Device Owners Research Articles

Patient Practices, Perceptions, and Barriers to Self-measurement of Blood Pressure in a Rural Health System.

Self-measurement of blood pressure (SMBP) is endorsed by current guidelines for diagnosing and managing hypertension (HTN). We surveyed individuals in a rural healthcare system on practices and attitudes related to SMBP that could guide future practice. Survey questions were sent via an online patient portal to a random sample of 56,275 patients with either BP > 140/90 mm Hg or cardiovascular care in the system. Questions addressed home blood pressure (BP) monitor ownership, use, willingness to purchase, desire to share data with providers, perceptions of patient education, and patient-centeredness of care. Multivariable logistic regression was used to examine patient characteristics associated with SMBP behaviors. The overall response rate was 12%, and 8.4% completed all questions. Most respondents, 60.9%, owned a BP monitor, while 51.5% reported checking their BP at home the month prior. Among device owners, 45.1% reported receiving instructions on SMBP technique, frequency, and reading interpretation. Only 29.2% reported sharing readings with providers in the last 6 months, whereas 57.9% said they would be willing to do so regularly. Older age, female sex, and higher income were associated with a higher likelihood of device ownership. Younger age, lower income, and Medicaid insurance were associated with a greater willingness to share SMBP results with providers regularly. While a significant proportion of respondents performed SMBP regularly, many reported insufficient education on SMBP, and few shared their home BP readings with providers. Patient-centered interventions and telemedicine-based care are opportunities that emerged in our survey that could enhance future HTN care.

On Disused Connected Devices: Understanding Disuse, “Holding On,” and Barriers to Circularity

In this article, we explore the complex phenomena behind why people “hold on” to disused connected devices, focusing especially on differences between “traditional” smartphones and computers, and newer categories of smart home devices, wearables, and single-function Internet of Things (IoT) devices. We investigate why and in what contexts different categories of connected devices become disused by their owners; what owners value about their disused devices; and what they perceive to be the barriers to adopting circular practices, for example, by fixing, recycling, or reusing them. Our contribution is to provide a descriptive account of how functional, sentimental, and other values associated with devices shape owners’ perceptions and attitudes toward their “end of life,” for an expanded range of connected products. By highlighting how perceptions of concepts including convenience, ownership, and wastefulness mediate how owners approach the “end of life” of a device, we map the barriers for device owners to engage in more circular practices and highlight opportunities to address them through design. Our study replicates previous findings in the domain, as well as extending them, contributing to how the design of modern IoT devices leads to new barriers, opportunities, and considerations for more circular design.

Influencing Factor on School-Age Children’s Holistic Health Care Who Using Online Media by Working-Age Parents in Northeast Thailand

Background. Thailand tends to have more access to online media. In 2020, the northeastern region had the highest usage rate. Especially school-age children have the highest usage rate. If misused for a long time, the consequences can adversely affect health in many aspects. Their parents should closely supervise and monitor them for this health problem. However, parents have limitations in caring because they have to work a lot. The aim was to study factors that influence working-age parents in holistic health care (HHC) of school-age children using online media in northeast Thailand. Materials and Methods. This research was a descriptive study, with a sample of parent amount 222. The questionnaire performed an item-objective congruence (IOC) = 0.60–1.00, the content validity index (CVI) = 0.90, and the Cronbach’s alpha coefficient was 0.93. The data collected then include the characteristics, use of online media, health problems, and health care. Finally, data were analyzed using descriptive statistics, frequency, percentage, standard deviation, and multiple regressions. Result. The results of the study are factors that influenced HHC with statistical significance (p≤.001) including the child’s gender, the child’s age, the child’s class, the type of internet, activities, the device, and the device owner. In addition, it was holistic health problems of school-age children such as physical, emotional, social, and intellectual. Importantly, these could explain the variation for HHC of 21.2% (adjusted R2 = 0.212), and the standard error in the model estimation was 25.06 (SEE = 25.06). Conclusion. Factors that influence school-age children’s health care are beneficial to preventing and solving health problems by using online media. Parents or health care professionals can use it as a guide to develop more effective health care strategies and policies. Ultimately, it will help to promote better health for children in an ever-changing digital world.

