Search results

The purpose of this study is to investigate the effect of internal and external factors on the accuracy and consistency of the data provided by mobile-embedded micro-electromechanical system (MEMS) pressure sensors based on smartphones currently in use.

For this purpose, sensor type and smartphone model have been regarded as internal factors, whereas temperature, location and usage habits have been considered as external factors. These factors have been investigated by examining data sets provided by sensors from 14 different smartphones. In this context, internal factors have been analyzed by implementing accuracy assessment processes for three different smartphone models, whereas external factors have been evaluated by analyzing the line charts which present timely pressure changes.

The study outlined that the sensor data at different sources have different characteristics due to the affecting parameters. Even if the pressure sensors are used under similar circumstances, data of these sensors have inconsistencies because of the sensor drift originated by internal factors. This study concluded that it was not applicable to provide a common correction coefficient for pressure sensor data of each smartphone model. Therefore, relative data (pressure differences) should be taken into consideration rather than absolute data (pressure values) when developing mobile applications using sensor data.

Results of this study can be used as the guideline for developing mobile applications using MEMS pressure sensors. One of the main finding of this paper is promoting the use of relative data (pressure differences) rather than absolute data (pressure values) when developing mobile applications using smartphone-embedded sensor data. This significant result was proved by examinations applied with in the study and can be applied by future research studies.

Existing studies mostly evaluate the use of MEMS pressure sensor data obtained from limited number of smartphone models. As each smartphone model has a specific technology, factors affecting the sensor performances should be identified and analyzed precisely in terms of smartphone models for providing extensive results. In this study, five smartphone models were used fractionally. In this context, they were used for examining the common effects of the factors, and detailed accuracy assessments were applied by using two high-tech smartphones in the market.

The purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.

This research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.

The methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.

The research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.

The societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.

This research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.

Currently, ubiquitous smartphones embedded with various sensors provide a convenient way to collect raw sequence data. These data bridges the gap between human activity and multiple sensors. Human activity recognition has been widely used in quite a lot of aspects in our daily life, such as medical security, personal safety, living assistance and so on.

To provide an overview, the authors survey and summarize some important technologies and involved key issues of human activity recognition, including activity categorization, feature engineering as well as typical algorithms presented in recent years. In this paper, the authors first introduce the character of embedded sensors and dsiscuss their features, as well as survey some data labeling strategies to get ground truth label. Then, following the process of human activity recognition, the authors discuss the methods and techniques of raw data preprocessing and feature extraction, and summarize some popular algorithms used in model training and activity recognizing. Third, they introduce some interesting application scenarios of human activity recognition and provide some available data sets as ground truth data to validate proposed algorithms.

The authors summarize their viewpoints on human activity recognition, discuss the main challenges and point out some potential research directions.

It is hoped that this work will serve as the steppingstone for those interested in advancing human activity recognition.

For a good number of Indians, their smartphone is their first digital computing device. They have less experience in dealing with the Internet-enabled device and hence less experience in handling security threats like malware as compared to users of other countries who have gone through the learning curve of handling such security threats using other Internet-enabled devices such as laptop and desktop. Because of this, the inexperienced Indian smartphone user may be vulnerable to Internet-related security breaches, as compared to the citizens of developed economies. Hence, it is essential to understand the attitude, behaviour and security practices of smartphone users in India. Limited research is available about the security behaviour of smartphone users in India as the majority of research in this domain is done outside India.

In this empirical study, the researchers identified 28 cybersecurity behaviours and practices through a survey of relevant literature. An online survey of identified cybersecurity behaviours and practices was administered to 300 smartphone users. Frequency analysis of the respondent data was done to understand the adoption of recommended cybersecurity behaviours and practices. Pearson’s chi-square with 5% level of significance has been used to test the hypotheses. Post hoc analysis with Bonferroni correction was conducted for statistically significant associations.

Overall, the respondents did not exhibit good cybersecurity behaviour. Respondents have adopted some of the most popular security features of the smartphone such as the use of screen lock. However, respondents have not adopted or are not aware of the technical security controls such as encryption and remote wipe. Statistically significant differences were found between the cybersecurity behaviour and practices and independent variables such as gender, age, mobile operating system (OS) and mother tongue. Respondents reported high level of motivation to protect their device and data, whereas they reported moderate level of threat awareness and the ability to protect to their device and data. Results of the comparative analysis with a similar study in China and the USA are also reported in this study.

The main limitations of this study are as follows: the respondents' perceptions about their cybersecurity behaviours and practices were measured as opposed to their actual behaviours and practices and the generalizability of the study is limited because the sample size is small as compared to the total number of smartphone users in India.

The findings of this study may be useful for the design of effective cybersecurity prevention and intervention programs for general smartphone users of India.

This study provides an insight about cybersecurity behaviour of smartphone users in India. To the knowledge of the researchers, this is the first study to collect such quantitative data of smartphone users in India for a better understanding of the cybersecurity behaviours and practices. This study identified 28 cybersecurity behaviours and practices, which smartphone users should follow to improve cybersecurity.

This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East.

A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems.

The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft.

Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future.

Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement.

Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk.

The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.

