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Ubiquitous Massive Sensing Using Smartphones

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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 (10 July 2018) | Viewed by 36235

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Dr. Emanuele Lattanzi

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Guest Editor

Department of Pure and Applied Sciences, University of Urbino, 61029 Urbino, Italy
Interests: wireless sensor networks; machine learning; internet of things; embedded devices
Special Issues, Collections and Topics in MDPI journals

Dr. Valerio Freschi

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Guest Editor

Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy
Interests: wireless sensor networks; networked embedded systems; energy-aware algorithms; machine learning; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ubiquitous massive sensing is a new idea, consisting of using widespread available mobile devices, such as smartphones, to gather sensor data. To date, smartphones are powerful devices characterized by i) a hardware/software architecture consisting of a multi-core processors, which can perform complex computational tasks; ii) tens of gigabytes of nonvolatile memory and several gigabytes of main memory; and iii) alternative wireless communication channel, such as LTE or WiFi, which enable ubiquitous connectivity. On the other hand, they are also equipped with various sensors, such as GPS, accelerometers, microphones, cameras, temperature sensors, compasses, and so on. Moreover, other sensors, such as those integrated into mobile fitness trackers, can be connected to a smartphone through short range communication technologies like Bluetooth to access the user’s heart rate, stress level, cadence, etc. The wide spectrum of sensing capabilities, together with the high-performance hardware/software architecture, actually turn modern smartphones into powerful mobile sensor platforms and enable a new paradigm of ubiquitous and massive sensing, called crowd sensing or mobile phone sensing. Thanks to this new paradigm, we can build a mobile sensor network with an excellent coverage (especially in urban areas) without investing large amounts of resources in building dedicated sensor networks.

However, in order to construct an effective sensing network, several problems need to be addressed. First of all, we need to ensure the quality of the gathered data (both measuring accuracy and users reliability). Second, we need to preserve precious energy resources of mobile devices. Third, in order to guarantee the meaningfulness of the sensing process, we need to ensure user participation and the usability of the sensing system by offering high-level abstraction interfaces. Finally, ubiquitous massive sensing leads to several challenges, characteristic of the big data domain, such as capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.

Potential topics include, but are not limited to:

Data quality estimation and enhancement algorithms;
Energy efficient techniques for data sensing and processing using smartphones;
Source location privacy and communication privacy preserving techniques for data sensing and processing;
Ubiquitous massive sensing platforms and system architecture;
Big data processing techniques and platforms.

Dr. Emanuele Lattanzi
Dr. Valerio Freschi
Guest Editors

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Keywords

ubiquitous sensing
massive sensing
mobile sensing
smartphones
sensor networks

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Published Papers (6 papers)

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Research

17 pages, 25859 KiB

Open AccessArticle

Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data

by Jorge Rodríguez, Ivana Semanjski, Sidharta Gautama, Nico Van de Weghe and Daniel Ochoa

Sensors 2018, 18(9), 2972; https://doi.org/10.3390/s18092972 - 6 Sep 2018

Cited by 18 | Viewed by 4926

Abstract

Understanding tourism related behavior and traveling patterns is an essential element of transportation system planning and tourism management at tourism destinations. Traditionally, tourism market segmentation is conducted to recognize tourist’s profiles for which personalized services can be provided. Today, the availability of wearable sensors, such as smartphones, holds the potential to tackle data collection problems of paper-based surveys and deliver relevant mobility data in a timely and cost-effective way. In this paper, we develop and implement a hierarchical clustering approach for smartphone geo-localized data to detect meaningful tourism related market segments. For these segments, we provide detailed insights into their characteristics and related mobility behavior. The applicability of the proposed approach is demonstrated on a use case in the Province of Zeeland in the Netherlands. We collected data from 1505 users during five months using the Zeeland app. The proposed approach resulted in two major clusters and four sub-clusters which we were able to interpret based on their spatio-temporal patterns and the recurrence of their visiting patterns to the region. Full article