User gait biometrics in smart ambient applications through wearable accelerometer signals: an analysis of the influence of training setup on recognition accuracy

Gait recognition can exploit the signals from wearables, e.g., the accelerometers embedded in smart devices. At present, this kind of recognition mostly underlies subject verification: the incoming probe is compared only with the templates in the system gallery that belong to the claimed identity. For instance, several proposals tackle the continuous recognition of the device owner to detect possible theft or loss. In this case, assuming a short time between the gallery template acquisition and the probe is reasonable. This work rather investigates the viability of a wider range of applications including identification (comparison with a whole system gallery) in the medium-long term. The first contribution is a procedure for extraction and two-phase selection of the most relevant aggregate features from a gait signal. A model is trained for each identity using Logistic Regression. The second contribution is the experiments investigating the effect of the variability of the gait pattern in time. In particular, the recognition performance is influenced by the benchmark partition into training and testing sets when more acquisition sessions are available, like in the exploited ZJU-gaitacc dataset. When close-in-time acquisition data is only available, the results seem to suggest re-identification (short time among captures) as the most promising application for this kind of recognition. The exclusive use of different dataset sessions for training and testing can rather better highlight the dramatic effect of trait variability on the measured performance. This suggests acquiring enrollment data in more sessions when the intended use is in medium-long term applications of smart ambient intelligence.

GEOSILENCE – LOCATION TRIGERRED DEVICE MUTING

Mobile devices have become an integral part of human lifestyle. The convenience offered by mobile devices usage can’t be denied. However, there are growing concerns about the disturbances caused by usage of mobile devices. One of the concerns is the disturbances caused by ringing sound of mobile devices at places that require silence such as mosque, library, cinema, and meeting room. Mobile device owners often forget to turn on silence mode on their devices in these places. To overcome such problem, this paper proposes an auto-notification application for mobile device using geofencing technique. Geofencing technique is used to detect virtual boundary that have been preset around required places. When a user enters the particular place, the application will detect that the user’s mobile device has crossed the virtual boundary and will automatically notify the user to turn the silence mode on the device. The opposite process will happen when the user crosses the boundary to exit the place. The main advantage of the application is that it will remind mobile device users to mute their devices when they are entering places that require silence. Nearly everything around us holds significance, be it data, expensive items, land, people and/or animals. It is essential that we keep all that matters to us safe and secure. To employ the highest form of security, we want to keep track of all the significant things on our own. This gives rise to the need for the meantime tracking o anything which is highly valuable to us. Hence, GPS tracking is employed in this technological solution to keep track. Innovative technology, based on telematics and satellite positioning, is the subject of this study on geofencing. With the use of geofencing technology, users can create virtual boundaries in the context of the actual world. By automatically detecting when monitored mobile items enter or leave geographical areas that are encircled by a virtual fence, it aids in the surveillance of those areas. A geofence is a virtual barrier that is set up around a specific region of interest utilizing several technologies, including Wi-Fi, cellular mobile, RFID, and GPS. A specific radius area on the map can be specified by the user. If any entry or exit is made from that location, they will be updated in real time. This assures the asset's safety and security and provides a system for inspection to deter any unwanted activity. Keyword : Auto notification,Cellular mobile, Geofencing, GPS, Mobile device, RFID, Window phone.

Hello me, meet the real me: Voice synthesis attacks on voice assistants

The radical advances in telecommunications and computer science have enabled a myriad of applications and novel seamless interactions with computing interfaces. Voice Assistants (VAs) have become the norm for smartphones, and millions of VAs incorporated in smart devices are used to control these devices in the smart home context. Previous research has shown that they are prone to attacks, leading vendors to implement countermeasures. One of these measures is to allow only a specific individual, the device's owner, to perform potentially dangerous tasks that may disclose personal information, involve monetary transactions, etc. To understand the extent to which VAs provide the necessary protection to their users, we experimented with two of the most widely used VAs, which the participants trained. We then utilised voice synthesis, using samples provided by participants, to synthesise commands that were used to trigger the corresponding VA and perform a dangerous task. Our extensive results showed that more than 30% of our audio synthesis attacks were successful and at least one successful attack for more than half of the participants. Moreover, they illustrate statistically significant variation among vendors and, in one case, even gender bias. The outcomes are rather alarming and require the deployment of further countermeasures to prevent exploitation, as the number of VAs in use is currently comparable to the world population.

