Sensors In Mobile Devices Research Articles

Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices.

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking into account several concepts, including data acquisition, data processing, data fusion, and pattern recognition. These concepts can be mapped onto different modules of the framework. The proposed framework should perform the identification of ADL without Internet connection, performing these tasks locally on the mobile device, taking in account the hardware and software limitations of these devices. The main purpose of this paper is to present a new approach for the creation of a framework for the recognition of ADL, analyzing the allowed sensors available in the mobile devices, and the existing methods available in the literature.

A novel task recommendation model for mobile crowdsourcing systems

With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. However, how to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived. Based on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, the trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE datasets to verify the effectiveness of the proposed recommendation model.

Analysis of the use of sensors in mobile devices with modified operating systems

This publication concentrate on the posibility of the use of sensors in mobile devices with modified operating systems. Presented research focuses on Android devices. The gyroscope, the accelerometer, the orientation sensor and the light sensor data was acquired with use of Physics Toolbox Sensor software. The research has been conducted on two mobile devices of Xiaomi under control of six different kinds of operating system. Measured values were compared to values recorded by very accurate, reference sensors

THE DATA RELIABILITY OF VOLUNTEERED GEOGRAPHIC INFORMATION WITH USING TRAFFIC ACCIDENT DATA

Abstract. The development of mobile technologies is important in the lives of humans. Mobile devices constitute a great part of the daily lives of people. It has come to such a point that when people first wake up, they check their smart phones for the first thing. Users may share their positions with the GNSS sensors in mobile devices or they can add information about their positions in mobile applications. Users contribute to Geographical Information System with this sharing. These users consist of native (citizens) living in that geographical position not of the CBS specialists. Creating, collecting, sharing and disseminating the geographical data provided by voluntary individuals constitute the Volunteered Geographic Information System. The data in the Volunteered Geographic Information System are received from amateur users. “How reliable will the data received from amateur users instead of specialists of the field be in scientific terms?” In this study, the reliability between the data received from the voluntary users through Volunteered Geographic Information System and real data is investigated. The real data consist of the traffic accident coordinates. The data that will be received from users will be received through the speed values in the relevant coordinates and the marking of the users for possible accident points on the map.

Adaptive Monocular Visual-Inertial SLAM for Real-Time Augmented Reality Applications in Mobile Devices.

Simultaneous localization and mapping (SLAM) is emerging as a prominent issue in computer vision and next-generation core technology for robots, autonomous navigation and augmented reality. In augmented reality applications, fast camera pose estimation and true scale are important. In this paper, we present an adaptive monocular visual–inertial SLAM method for real-time augmented reality applications in mobile devices. First, the SLAM system is implemented based on the visual–inertial odometry method that combines data from a mobile device camera and inertial measurement unit sensor. Second, we present an optical-flow-based fast visual odometry method for real-time camera pose estimation. Finally, an adaptive monocular visual–inertial SLAM is implemented by presenting an adaptive execution module that dynamically selects visual–inertial odometry or optical-flow-based fast visual odometry. Experimental results show that the average translation root-mean-square error of keyframe trajectory is approximately 0.0617 m with the EuRoC dataset. The average tracking time is reduced by 7.8%, 12.9%, and 18.8% when different level-set adaptive policies are applied. Moreover, we conducted experiments with real mobile device sensors, and the results demonstrate the effectiveness of performance improvement using the proposed method.

A Framework for Evaluating Stay Detection Approaches

In recent years, sensors of mobile devices are increasingly used in the research field of Active and Assisted Living (AAL), in particular, for movement analysis. Questions, such as where users typically stay (and for how long), where they have been or where they will most likely be going to, are of utmost importance for implementing smart AAL services. Due to the plethora of application scenarios and varying requirements, the challenge is the identification of an appropriate stay detection approach. Thus, this paper presents a comprehensive framework covering the entire process from data acquisition, pre-processing, parameterization to evaluation so that it can be applied to evaluate various stay detection methods. Additionally, ground truth data as well as application field data are used within the framework. The framework has been validated with three different spatio-temporal clustering approaches (time-based/incremental clustering, extended density based clustering, and a mixed method approach). Using the framework with ground truth data and data from the AAL field, it can be concluded that the time-based/incremental clustering approach is most suitable for this type of AAL applications. Furthermore, using two different datasets has proven successful as it provides additional data for selecting the appropriate method. Finally, the way the framework is designed it might be applied to other domains such as transportation, mobility, or tourism by adapting the pre-selection criteria.

