Generating Device Fingerprints for Smart Device Pairing Using the Unique Spectrum Characteristic From LEDs

2022 ◽  
pp. 111-124
Author(s):  
Md Imran Hossen ◽  
Md Abdullah Al Momin ◽  
Xiali Hei
Keyword(s):  
Unique Feature ◽  
Spectral Characteristics ◽  
Smart Devices ◽  
Smart Device ◽  
Spectrum Characteristic ◽  
Led Lights ◽  
Device Pairing

Currently, the vast majority of smart devices with LEDs are on the rise. It has been observed that the lights emitted by each LED have unique spectral characteristics. Despite the fact that there are a number of methods out there to generate fingerprints, none seem to explore the possibility of generating fingerprints using this unique feature. In this chapter, the method to perform device fingerprinting using the unique spectrum emitted from the LED lights is discussed. The generated fingerprint is then used in device pairing.

P577Early experience with the first pacemakers to directly connect with smart devices for remote monitoring

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
K Tarakji ◽  
S Zweibel ◽  
A Seiler ◽  
P Roberts ◽  
N Shaik ◽  
...  
Keyword(s):  
Remote Monitoring ◽  
Communication Process ◽  
Smart Devices ◽  
Smart Device ◽  
Cardiac Device ◽  
Remote Monitoring System ◽  
Wide Range ◽  
Device Pairing ◽  
Implanted Device

Abstract Background Remote monitoring is associated with improved patient outcomes; however, adoption and adherence to remote monitoring via home-based consoles remains suboptimal. BlueSync technology in new generation pacemaker and CRT-P models enables the implanted device to communicate directly with patient-owned, Bluetooth-equipped smartphones/tablets and an app (MyCareLink Heart). The app can automatically retrieve information from the cardiac device and transmit the data to the remote network, eliminating the need for traditional remote monitoring consoles. Objectives To characterize the communication process between implanted pacemakers and smart device remote monitoring apps by assessing the success of prescheduled remote transmissions in the first month of follow-up. Additionally, to assess the feedback of both patients and clinicians about the process of device pairing. Methods Enrollment in the BlueSync Field Evaluation began in April 2018 and was completed November 2018. Follow-up is ongoing. Prior to enrollment in the evaluation, patients completed the device pairing process with the app using their own compatible smartphone or tablet. Patient and clinician questionnaires were completed at the time of the device pairing process. After enrollment, successful completion of scheduled transmissions occurring in the first month were analyzed. Results Preliminary data includes 241 enrolled patients with mean age of 64.7±15.5 yrs (min 20, max 90 yrs), who completed device pairing between their implanted device and their smart device app. Of enrolled patients, 79% felt that the device paring was easy to do, 85% were satisfied with the amount of time it took to complete it, and 93% felt that they would be comfortable using the app. Clinicians reported that 67% of the device pairings took less than 20 minutes and 78% felt patients would be able to use the app independently. At the time of analysis 174 patients had at least one scheduled transmission within the first month, and collectively had a total of 322 scheduled transmissions. Out of these, 309 (96%, 95% CI: 93%-98%) were successfully completed. MyCareLink Heart App Conclusions Initial experience with the world's first app based remote monitoring system for Bluetooth enabled pacemakers demonstrated success to scheduled transmissions in the first month across a wide range of patient ages. Patients and clinicians reported high satisfaction with this novel technology. Acknowledgement/Funding Medtronic PLC

scholarly journals Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2347
Author(s):  
Yanyan Wang ◽  
Lin Wang ◽  
Ruijuan Zheng ◽  
Xuhui Zhao ◽  
Muhua Liu
Keyword(s):  
Queue Length ◽  
Optimization Problem ◽  
Time Slot ◽  
Smart Homes ◽  
Back Pressure ◽  
Smart Devices ◽  
Smart Device ◽  
Simulation Results ◽  
Computational Offloading ◽  
The Stability

In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.