IoT forensic analysis: A family of experiments with Amazon Echo devices

Criminal Investigations involving digital devices often focus on the analysis of mobile phones, tablets, and computers associated with the suspect or the victim. The connection of IoT devices to criminal investigations may not always be considered or understood by professionals handling the crime scene. The language, experience, and understanding needed to articulate the appropriate causes required to include IoT devices in search warrant affidavits are not always known or available to case agents overseeing the evidence-gathering portion of the investigation. For this reason, we introduce a novel methodology that shows how to locate and identify IoT device owner account information, device specifications and configurations, and the location of user activity on the device. We use this methodology to carry out a family of eight experiments on IoT devices that can assist law enforcement professionals in the construction of search warrant affidavits with information that will help satisfy the legal requirement to show evidence of a crime is likely contained on IoT devices. In this paper, we focus on Amazon Echo Show IoT devices and the legal justification for seizing and examining the devices, methods of extraction, and location of user-related artifacts on IoT device hardware. Overall, the implications of our study offer law enforcement professionals specific, practical instructions on how to deal with Amazon IoT devices involved in a crime scene. The analysis of data related to these devices is presented through practical demonstrations of these devices in action.

Private and Mutual Authentication Protocols for Internet of Things

The Internet of Things (IoT) consists of a range of devices that communicate with other devices that are surrounding them. Secure communications between IoT devices have been an essential requirement. However, most existing authentication protocols fail to provide privacy protection for devices such that sensitive information including device owner’s identity and device type can be leaked. In this paper, based on attribute-based encryption (ABE), we propose two private and mutual authentication protocols to protect the privacy of IoT devices. The first protocol is a three-message key exchange protocol, following the public-key encryption mode of the Internet Key Exchange (IKE). The second protocol is a one-round key exchange protocol, thereby providing simplicity and efficiency of messages communicated between the participants. Both of our protocols can be proved secure in the defined model. Finally, we implement the two protocols in an open source framework.

Why people replace their aging smart devices: A push–pull–mooring perspective

During the last decade, the Internet of Things (IoT) has become a central enabler for technological developments and services, such as ambient assisted living and localization services. Billions of smart devices have been sold, with many aged devices still in use today. In several cases, such aged smart devices do not receive security updates after some time of operation, making them a threat to the privacy of end-users. For this reason, it is crucial to understand driving factors for users to keep older devices as well as factors that lead to device switches, which can be considered as a security measure against cyber threats in this context.In this paper, we analyze what factors people associate with replacing older smart devices with newer ones as a way to mitigate the risks linked to aged smart devices. To achieve this, we apply the push-pull-mooring framework to integrate privacy, adoption and switching theories into a unified framework. To empirically validate the framework, we conducted an online survey among N=513 owners of older smart devices (i.e., purchased more than a year ago) from the UK through the Prolific platform. The results of our study show that perceived usefulness of new devices was strongly associated with switching intention. These results offer only limited support for technology adoption theories, as switching intention was not associated with other adoption constructs (pull factors). Privacy concern regarding improper access to personal information collected by an older smart device and switching costs (a push and a mooring factor, respectively) were also associated with switching costs. The results also indicate support for the moderating role of age of smart device, since the latter associations were not significant for smart devices up to three years old. We also provide some practical implications for manufacturers with a green and sustainable future in mind.