This paper aims to discuss the privacy and security concerns that have risen from the permissions model in the Android operating system, along with two shortcomings that have not been adequately addressed.

The impact of the applications’ evolutionary increment of permission requests from both the user’s and the developer’s point of view is studied, and finally, a series of remedies against the erosion of users’ privacy is proposed.

The results of this work indicate that, even though providing access to personal data of smartphone users is by definition neither problematic nor unlawful, today’s smartphone operating systems do not provide an adequate level of protection for the user’s personal data. However, there are several ideas that can significantly improve the situation and mitigate privacy concerns of users of smart devices.

The proposed approach was evaluated through an examination of the Android’s permission model, although issues arise in other operating systems. The authors’ future intention is to conduct a user study to measure the user’s awareness and concepts surrounding privacy concerns to empirically investigate the above-mentioned suggestions.

The proposed suggestions in this paper, if adopted in practice, could significantly improve the situation and mitigate privacy concerns of users of smart devices.

The recommendations proposed in this paper would strongly enhance the control of users over their personal data and improve their ability to distinguish legitimate apps from malware or grayware.

This paper emphasises two shortcomings of the permissions models of mobile operating systems which, in authors’ view, have not been adequately addressed to date and propose an inherent way for apps and other entities of the mobile computing ecosystem to commit to responsible and transparent practices on mobile users’ privacy.

The purpose of this paper is to examine the effect of smartphones and computers as web survey entry response devices on the quality of responses in different question formats and across different survey invitations delivery modes. The respondents’ preference of device and the response immediacy were also compared.

Two field experiments were conducted with a cluster sampling and a census of all students in a public university in the USA.

Device effect on response quality was only found when using computer-aided self-interviews, but not in e-mail delivered web surveys. Even though the computer was the preferred device, but the smartphone’s immediate response was significantly higher than the computer.

The sample was restricted to college students who are more proficient users of smartphones and have high access to computers. But the direct comparison in the two studies using the same population increases the internal validity of the study comparing different web survey delivery modes.

Because of the minor differences in device on response quality, researchers can consider using more smartphones for field work such as computer-aided self-interviews to complement e-mail delivered surveys.

This is the first study that compares the response device effects of computer-aided self-interviews and e-mailed delivered web surveys. Because web surveys are increasingly used and various devices are being used to collect data, how respondents behave in different devices and the strengths and weaknesses of different methods of delivery survey help researchers to improve data quality and develop effective web survey delivery and participant recruitment.

The purpose of this paper is to present a smartphone-based physical access control system in which the access points are not directly connected to a central authorization server, but rather use the connectivity of the mobile phone to authorize a user access request online by a central access server. The access points ask the mobile phone whether a particular user has access or not. The mobile phone then relays such a request to the access server or presents an offline ticket. One of the basic requirements of our solution is the independence from third parties like mobile network operators, trusted service managers and handset manufacturers.

The authentication of the smartphone is based on public key cryptography. This requires that the private key is stored in a secure element or in a trusted execution environment to prevent identity theft. However, due to the intended independence from third parties, subscriber identity module (SIM)-based secure elements and embedded secure elements (i.e. separate hardware chips on the handset) were not an option and only one of the remaining secure element architectures could be used: host card emulation (HCE) or a microSD-based secure element.

This paper describes the implementation of such a physical access control system and discusses its security properties. In particular, it is shown that the HCE approach cannot solve the relay attack under conservative security assumptions and an implementation based on a microSD secure element is presented and discussed. Moreover, the paper also describes an offline solution which can be used if the smartphone is not connected to the access server. In this case, an access token is sent to the access point in response to an access request. These tokens are renewed regularly and automatically whenever the smartphone is connected.

In this paper, a physical access control system is presented which operates as fast as existing card-based solutions. By using a microSD-based secure element (SE), the authors were able to prevent the software relay attack. This solution is not restricted to microSD-based SEs, it could also be implemented with SIM-based or embedded secure elements (with the consequence that the solution depends on third parties).

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

The purpose of the study is to highlight the potential of the sensor based smartphone in assessing the covid-19 cases. Coronavirus disease 2019 (COVID-19) is a noxious pandemic affecting the respiratory system of the human and leading to the severe acute respiratory syndrome, sometimes causing death. COVID-19 is a highly transmittable disease that spreads from an infected person to others. In this regard, a smart device is required to monitor the COVID-19 infected patients by which widespread pandemic can be reduced.

In this paper, an electrochemical sensor-enabled smartphone has been developed to assess the COVID-19 infected patients. The data-enabled smartphone uses the Internet of Things (IoT) to share the details with the other devices. The electrochemical sensor enables the smartphone to evaluate the ribonucleic acid (RNA) of COVID-19 without the nucleic acid and feeds the data into the data server by using a smartphone.

The obtained result identifies the infected person by using the portable electrochemical sensor-enabled smartphone, and the data is feed into the data server using the IoT. Whenever an infected person moves outside the restricted zone, the data server gives information to the concerned department.

The developed electrochemical sensor-enabled smartphone gives an accuracy of 81% in assessing the COVID-19 cases. Thus, through the developed approach, a COVID-19 infected person can be identified and the spread can be minimized.