently, the growing ubiquity of location-based service (LBS) technology has increased the likelihood of users’ privacy breaches due to the exposure of their real-life information to untrusted third parties. Extensive use of such LBS applications allows untrusted third-party adversarial entities to collect large quantities of information regarding users’ locations over time, along with their identities. Due to the high risk of private information leakage using resource-constrained smart mobile devices, most LBS users may not be adequately encouraged to access all LBS applications. In this paper, we study the use of game theory to protect users against private information leakage in LBSs due to malicious or selfish behavior of third-party observers. In this study, we model a scenario of privacy protection gameplay between a privacy protector and an outside visitor and then derive the situation of the prisoner’s dilemma game to analyze the traditional privacy protection problems. Based on the analysis, we determine the corresponding benefits to both players using a point of view that allows the visitor to access a certain amount of information and denies further access to the user’s private information when exposure of privacy is forthcoming. Our proposed model uses the collection of private information about historical access data and current LBS access scenario to effectively determine the probability that the visitor’s access is an honest one. Moreover, we present the procedures involved in the privacy protection model and framework design, using game theory for decision-making. Finally, by employing a comparison analysis, we perform some experiments to assess the effectiveness and superiority of the proposed game-theoretic model over the traditional solutions. Full article

(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)

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14 pages, 788 KiB

Open AccessArticle

Connectivity Analysis of Cognitive Radio Ad-Hoc Networks with Multi-Pair Primary Networks

by Le The Dung and Seong-Gon Choi

Sensors 2019, 19(3), 565; https://doi.org/10.3390/s19030565 - 29 Jan 2019

Cited by 6 | Viewed by 2876

Abstract

In this paper, we study the connectivity of cognitive radio ad-hoc networks (CRAHNs) where primary users (PUs) and secondary users (SUs) are randomly distributed in a given area following a homogeneous Poisson process. Moreover, for the sake of more realistic CRAHNs, contrary to previous works in the literature, we consider the case that primary network is comprised of multiple communication pairs which are spatial-temporal distributed in the network area. We also take into consideration the differences in transmission range and interference range of both PUs and SUs. The connectivity of such CRAHN is studied from three viewpoints. First, we mathematically analyze the probability of isolated secondary transmitter and secondary receiver. Second, we derive the approximation expression of the link probability between two adjacent SUs. Third, we investigate the path connectivity between two arbitrary SUs by using the simulation analysis approach. The correctness of our mathematical expressions is confirmed by comparing analytical results with simulation results. The results in this paper provide insights into how multiple communication pairs in primary network affect the connectivity of secondary network, which can be useful guidelines for the design of CRAHNs. Full article

(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)

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15 pages, 620 KiB

Open AccessArticle

Cooperative Sensing Data Collection and Distribution with Packet Collision Avoidance in Mobile Long-Thin Networks

by Lien-Wu Chen, Yu-Hao Peng, Yu-Chee Tseng and Ming-Fong Tsai

Sensors 2018, 18(10), 3588; https://doi.org/10.3390/s18103588 - 22 Oct 2018

Cited by 11 | Viewed by 3432

Abstract

Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is composed of 3G/LTE interfaces and the low-tier network is composed of IEEE 802.11 interfaces. The low-tier network may consist of several path-like networks. This work investigates cooperative sensing data collection and distribution with packet collision avoidance in a two-tier MLTN. As numbers of cyclists upload their sensing data and download global fleet information frequently, serious bandwidth and latency problems may result if all members rely on their high-tier interfaces. We designed and analyzed a cooperative framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. Through cooperation between the two tiers, the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmission and the number of 3G/LTE connections. Full article

(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)

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21 pages, 3930 KiB

Open AccessFeature PaperArticle

From Signal to Image: Enabling Fine-Grained Gesture Recognition with Commercial Wi-Fi Devices

by Qizhen Zhou, Jianchun Xing, Wei Chen, Xuewei Zhang and Qiliang Yang

Sensors 2018, 18(9), 3142; https://doi.org/10.3390/s18093142 - 18 Sep 2018

Cited by 30 | Viewed by 4918

Abstract

Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image processing so as to better depict the influence induced by subtle finger movements. In particular, we make multiple efforts to transfer sensitive Channel State Information (CSI) into depth radio images, including antenna selection, gesture segmentation and image construction, followed by noisy image purification using high-dimensional relations. To fulfill the restrictive size requirements of deep learning model, we propose a novel region-selection method to constrain the image size and select qualified regions with dominant color and texture features. Finally, a 7-layer Convolutional Neural Network (CNN) and SoftMax function are adopted to achieve automatic feature extraction and accurate gesture classification. Experimental results demonstrate the excellent performance of DeepNum, which recognizes 10 finger gestures with overall accuracy of 98% in three typical indoor scenarios. Full article

(This article belongs to the Special Issue Mobile Computing and Ubiquitous Networking)

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