PETIoT: PEnetration Testing the Internet of Things

Attackers may attempt exploiting Internet of Things (IoT) devices to operate them unduly as well as to gather personal data of the legitimate device owners’. Vulnerability Assessment and Penetration Testing (VAPT) sessions help to verify the effectiveness of the adopted security measures. However, VAPT over IoT devices, namely VAPT targeted at IoT devices, is an open research challenge due to the variety of target technologies and to the creativity it may require. Therefore, this article aims at guiding penetration testers to conduct VAPT sessions over IoT devices by means of a new cyber Kill Chain (KC) termed PETIoT. Several practical applications of PETIoT confirm that it is general, while its main novelty lies in the combination of attack and defence steps. PETIoT is demonstrated on a relevant example, the best-selling IP camera on Amazon Italy, the TAPO C200 by TP-Link, assuming an attacker who sits on the same network as the device’s in order to assess all the network interfaces of the device. Additional knowledge is generated in terms of three zero-day vulnerabilities found and practically exploited on the camera, one of these with High severity and the other two with Medium severity by the CVSS standard. These are camera Denial of Service (DoS), motion detection breach and video stream breach. The application of PETIoT culminates with the proof-of-concept of a home-made fix, based on an inexpensive Raspberry Pi 4 Model B device, for the last vulnerability. Ultimately, our responsible disclosure with the camera vendor led to the release of a firmware update that fixes all found vulnerabilities, confirming that PetIoT has valid impact in real-world scenarios.

ISF: Security Analysis and Assessment of Smart Home IoT-based Firmware

The applications and services offered by the Internet of Things (IoT) have grown significantly during the past few years. Device makers and corporate suppliers have taken notice of this, which has led to a sudden inflow of new-age firms. Confidential data and information are involved as IoT device use rises. IoT device security has emerged as a major issue and is becoming more and more significant. Appropriate security measures are needed to prevent dangers and hazards associated with the adoption of smart technology in smart cities and houses that run IoT devices, according to security evaluations. In order to safeguard the smart home environment, our research focuses on IoT device firmware. The security methodology presented in this research may be used to analyze and investigate IoT firmware, revealing sensitive data and hardcoded user IDs and passwords that can be used in future attacks and breach of IoT devices. The authors put out an idea for how real-time datasets produced by IoT search engines may be analyzed using keywords according to different device kinds, locations, and manufacturers. The results showed that it took device owners 11–13 months to upgrade the firmware. Only HP and Cisco routinely provided firmware updates to protect IoT devices among IoT device makers.

Threat model of the testing subsystem for rare mobile device owners

Testing mobile applications for owners of rare mobile devices is a complex task because of the diversity of devices. The goal of this study is to create a threat model of the testing subsystem for owners of sparse mobile devices. The authors of the paper describe step by step the classes of users and the key components of the subsystem. The article defines the level of security and the dominant classes of subsystem vulnerabilities. The authors created an intruder model for the testing subsystem, defined the security class, and planned measures to protect the testing subsystem.

Digital body image of users of devices for self-tracking

The article presents the results of an empirical study implemented in a combined strategy in 2020–2021 and aimed at studying digital practices of healthcare of megalopolis residents. One of the focuses of interest was digital self-tracking, namely, the ways users interact with data supplied by personal devices, and the construction of a body image as a result of this process. The analysis of the data obtained allowed us to formulate several conclusions. Thus, for a significant part of active users of digital devices, the generated visual content, expressed in quantitative form, is of significant interest. There is an effect of access to the “real” body as well as the risks of a gap between personal feelings and digital data. The following arguments are put forward in favor of digital information: 1) the gadget can record indicators that are not available at the level of bodily sensations; 2) it generates content in ways that go beyond the capabilities of an individual. When there are significant discrepancies between digital data and subjective experience, as a rule, it is not about their opposition, but about the work of coordinating and “smoothing” information received from various sources. Data visualization forms an idea of the owners of digital devices about their physical condition and progress in achieving their goals. In some cases, the digital body image becomes a reason for communication on social networks and a way of self-presentation. For those who actively practice digital self-tracking, the body image is considered to be incomplete without information expressed in numerical form. Moreover, for feeling corporeality, the experience of vision turns out to be fundamentally significant today.