A 3-dimensional group management MAC scheme for mobile IoT devices in wireless sensor networks

We are truly entering the era of Internet of Things (IoT) in which all things are connected and can be used in a variety of ways regardless of time and place. Various types of sensor devices using advanced technology will gather data around us and deliver the information wherever we want. In this paper, we propose an energy efficient MAC scheme for IoT ecosystem environments that include mobile IoT sensor devices. The mobile sensor devices in a target IoT ecosystem gather and collect any required data while moving and transmitting the collected data to a sink node. Energy consumption of the sensor devices depends on the distance from the sink node and also affects overall lifetime of the IoT ecosystems. The proposed 3-D group management MAC (3-D GM MAC) scheme groups sensor devices based on the distance (hop) from the sink node and transmits the collected data only to the next higher group level. That is, the data is transmitted only in the direction to the sink node. In addition, the energy efficiency of the entire IoT ecosystem can be improved by transmitting data based on pre-configured buffer threshold values that are set differently for each group and consequently minimizing the energy consumption of sensor devices near the sink node. When any sensor device cannot transmit data to the next higher group level due to movement, it is newly assigned an appropriate group number and transmits data using a new route. We have shown that the proposed 3-D GM MAC scheme shows excellent behavior in the aspect of energy efficiency for the target IoT ecosystem by simulation. Therefore, the proposed scheme might be adaptable for mobile sensor devices used in various kinds of computing and networking environments such as IoT, big data, cloud computing, and fog computing.

Three-dimensional evaluation ofposturalstability in Parkinson's disease with mobile technology.

Postural instability is a hallmark of Parkinson's disease. Objective metrics to characterize postural stability are necessary for the development of treatment algorithms to aid in the clinical setting. The aim of this project was to validate a mobile device platform and resultant three-dimensional balance metric that characterizes postural stability. A mobile Application was developed, in which biomechanical data from inertial sensors within a mobile device were processed to characterize movement of center of mass in the medial-lateral, anterior-posterior and trunk rotation directions. Twenty-seven individuals with Parkinson's disease and 27 age-matched controls completed various balance tasks. A postural stability metric quantifying the amplitude (peak-to-peak) of sway acceleration in each movement direction was compared between groups. The peak-to-peak value in each direction for each individual with Parkinson's disease across all trials was expressed as a normalized value of the control data to identify individuals with severe postural instability, termed Cleveland Clinic-Postural Stability Index. In all conditions, the balance metric for peak-to-peak was significantly greater in Parkinson's disease compared to controls (p < 0.01 for all tests). The balance metric, in conjunction with mobile device sensors, provides a rapid and systematic metric for quantifying postural stability in Parkinson's disease.

A Mobile Augmented Reality Emulator for Android

Augmented Reality (AR) provides a fusion of the real and virtual worlds by superimposing virtual objects on real world scenery. The implementation of AR on mobile devices is known as Mobile Augmented Reality (MAR). MAR is in its infancy and MAR development software is in the process of maturing. Dating back to the origin of Computer Science as an independent field, software development tools have been an integral part of the process of software creation. MAR, being a relatively new technology, is still lacking such related software development tools.&#x0D; &#x0D; With the rapid progression of mobile devices, the development of MAR applications fusing advanced Computer Vision techniques with mobile device sensors have become increasingly feasible. However, testing and debugging of MAR applications present a new challenge in that they require the developer be at the location that is being augmented at some point during the development process.&#x0D; &#x0D; In this research study, a MAR recorder application was developed as well as emulation class libraries for Android devices that allows the recording and off-site playback of video, location and motion sensor data. The research objective was to provide a software emulator which provides debugging, testing and prototyping capabilities for a MAR application including the ability to emulate the combination of computer vision with locational and motion sensors using previously recorded data. The emulator was evaluated using different mobile technologies. The results indicate that this research could assist developers of MAR applications to implement applications more rapidly, without being at the location.