scholarly journals The Use of Smart Devices by Care Providers in Emergency Departments: A Cross-Sectional Study (Preprint)

2019 ◽  
Author(s):  
Mohamad Alameddine ◽  
Hussein Soueidan ◽  
Maha Makki ◽  
Hani Tamim ◽  
Eveline Hitti
Keyword(s):  
Health Center ◽  
Healthcare Providers ◽  
Target Population ◽  
Academic Health Center ◽  
Care Quality ◽  
Smart Devices ◽  
Smart Device ◽  
Care Providers ◽  
Cross Sectional ◽  
Academic Health

BACKGROUND The use of smart devices (SD) by healthcare providers in care settings is a common practice nowadays. Such use is not restricted to applications related to the care of patients but often extends to personal calls and applications with frequent prompts and interruptions. This enhances the risk of distractions caused by SD in the hospital settings and raises concerns on service quality and patient safety. Such concerns are exacerbated in complex care settings like the Emergency Department (ED). OBJECTIVE This study measured the frequency and patterns of SD use among healthcare providers in the ED of a large academic health center in Lebanon. The perceived consequences of care providers on using SDs on the provider-provider communication and the care quality of patients in ED were further assessed. The study further examined the factors associated with the use of smart devices and measured the approval for regulating such use. METHODS The study was carried at the ED of an academic health center in Lebanon. The ED received the highest volume of patient visits in the country. Data was collected using a cross-sectional electronic survey sent to all ED healthcare providers (n=236). The target population included core ED faculty members, attending physicians, residents, medical students, and the nursing care providers. RESULTS Half of the target population responded to the questionnaire. A total of 85.6% of the respondents use one or more medical applications on their smart devices. The respondents believed that using the SD in the ED improved the coordination among the care team (81.6%) and that it was beneficial to patient care (78.9%). In addition, 41.1% of the respondents acknowledged they were distracted when using their SD for non-work purposes. Furthermore, 54.8% of the respondents acknowledged having witnessed their colleagues committed a near miss or an error due to the smart device-caused distractions. Regression analysis revealed that age and missing information due to using the SD are major predictors of committing an error at the ED (p<0.05). Interestingly, more than 40% of the respondents were significantly addicted to using SD and more than third of them felt the need to cut down on such use. CONCLUSIONS The findings of this study make it imperative to safeguard the safety and wellbeing of patients, particularly in high intensity, high volume department such as the ED. Irrespective of the positive role the SD play in the healthcare process, the negative effects of its use mandate proper regulation. This is an ethical mandate taking into consideration the important consequences such use may have on care processes and outcomes.

The Negative Effects of Using Smart Devices on the Physical and Psychological Health of Palestinian Children aged (13-16) years from their Perspective

2020 ◽  
Vol 8 (3) ◽  
pp. 01-19
Author(s):  
Nuha Iter
Keyword(s):  
Psychological Health ◽  
Smart Devices ◽  
Smart Device ◽  
Negative Effects ◽  
Physical And Psychological Health ◽  
Palestinian Children ◽  
Open Question ◽  
High Degree ◽  
The Relationship ◽  
Descriptive Method

The study aimed to explore the negative effects of using smart devices on the physical and psychological health of children aged (13-16) years from their perspective. The study was applied to a random sample of children aged (13-16), consisting of (102) male and female students. The descriptive method was used to answer the study questions, and a questionnaire was developed to collect data, which contains (3) sections, first section asked about the most used and preferred devices by children aged (13-16) years, and the number of hours the child used the smart device, the second one asked about the negative effects of using the smart devices on the physical and psychological health of children aged (13-16) years from their perspective, and the third section is an open question to know other negative effects of using the smart devices on the physical and psychological health of children aged (13-16) years. The study achieved a set of results, such as the smartphones are the most used and preferred devices by children aged (13-16) years, where (57%) of the study sample preferred to use, and there is  (86.3%) of children aged (13-16) use these devices at average from 4 up to 6 hours daily.  The responders highly agreed upon the negative effects of the use of smart devices on the physical health with average (4.2); which is a high degree, also the responders highly agreed upon the negative effects of  the use of smart devices on the physiological health with average  is  (3.73) which is also high,  added there are other effects caused by the use of smart devices for long hours on  children aged (13-16); the low rate of family discussions, and causes the low writing skills for child.   Depending on the results of the study, the researcher recommends that:  researchers should conduct a correlative study to know the relationship between the effects and the number of hours of daily use of devices; families should rationalize the use of smart devices.