Protecting Smart Home from Cybersecurity Threats Strategies for Homeowners

Cyberthreat proliferation parallels the rapid surge in smart home usage. While having everything in one place is convenient, it also increases your home's vulnerability to cyber threats. Such an attack could result in bodily harm, the theft of sensitive information, or both. To mitigate the effects of these threats, owners of smart homes can make efforts to prevent cybercriminals from breaking into their premises starting by updating their firmware to the most recent version, creating secure passwords, and enabling two-factor authentication. Second, people should safeguard their gadgets by creating unique user IDs, disabling unneeded functions, and always keeping a tight eye on them. Finally, they must safeguard the facility where they conduct business by installing surveillance equipment, employing electronic locks, and restricting network access. Individuals must take these safeguards, but they must also stay informed about the most recent threats to home cybersecurity and the best strategies to combat them. Smart home device owners should become acquainted with the risks to which their devices are prone and ensure that their devices are updated to the most recent versions of all available software and security upgrades. Collaboration between homeowners, connected device manufacturers, and internet service providers is required to ensure the security of a smart home. Homeowners should research the security features available in smart home devices and only buy from reputable businesses that value consumer privacy and security. As the Internet of Things (IoT) expands and develops, a data privacy standard that meets the criteria of Data protection is in great demand. Safeguarding smart family apps necessitates a community agreement and specific permission from users to store their personal information in the product's database.

Residency and worker status identification based on mobile device location data

Mobile device location data (MDLD) have been widely recognized for their rich human mobility information and thus considered as a supplementary data source for the current travel data bank consisting of travel survey data and traffic monitoring data. However, the lack of ground truth information about the device owners raises concern about the biases and representativeness of the nonprobability MDLD sample and significantly limits the applications of MDLD. This paper focuses on identifying two important socio-demographic characteristics for the MDLD sample devices: residency and worker status, including four worker types (normal commuters, professional drivers, mobility-for-work workers, and nonworkers/home-based workers). Based on the spatial–temporal patterns of location sightings and derived trips from MDLD, a comprehensive imputation framework is proposed with parameters calibrated against public domain ground truth data. A national-level case study in the U.S. based on a commercial MDLD dataset further evaluates the performances of the proposed heuristic rules. The multi-level validation results indicate a satisfying match against the ground truth data and prove the effectiveness of the proposed methods. As one of the earliest efforts to identify the residency and worker status information for a large-scale national-level MDLD dataset, mobile workers—including professional drivers and mobility-for-work workers—are also identified from MDLD for the first time.

A Divide-and-Conquer Bilevel Optimization Algorithm for Jointly Pricing Computing Resources and Energy in Wireless Powered MEC.

This article investigates a wireless-powered mobile edge computing (MEC) system, where the service provider (SP) provides the device owner (DO) with both computing resources and energy to execute tasks from Internet-of-Things devices. In this system, SP first sets the prices of computing resources and energy whereas DO then makes the optimal response according to the given prices. In order to jointly optimize the prices of computing resources and energy, we formulate a bilevel optimization problem (BOP), in which the upper level generates the prices of computing resources and energy for SP and then under the given prices, the lower level optimizes the mode selection, broadcast power, and computing resource allocation for DO. This BOP is difficult to address due to the mixed variables at the lower level. To this end, we first derive the relationships between the optimal broadcast power and the mode selection and between the optimal computing resource allocation and the mode selection. After that, it is only necessary to consider the discrete variables (i.e., mode selection) at the lower level. Note, however, that the transformed BOP is still difficult to solve because of the extremely large search space. To solve the transformed BOP, we propose a divide-and-conquer bilevel optimization algorithm (called DACBO). Based on device status, task information, and available resources, DACBO first groups tasks into three independent small-size sets. Afterward, analytical methods are devised for the first two sets. As for the last one, we develop a nested bilevel optimization algorithm that uses differential evolution and variable neighborhood search (VNS) at the upper and lower levels, respectively. In addition, a greedy method is developed to quickly construct a good initial solution for VNS. The effectiveness of DACBO is verified on a set of instances by comparing with other algorithms.