Partial fingerprint matching using minutiae and ridge shape features for small fingerprint scanners

New partial fingerprint-matching for small sensors in mobile devices is proposed.The method incorporates new ridge shape features (RSFs) in addition to minutiae.RSFs represent small ridge segments where specific edge shapes are observed.These edge shapes are detectable in conventional 500 dpi images of small sensors. Currently, most mobile devices adopt very small fingerprint sensors that only capture small partial fingerprint images. Accordingly, conventional minutiae-based fingerprint matchers are not capable of providing convincing results due to the insufficiency of minutiae. To secure diverse mobile applications such as those requiring privacy protection and mobile payments, a more accurate fingerprint matcher is demanded. This manuscript proposes a new partial fingerprint-matching method incorporating new ridge shape features (RSFs) in addition to the conventional minutia features. These new RSFs represent the small ridge segments where specific edge shapes (concave and convex) are observed, and they are detectable in conventional 500 dpi images. The RSFs are effectively utilized in the proposed matching scheme which consists of minutiae matching and ridge-feature-matching stages. In the minutiae matching stage, corresponding minutia pairs are determined by comparing the local RSFs and minutiae adjacent to each minutia. During the subsequent ridge-feature-matching stage, the RSFs in the overlapped area of two images are further compared to enhance the matching accuracy. A final matching score is obtained by combining the resulting scores from the two matching stages. Various tests for partial matching were conducted on the FVC2002, FVC2004 and BERC (self-constructed) databases, and the proposed method shows significantly lower equal-error rates compared to other matching methods. The results show that the proposed method improves the accuracy of fingerprint recognition, especially for implementation in mobile devices where small fingerprint scanners are adopted.

A Energy Efficient Group Management MAC Scheme for Mobile Sensor Devices in IoT Systems

A Energy Efficient Group Management MAC Scheme for Mobile Sensor Devices in IoT Systems

Miniature Low-Cost Carbon Dioxide Sensor for Mobile Devices

We present our recent advances on developing a miniature sensor for carbon dioxide that may be used in mobile devices. Until now, limiting factors for the implementation of gas sensors in mobile devices, such as smartphones, include their production costs and large size, which is associated with the comparatively poor sensitivity. To overcome these constraints, we employ a photoacoustic-based infrared detection technology to gauge the light intensity of a mid-infrared LED. The photoacoustic detector mainly consists of a commercially available microphone inside a hermetically sealed, carbon dioxide filled cell. To save space and minimize intensity losses, a novel waveguide is used to direct the LED radiation to the detector. The waveguide simultaneously forms the measuring chamber. Because of the high sensitivity of our device, the overall size can be reduced to a level where it is compatible with standard IC sockets. Gas measurements were performed that demonstrate the suitability of the sensor. While providing high sensitivity, the influence of humidity on the sensor signal is insignificant and influences due to temperature shifts may be compensated for.

Efficient end-to-end authentication protocol for wearable health monitoring systems

Wearable health monitoring systems (WHMSs) will play an increasingly important role in future e-healthcare and enable smart and ubiquitous healthcare services. Given its sensitivity, the health data should be protected against unauthorized access. As a result, it is critical to design an end-to-end mutual authentication protocol that enables secure communication between the wearable sensor and medical professionals. Recently, Amin et al. proposed an anonymity preserving mutual authentication protocol for WHMSs. However, we identify that their protocol suffers from stolen mobile device attack, desynchronization attack, and sensor key exposure. Then we put forward an improved end-to-end authentication protocol based on quadratic residues. Comprehensive security analysis is conducted to show that the proposed protocol fixes these flaws of Amin et al.’s protocol and satisfies all desired requirements. The comparison with these existing protocols demonstrates that our protocol provides a practical end-to-end security solution for WHMSs.

An Efficient Localization Method for Mobile Nodes in Wireless Sensor Networks

With the development of electronic technology and communication protocols, wireless sensor network technology is developing rapidly. In a sense, the traditional static wireless sensor network has been unable to meet the needs of new applications. However, the introduction of mobile nodes extends the application of wireless sensor networks, despite the technical challenges. Because of its flexibility, the mobile wireless sensor network has attracted great attention, and even small, self-controlled mobile sensor devices have appeared. At present, mobile node localization has become one of the hotspots in wireless sensor networks. As the storage energy of wireless sensor network nodes is limited, and the communication radius is small, many scientists have focused their research direction on the location algorithm of mobile nodes. According to the continuity principle of mobile node movement, in this paper we propose an improved mobile node localization algorithm based on the Monte Carlo Location (MCL) algorithm, and the method can reduce the sampling interval effectively. First of all, this paper introduces the structure and classification of wireless sensor localization technology. Secondly, the principle of the Monte Carlo Location algorithm is described in detail. Thirdly, we propose an efficient method for mobile node localization based on the MCL algorithm. Finally, the effectiveness and accuracy of the new algorithm are verified by comparative analysis.