(This article belongs to the Special Issue Ubiquitous Massive Sensing Using Smartphones)

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16 pages, 3424 KiB

Open AccessArticle

Recording Heart Rate Variability of Dairy Cows to the Cloud—Why Smartphones Provide Smart Solutions

by Maren Wierig, Leonard P. Mandtler, Peter Rottmann, Viktor Stroh, Ute Müller, Wolfgang Büscher and Lutz Plümer

Sensors 2018, 18(8), 2541; https://doi.org/10.3390/s18082541 - 3 Aug 2018

Cited by 13 | Viewed by 12315

Abstract

In the last decades, there has been an increasing interest in animal protection and welfare issues. Heart rate variability (HRV) measurement with portable heart rate monitors on cows has established itself as a suitable method for assessing physiological states. However, more forward-looking technologies, already successfully applied to evaluate HRV data, are pushing the market. This study examines the validity and usability of collecting HRV data by exchanging the Polar watch V800 as a receiving unit of the data compared to a custom smartphone application on cows. Therefore, both receivers tap one signal sent by the Polar H7 transmitter simultaneously. Furthermore, there is a lack of suitable methods for the preparation and calculation of HRV parameters, especially for livestock. A method is presented for calculating more robust time domain HRV parameters via median formation. The comparisons of the respective simultaneous recordings were conducted after artifact correction for time domain HRV parameters. High correlations (r = 0.82–0.98) for cows as well as for control data set in human being (r = 0.98–0.99) were found. The utilization of smart devices and the robust method to determine time domain HRV parameters may be suitable to generate valid HRV data on cows in field-based settings. Full article

(This article belongs to the Special Issue Ubiquitous Massive Sensing Using Smartphones)

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19 pages, 4749 KiB

Open AccessArticle

Fusion of Infrared Thermal Image and Visible Image for 3D Thermal Model Reconstruction Using Smartphone Sensors

by Ming-Der Yang, Tung-Ching Su and Hung-Yu Lin

Sensors 2018, 18(7), 2003; https://doi.org/10.3390/s18072003 - 22 Jun 2018

Cited by 47 | Viewed by 5510

Abstract

Thermal infrared imagery provides temperature information on target objects, and has been widely applied in non-destructive testing. However, thermal infrared imagery is not always able to display detailed textures of inspected objects, which hampers the understanding of geometric entities consisting of temperature information. Although some commercial software has been developed for 3D thermal model displays, the software requires the use of expensive specific thermal infrared sensors. This study proposes a cost-effective method for 3D thermal model reconstruction based on image-based modeling. Two smart phones and a low-cost thermal infrared camera are employed to acquire visible images and thermal images, respectively, that are fused for 3D thermal model reconstruction. The experiment results demonstrate that the proposed method is able to effectively reconstruct a 3D thermal model which extremely approximates its corresponding entity. The total computational time for the 3D thermal model reconstruction is intensive while generating dense points required for the creation of a geometric entity. Future work will improve the efficiency of the proposed method in order to expand its potential applications to in-time monitoring. Full article

(This article belongs to the Special Issue Ubiquitous Massive Sensing Using Smartphones)