“You offer privacy like you offer tea”: Investigating Mechanisms for Improving Guest Privacy in IoT-Equipped Households

IoT devices are becoming more common and prevalent in private households. Since guests can be present in IoT-equipped households, IoT devices can pose considerable privacy risks to them. In this paper, we present an in-depth evaluation of privacy protection for guests considering the perspectives of hosts and guests. First, we interviewed 21 IoT device owners about four classes of mechanisms obtained from the literature and social aspects. Second, we conducted an online survey (N=264) that investigates the perspective of guests in IoT-equipped households. From our results, we learn that protection mechanisms should not introduce privacy threats and require low resources. Further, hosts should keep control over their devices and the aesthetics of their living spaces. Guests, however, value feedback about the status of privacy protection which can interfere with aesthetics. Privacy protection should rather foster collaboration and not impact the visit of the guest too severely. We use our results to identify a design space for guest privacy protection in IoT-equipped households.

MDEFTL: Incorporating Multi-Snapshot Plausible Deniability into Flash Translation Layer

Conventional encryption solutions cannot defend against a coercive attacker who can capture the device owner, and force the owner to disclose keys used for decrypting sensitive data. To defend against such a coercive adversary, Plausibly Deniable Encryption (PDE) was introduced to allow the device owner to deny the very existence of sensitive data. The existing PDE systems built for computing devices equipped with flash storage media, are problematic, since they cannot defend against multi-snapshot adversaries, who may have access to the storage medium of a user's device at different points of time. In this article, we propose MDEFTL, a secure multi-snapshot PDE system for mobile devices which incorporates plausible deniability into Flash Translation Layer (FTL). MDEFTL is the first practical design which integrates multi-snapshot PDE into FTL, a pervasively deployed layer in literally all the current mobile devices. A salient advantage of MDEFTL lies in its capability of achieving multi-snapshot plausible deniability while being able to accommodate the special nature of NAND flash as well as eliminate deniability compromises from it. We implemented MDEFTL using an open-source NAND flash controller. The experimental results show that, compared to conventional encryption which does not provide deniability, our MDEFTL only incurs a small overhead.

Task Offloading in Wireless Powered Mobile Crowd Sensing: A Matching-Based Approach

Mobile crowd sensing (MCS) is a new sensing paradigm that leverages participatory sensing data from mobile devices for accomplishing large-scale sensing tasks. Incentivizing device owners to contribute high-quality sensing data is a prerequisite for the success of MCS services. In this paper, we first propose a pre-contracting incentive mechanism that involves the participation of not only the device owners located in close proximity to Point of Interests (PoIs) but also the device owners that are going to pass through those locations. Furthermore, the quality of sensing data is guaranteed through the use of redundancy. In particular, sensing data from multiple device owners is processed and compared at an edge side (i.e., base station) so as to detect the measurement error at the proximity of data sources. Simulation results confirm that the proposed incentive mechanism is efficient in terms of improving the total utility.

A hybrid trust computing approach for IoT using social similarity and machine learning.

Every year, millions of new devices are added to the Internet of things, which has both great benefits and serious security risks for user data privacy. It is the device owners’ responsibility to ensure that the ownership settings of Internet of things devices are maintained, allowing them to communicate with other user devices autonomously. The ultimate goal of the future Internet of Things is for it to be able to make decisions on its own, without the need for human intervention. Therefore, trust computing and prediction have become more vital in the processing and handling of data as well as in the delivery of services. In this paper, we compute trust in social IoT scenarios using a hybrid approach that combines a distributed computation technique and a global machine learning approach. The approach considers social similarity while assessing other users’ ratings and utilize a cloud-based architecture. Further, we propose a dynamic way to aggregate the different computed trust values. According to the results of the experimental work, it is shown that the proposed approaches outperform related work. Besides, it is shown that the use of machine learning provides slightly better performance than the computing model. Both proposed approaches were found successful in degrading malicious ratings without the need for more complex algorithms.