Human-Robot Interaction Design Using Smart Device Based Robot Partner

2019 ◽  
pp. 119-140
Author(s):  
Jinseok Woo ◽  
Naoyuki Kubota
Keyword(s):  
Nonverbal Communication ◽  
Interaction Design ◽  
Human Robot Interaction ◽  
Smart Devices ◽  
Smart Device ◽  
Design Support ◽  
Motion Patterns ◽  
Software Modules ◽  
Content Design ◽  
Design Support Environment

Nowadays, various robot partners have been developed to realize human-friendly interactions. In general, a robot system is composed of hardware modules, software modules, and application contents. It takes much time to design utterance contents and motion patterns as application contents simultaneously, but the design support systems mainly focus on the generation of robot motion patterns. Furthermore, a methodology is needed to easily change the specification of hardware and software according to diversified needs, and the developmental environment to design the application contents on verbal and nonverbal communication with people. In this paper, the authors propose robot partners with the modularized architecture of hardware and software by using smart devices, and propose a developmental environment to realize easy contents design of verbal and nonverbal communication. In order to solve the problem of difficulty in the content design, they develop a design support environment using design templates of communication application contents. Next, they apply the robot partner to navigate visitors to the robot contest of the system design forum held in Tokyo Metropolitan University. Finally, they show several examples of the interaction cases, and discuss the interaction design for smart device based robot partners.

Next Generation Wearable Devices

2014 ◽  
Vol 6 (2) ◽  
pp. 25-51 ◽  
Author(s):  
Niraj Shakhakarmi
Keyword(s):  
Health Monitoring ◽  
Service Management ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Smart Devices ◽  
Smart Device ◽  
Monitoring Device ◽  
Next Generation ◽  
Spectrum Utilization ◽  
Smart Health

The next generation wearable devices are Smart health monitoring device and Smart sousveillance hat which are capable of using wearable sensors for measuring physiological information, sousveillanace, navigation, as well as smart device to smart device communications over cellular coverage. Smart health monitoring device collect and observe different health related information deploying biosensors and can predict health problems. Smart sousveillance hat provides the brainwaves based fatigue state, training and sousveillance around the wearer. The next generation wearable smart devices deploy the device to device communications in LTE assisted networks with D2D server, D2D Application server and D2D enhanced LTE signalling for D2D service management, spectrum utilization and broad cellular coverage, which make them portable, social, commercial and sustainable. Thus, the wearable device technology will merge with the smart communications besides the health and wellness. Furthermore, the simulation and performance evaluation shows that LTE-D2D wearable smart device communications provides two times more energy efficiency than LTE-UEs cellular communications. The LTE-D2D data rate is also found significantly higher with higher D2D-SINR for lower relative mobility (= 30m/s) and lower D2D distance (<400m) between devices.

scholarly journals Robot Partner Development Platform for Human-Robot Interaction Based on a User-Centered Design Approach

2020 ◽  
Vol 10 (22) ◽  
pp. 7992
Author(s):  
Jinseok Woo ◽  
Yasuhiro Ohyama ◽  
Naoyuki Kubota
Keyword(s):  
System Integration ◽  
Industrial Sector ◽  
Human Robot Interaction ◽  
Service Robots ◽  
Smart Devices ◽  
Smart Device ◽  
User Centered Design ◽  
Human Communication ◽  
Robot Interaction ◽  
Development Platform