One-pass error bounded trajectory simplification

Nowadays, various sensors are collecting, storing and transmitting tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network band and computing resource. Line simplification (LS) algorithms are an effective approach to attacking this issue by compressing data points in a trajectory to a set of continuous line segments, and are commonly used in practice. However, existing LS algorithms are not sufficient for the needs of sensors in mobile devices. In this study, we first develop a one-pass error bounded trajectory simplification algorithm (OPERB), which scans each data point in a trajectory once and only once. We then propose an aggressive one-pass error bounded trajectory simplification algorithm (OPERB-A), which allows interpolating new data points into a trajectory under certain conditions. Finally, we experimentally verify that our approaches (OPERB and OPERB-A) are both efficient and effective, using four real-life trajectory datasets.

Voice-Based User-Device Physical Unclonable Functions for Mobile Device Authentication

In this paper, a novel voice-based User-Device (UD-) physical unclonable function (PUF) is demonstrated. In traditional PUFs, variability of challenge-response pairs (CRPs) only comes from physical randomness of silicon. Recently, a new type of PUF, touch screen-based UD-PUF was proposed, which entangles human user biometric variability with the silicon variability. Any silicon-based mobile device sensor which is a UI element can potentially seed such a UD-PUF. Having multiple orthogonal sensor space UD-PUFs helps robustness. If one UD-PUF behaves poorly in certain environmental conditions, another one might behave well. In voice UD-PUF, challenges are single words chosen by the user. The user speaks the challenge word into the microphone of the mobile device. The speech has natural human biometric variability. Raw microphone output data of analog to digital converter (ADC) also reflects the silicon variability. This voice microphone data sequence can be quantized into a binary sequence leading to a PUF—a physical randomness derived, unclonable function. To ensure reproducibility, a background noise reduction algorithm, a statistical error correction, and a frequency domain canonical representation are utilized. Both variability and reproducibility of this voice UD-PUF are evaluated. Several authentication algorithms are proposed in this paper. Pixel-matching authentication algorithm provides an average 15.33% false positive rate and an average 12.30% false negative rate while frame-count authentication algorithm provides an average 6.27% false positive rate and an average 13.23% false negative rate. For variability, we show 250+ bits Hamming distance, on average, between 512 bits binary responses of different (user, device, challenge) combinations. We also assess the pseudorandom number generation properties of voice UD-PUF by putting its binary responses through UMontreal TESTU01 suite of tests. The best voice UD-PUF algorithm passed all 26 randomness tests.

A Novel Task Recommendation Model for Mobile Crowdsourcing Systems

With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. However, how to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived. Based on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, the trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE datasets to verify the effectiveness of the proposed recommendation model.

Location Privacy Leakage through Sensory Data

Mobile devices bring benefits as well as the risk of exposing users’ location information, as some embedded sensors can be accessed without users’ permission and awareness. In this paper, we show that, only by using the data collected from the embedded sensors in mobile devices instead of GPS data, we can infer a user’s location information with high accuracy. Three issues are addressed which are route identification, user localization in a specific route, and user localization in a bounded area. The Dynamic Time Warping based technique is designed and we develop a Hidden Markov Model to solve the localization problem. Real experiments are performed to evaluate our proposed methods.

Crowdsensing: Socio-Technical Challenges and Opportunities

With the advancement in mobile technology, the sensing and computational capability of mobile devices is increasing. The sensors in mobile devices are being used in a variety of ways to sense and actuate. Mobile crowdsensing is a paradigm that involves ordinary people to participate in a sensing task. This sensing model has the capability to provide a new vision of people centric sensing as a service. This research work reviewed different domains utilizing mobile crowdsensing for solving different domain specific problems. Mobile crowd sensing model is also posing different socio-technical challenges which needs to be addressed. The research work reviewed and analyzed a variety of socio-technical challenges of mobile crowdsensing and possible solutions presented by different studies. There are different socio-technical challenges but the challenge of privacy in crowdsensing requires extra measures to realize the vision of mobile crowdsensing.

Indoor Positioning Method Based on Metameric White Light Sources and Subpixels on a Color Image Sensor

An indoor positioning method based on metameric white light sources and subpixels on a color image sensor is proposed in this paper. Metameric white light sources are obtained by merging multiple-color light-emitting diode (LED) chips with different intensity ratios. The output intensity ratios of subpixels on color image sensor at the receiver are employed to identify the respective metameric white light sources. Proof of concept experiments demonstrates that the average error of 2-D indoor positioning is 1.50 cm and 3-D indoor positioning is 3.58 cm. The indoor positioning system with the proposed method is cost effective and convenient because image sensors in nowadays available mobile devices can be utilized.