scholarly journals AI-Aided Individual Emergency Detection System in Edge-Internet of Things Environments

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2374
Author(s):  
Taehun Yang ◽  
Sang-Hoon Lee ◽  
Soochang Park
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Detection System ◽  
Learning Technologies ◽  
Detection Mechanism ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
User Behaviors ◽  
Localization Scheme ◽  
Smart Mobile

Recently, many disasters have occurred in indoor places. In order to rescue or detect victims within disaster scenes, vital information regarding their existence and location is needed. To provide such information, some studies simply employ indoor positioning systems to identify each mobile device of victims. However, their schemes may be unreliable, since people sometimes drop their mobile devices or put them on a desk. In other words, their methods may find a mobile device, not a victim. To solve this problem, this paper proposes a novel individual monitoring system based on edge intelligence. The proposed system monitors coexisting states with a user and a smart mobile device through a user state detection mechanism, which could allow tracking through the monitoring of continuous user state switching. Then, a fine-grained localization scheme is employed to perceive the precise location of a user who is with a mobile device. Hence, the proposed system is developed as a proof-of-concept relying on off-the-shelf WiFi devices and reusing pervasive signals. The smart mobile devices of users interact with hierarchical edge computing resources to quickly and safely collect and manage sensing data of user behaviors with encryption by cipher-block chaining, and user behaviors are analyzed via the ensemble paradigm of three machine learning technologies. The proposed system shows 98.82% prevision for user activity recognition, and 96.5% accuracy for user localization accuracy is achieved.

scholarly journals LOCATE-US: Indoor Positioning for Mobile Devices Using Encoded Ultrasonic Signals, Inertial Sensors and Graph-Matching

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1950
Author(s):  
David Gualda ◽  
María Carmen Pérez-Rubio ◽  
Jesús Ureña ◽  
Sergio Pérez-Bachiller ◽  
José Manuel Villadangos ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Acoustic Waves ◽  
Graph Matching ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Positioning Systems

Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (U-LPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others.

THE APPLICATIONS OF SMART MOBILE DEVICE FOR DETECTING BALANCE DYSFUNCTION IN INDIVIDUALS WITH DOWN SYNDROME

2018 ◽  
Vol 30 (01) ◽  
pp. 1850007
Author(s):  
Tsung-Hsun Hsieh ◽  
Chih-Wei Peng ◽  
Kai-Yun Chen ◽  
Ying-Zu Huang ◽  
Yi-Huang Lin ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Mobile Devices ◽  
Mobile Device ◽  
Neurological Disorders ◽  
Fall Risk ◽  
Force Plate ◽  
Balance Testing ◽  
Neurological Patients ◽  
Smart Mobile ◽  
Balance Functions

Falls are a major health concern leading cause of fatal and non-fatal injuries for neurological disorders. Balance dysfunction is one of the common factors to determine fall risk in neurological patients. Preventative measures may help to reduce the incidence and severity of falls for detecting balance function and fall risk factors. However, the objective measures for balance require expensive equipment with the assessment of clinical expertise. A main gap remains in the evaluation method to objectively characterize the balance functions in individuals with high risk of falling. With the development of wearable and mobile devices, recent advances in smart mobile devices may provide a potential opportunity to manage the gap in the detailed quantification of balance impairments. The purpose of this study is to identify whether the biomechanical data measured by the mobile device is reliable to characterize the posture stability in various balance test conditions. A total of 39 children with Down syndrome completed four balance-testing tasks under altered base of support and vision. Simultaneous biomechanical measurements were gathered from the iPod and force plate analysis system during functional balance testing. The force plate and mobile system provided similar patterns of stability across groups. Correlation ([Formula: see text] between two systems for path length, 95% ellipse area, peak-to-peak, standard deviation and mean ranged from 0.60 to 0.99. We expect that the smart mobile device can provide reliable and accurate information to quantify the postural stability in individuals with elderly people or neurological disorders. The objectivity, portability and easy use of such mobile device make it ideal to apply in clinical environments for detecting balance functions and reducing the risk of falls in Down syndrome or other neurological patients.

scholarly journals The Implementation of Augmented Reality Applications in Education

2018 ◽  
pp. 1
Author(s):  
Kalliopi Kanaki ◽  
Nikolaos D. Katsali
Keyword(s):  
High School ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Mobile Device ◽  
Vocational High School ◽  
Global Spread ◽  
Smart Mobile ◽  
Smart Mobile Devices