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23 pages, 4798 KiB

Open AccessArticle

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer

by Maolin Liu, Huaiyu Li, Yuan Wang, Fei Li and Xiuwan Chen

Sensors 2018, 18(4), 1061; https://doi.org/10.3390/s18041061 - 1 Apr 2018

Cited by 6 | Viewed by 4263

Abstract

Accelerometers, gyroscopes and magnetometers in smartphones are often used to recognize human motions. Since it is difficult to distinguish between vertical motions and horizontal motions in the data provided by these built-in sensors, the vertical motion recognition accuracy is relatively low. The emergence of a built-in barometer in smartphones improves the accuracy of motion recognition in the vertical direction. However, there is a lack of quantitative analysis and modelling of the barometer signals, which is the basis of barometer’s application to motion recognition, and a problem of imbalanced data also exists. This work focuses on using the barometers inside smartphones for vertical motion recognition in multi-floor buildings through modelling and feature extraction of pressure signals. A novel double-windows pressure feature extraction method, which adopts two sliding time windows of different length, is proposed to balance recognition accuracy and response time. Then, a random forest classifier correlation rule is further designed to weaken the impact of imbalanced data on recognition accuracy. The results demonstrate that the recognition accuracy can reach 95.05% when pressure features and the improved random forest classifier are adopted. Specifically, the recognition accuracy of the stair and elevator motions is significantly improved with enhanced response time. The proposed approach proves effective and accurate, providing a robust strategy for increasing accuracy of vertical motions. Full article

(This article belongs to the Special Issue Ubiquitous Massive Sensing Using Smartphones)

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18 pages, 3190 KiB

Open AccessArticle

Towards the Crowdsourcing of Massive Smartphone Assisted-GPS Sensor Ground Observations for the Production of Digital Terrain Models

by Ido Massad and Sagi Dalyot

Sensors 2018, 18(3), 898; https://doi.org/10.3390/s18030898 - 17 Mar 2018

Cited by 10 | Viewed by 4259

Abstract

Digital Terrain Models (DTMs) used for the representation of the bare earth are produced from elevation data obtained using high-end mapping platforms and technologies. These require the handling of complex post-processing performed by authoritative and commercial mapping agencies. In this research, we aim to exploit user-generated data to produce DTMs by handling massive volumes of position and elevation data collected using ubiquitous smartphone devices equipped with Assisted-GPS sensors. As massive position and elevation data are collected passively and straightforwardly by pedestrians, cyclists, and drivers, it can be transformed into valuable topographic information. Specifically, in dense and concealed built and vegetated areas, where other technologies fail, handheld devices have an advantage. Still, Assisted-GPS measurements are not as accurate as high-end technologies, requiring pre- and post-processing of observations. We propose the development and implementation of a 2D Kalman filter and smoothing on the acquired crowdsourced observations for topographic representation production. When compared to an authoritative DTM, results obtained are very promising in producing good elevation values. Today, open-source mapping infrastructures, such as OpenStreetMap, rely primarily on the global authoritative SRTM (Shuttle Radar Topography Mission), which shows similar accuracy but inferior resolution when compared to the results obtained in this research. Accordingly, our crowdsourced methodology has the capacity for reliable topographic representation production that is based on ubiquitous volunteered user-generated data. Full article

(This article belongs to the Special Issue Ubiquitous Massive Sensing Using Smartphones)

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3257 KiB

Open AccessArticle

Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications

by Duc V. Le, Thuong Nguyen, Hans Scholten and Paul J. M. Havinga

Sensors 2017, 17(12), 2763; https://doi.org/10.3390/s17122763 - 29 Nov 2017

Cited by 2 | Viewed by 4334

Abstract

Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating systems were single-tasking, conventional sensing paradigms such as opportunistic sensing and participatory sensing do not explore the relationship among concurrent applications for energy-intensive tasks. In this paper, inspired by social relationships among living creatures in nature, we propose a symbiotic sensing paradigm that can conserve energy, while maintaining equivalent performance to existing paradigms. The key idea is that sensing applications should cooperatively perform common tasks to avoid acquiring the same resources multiple times. By doing so, this sensing paradigm executes sensing tasks with very little extra resource consumption and, consequently, extends battery life. To evaluate and compare the symbiotic sensing paradigm with the existing ones, we develop mathematical models in terms of the completion probability and estimated energy consumption. The quantitative evaluation results using various parameters obtained from real datasets indicate that symbiotic sensing performs better than opportunistic sensing and participatory sensing in large-scale sensing applications, such as road condition monitoring, air pollution monitoring, and city noise monitoring. Full article

(This article belongs to the Special Issue Ubiquitous Massive Sensing Using Smartphones)

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