This paper presents a robot partner development platform based on smart devices. Humans communicate with others based on the basic motivations of human cooperation and have communicative motives based on social attributes. Understanding and applying these communicative motives become important in the development of socially-embedded robot partners. Therefore, it is becoming more important to develop robots that can be applied according to needs while taking these human communication elements into consideration. The role of a robot partner is more important in not only on the industrial sector but also in households. However, it seems that it will take time to disseminate robots. In the field of service robots, the development of robots according to various needs is important and the system integration of hardware and software becomes crucial. Therefore, in this paper, we propose a robot partner development platform for human-robot interaction. Firstly, we propose a modularized architecture of robot partners using a smart device to realize a flexible update based on the re-usability of hardware and software modules. In addition, we show examples of implementing a robot system using the proposed architecture. Next, we focus on the development of various robots using the modular robot partner system. Finally, we discuss the effectiveness of the proposed robot partner system through social implementation and experiments.

Realizing IoE for Smart Service Delivery

2020 ◽  
pp. 186-215
Author(s):  
Umar Mahmud ◽  
Shariq Hussain ◽  
Arif Jamal Malik ◽  
Sherjeel Farooqui ◽  
Nazir Ahmed Malik
Keyword(s):  
Industrial Revolution ◽  
Detailed Comparison ◽  
Smart Devices ◽  
Smart Device ◽  
Context Aware ◽  
Museum Guide ◽  
Guide System ◽  
Smart Service ◽  
Comparison Table

Widespread use of numerous hand-held smart devices has opened new avenues in computing. Internet of things (IoT) is the next big thing resulting in the 4th industrial revolution. Coupling IoT with data collection, storage, and processing leads to Internet of everything (IoE). This work outlines the concept of smart device and presents an IoE ecosystem. Characteristics of IoE ecosystem with a review of contemporary research is also presented. A comparison table contains the research finding. To realize IoE, an object-oriented context aware model is presented. This model is based on Unified Modelling Language (UML). A case study of a museum guide system is outlined that discusses how IoE can be implemented. The contribution of this chapter includes review of contemporary IoE systems, a detailed comparison, a context aware IoE model, and a case study to review the concepts.

scholarly journals Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

2019 ◽  
Vol 2019 ◽  
pp. 1-26 ◽  
Author(s):  
Mohammad Masoud ◽  
Yousef Jaradat ◽  
Ahmad Manasrah ◽  
Ismael Jannoud
Keyword(s):  
Machine Learning ◽  
Electronic Devices ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Smart Device ◽  
Internet Of Everything ◽  
Data Collection And Analysis ◽  
Machine Learning Applications ◽  
Management Operation

Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.

Crafting Strategies of Security Breaches

2022 ◽  
pp. 214-234
Author(s):  
Heru Susanto ◽  
Nurul Mardhiah ◽  
Alifya Kayla Shafa Susanto
Keyword(s):  
Business Model ◽  
Cyber Security ◽  
Smart Devices ◽  
Smart Device ◽  
Technological Advancement ◽  
Security Measures ◽  
Security Breaches ◽  
Cyber Threats ◽  
Cross Platform ◽  
Financial Technology

In recent years, the number of financial technology players and users have increased at a significant rate due to the rapid technological advancement in financial technology. While smart devices are providing more useful features to users, they have also made it possible for cyber threats to migrate from desktops to smart devices. Thus, it is important for smart device users to be aware that their device could be exposed to cyber threats and that users could protect their devices by employing data-centric cyber security measures. This study reveals how financial technology business model responded to the breach phenomenon by employing data-centric protection approaches. The result is very interesting. Data-centric security is very needed as it is capable of protecting data as a whole. It provides a gapless protection, meaning to say, the data are encrypted and classified wherever it moves. With persistent protection and cross-platform operability, data-centric security will eliminate gaps and keep data protected.

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