In this paper, we present augmented reality applications implemented by students and teachers of the 5th Vocational High School of Heraklion in Crete, within the context of informatics courses. The applications aim to enhance the traveling experience of the visitors of Heraklion city, exploiting the global spread of smart mobile devices in contemporary societies and the facilities they provide. The whole project was accomplished in a collaborative manner and focused on the provision of information about museums and monuments of Heraklion city. The applications have to be installed on the smart mobile device of the user.

scholarly journals Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks

2020 ◽  
Vol 10 (23) ◽  
pp. 8482
Author(s):  
Konstantinos Peppas ◽  
Apostolos C. Tsolakis ◽  
Stelios Krinidis ◽  
Dimitrios Tzovaras
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Real Time ◽  
Mobile Devices ◽  
Mobile Device ◽  
Classification Performance ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Smart Mobile ◽  
Fully Connected

Given the ubiquity of mobile devices, understanding the context of human activity with non-intrusive solutions is of great value. A novel deep neural network model is proposed, which combines feature extraction and convolutional layers, able to recognize human physical activity in real-time from tri-axial accelerometer data when run on a mobile device. It uses a two-layer convolutional neural network to extract local features, which are combined with 40 statistical features and are fed to a fully-connected layer. It improves the classification performance, while it takes up 5–8 times less storage space and outputs more than double the throughput of the current state-of-the-art user-independent implementation on the Wireless Sensor Data Mining (WISDM) dataset. It achieves 94.18% classification accuracy on a 10-fold user-independent cross-validation of the WISDM dataset. The model is further tested on the Actitracker dataset, achieving 79.12% accuracy, while the size and throughput of the model are evaluated on a mobile device.

scholarly journals An Authentication Scheme Based on Novel Construction of Hash Chains for Smart Mobile Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Qinglong Huang ◽  
Haiping Huang ◽  
Wenming Wang ◽  
Qi Li ◽  
Yuhan Wu
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Research Work ◽  
Hash Functions ◽  
Authentication Scheme ◽  
Hash Chain ◽  
Cryptographic Primitive ◽  
One Time Password ◽  
Smart Mobile ◽  
Smart Mobile Devices

With the increasing number of smart mobile devices, applications based on mobile network take an indispensable role in the Internet of Things. Due to the limited computing power and restricted storage capacity of mobile devices, it is very necessary to design a secure and lightweight authentication scheme for mobile devices. As a lightweight cryptographic primitive, the hash chain is widely used in various cryptographic protocols and one-time password systems. However, most of the existing research work focuses on solving its inherent limitations and deficiencies, while ignoring its security issues. We propose a novel construction of hash chain that consists of multiple different hash functions of different output lengths and employ it in a time-based one-time password (TOTP) system for mobile device authentication. The security foundation of our construction is that the order of the hash functions is confidential and the security analysis demonstrates that it is more secure than other constructions. Moreover, we discuss the degeneration of our construction and implement the scheme in a mobile device. The simulation experiments show that the attacker cannot increase the probability of guessing the order by eavesdropping on the invalid passwords.

scholarly journals Revealing Cyber Threat of Smart Mobile Devices within Digital Ecosystem: User Information Security Awareness

2021 ◽  
Author(s):  
Heru Susanto
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Cyber Security ◽  
Security Awareness ◽  
Security Measures ◽  
User Behaviour ◽  
Cyber Threats ◽  
Framework Approach ◽  
Smart Mobile ◽  
Smart Mobile Devices

In recent years, the number of mobile device users has increased at a significant rate due to the rapid technological advancement in mobile technology. While mobile devices are providing more useful features to its users, it has also made it possible for cyber threats to migrate from desktops to mobile devices. Thus, it is important for mobile device users to be aware that their mobile device could be exposed to cyber threats and that users could protect their devices by employing cyber security measures. This study discusses how users in responded to the smart mobile devices (SMD) breaches. A number of behavioural model theories are used to understand the user behaviour towards security features of smart mobile devices. To assess the impact of smart mobile devices (SMD) security and privacy, surveys had been conducted with users, stressing on product preferences, user behaviour of SMD, as well as perceptions on the security aspect of SMD. The results was very interesting, where the findings revealed that there were a lack of positive relationships between SMD users and their level of SMD security awareness. A new framework approach to securing SMD is proposed to ensure that users have strong protection over their data within SMD.

Accuracy analysis of BLE beacon-based localization in smart buildings

2021 ◽  
pp. 1-20
Author(s):  
Rosen Ivanov
Keyword(s):  
Use Case ◽  
Accuracy Analysis ◽  
Localization Algorithm ◽  
Computing Power ◽  
Smart Buildings ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Deployment Strategy ◽  
Accurate Localization ◽  
Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

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