scholarly journals Frame-Based Elicitation of Mid-Air Gestures for a Smart Home Device Ecosystem

Informatics ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 23 ◽  
Author(s):  
Panagiotis Vogiatzidakis ◽  
Panayiotis Koutsabasis
Keyword(s):  
Mental Model ◽  
Smart Home ◽  
Smart Device ◽  
Elicitation Study ◽  
Multiple Devices ◽  
Single User ◽  
Definition Of ◽  
High Consistency ◽  
Very High ◽  
Smart Home Environments

If mid-air interaction is to be implemented in smart home environments, then the user would have to exercise in-air gestures to address and manipulate multiple devices. This paper investigates a user-defined gesture vocabulary for basic control of a smart home device ecosystem, consisting of 7 devices and a total of 55 referents (commands for device) that can be grouped to 14 commands (that refer to more than one device). The elicitation study was conducted in a frame (general scenario) of use of all devices to support contextual relevance; also, the referents were presented with minimal affordances to minimize widget-specific proposals. In addition to computing agreement rates for all referents, we also computed the internal consistency of user proposals (single-user agreement for multiple commands). In all, 1047 gestures from 18 participants were recorded, analyzed, and paired with think-aloud data. The study reached to a mid-air gesture vocabulary for a smart-device ecosystem, which includes several gestures with very high, high and medium agreement rates. Furthermore, there was high consistency within most of the single-user gesture proposals, which reveals that each user developed and applied her/his own mental model about the whole set of interactions with the device ecosystem. Thus, we suggest that mid-air interaction support for smart homes should not only offer a built-in gesture set but also provide for functions of identification and definition of personalized gesture assignments to basic user commands.

scholarly journals A Secure and Lightweight Authentication Protocol for IoT-Based Smart Homes

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1488
Author(s):  
JiHyeon Oh ◽  
SungJin Yu ◽  
JoonYoung Lee ◽  
SeungHwan Son ◽  
MyeongHyun Kim ◽  
...  
Keyword(s):  
Information And Communication Technologies ◽  
Smart Home ◽  
Smart Homes ◽  
Authentication Protocol ◽  
Security And Privacy ◽  
Smart Devices ◽  
Smart Device ◽  
Home Environments ◽  
Lightweight Authentication ◽  
Smart Home Environments

With the information and communication technologies (ICT) and Internet of Things (IoT) gradually advancing, smart homes have been able to provide home services to users. The user can enjoy a high level of comfort and improve his quality of life by using home services provided by smart devices. However, the smart home has security and privacy problems, since the user and smart devices communicate through an insecure channel. Therefore, a secure authentication protocol should be established between the user and smart devices. In 2020, Xiang and Zheng presented a situation-aware protocol for device authentication in smart grid-enabled smart home environments. However, we demonstrate that their protocol can suffer from stolen smart device, impersonation, and session key disclosure attacks and fails to provide secure mutual authentication. Therefore, we propose a secure and lightweight authentication protocol for IoT-based smart homes to resolve the security flaws of Xiang and Zheng’s protocol. We proved the security of the proposed protocol by performing informal and formal security analyses, using the real or random (ROR) model, Burrows–Abadi–Needham (BAN) logic, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Moreover, we provide a comparison of performance and security properties between the proposed protocol and related existing protocols. We demonstrate that the proposed protocol ensures better security and lower computational costs than related protocols, and is suitable for practical IoT-based smart home environments.

Intuitive Control on Electric Devices by Smartphones for Smart Home Environments

2016 ◽  
Vol 16 (11) ◽  
pp. 4281-4294 ◽  
Author(s):  
Meng-Shiuan Pan ◽  
Chun-Jie Chen
Keyword(s):  
Smart Home ◽  
Home Environments ◽  
Smart Home Environments

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

The importance of first and second ventilatory thresholds to define aerobic exercise intensity in cardiac patients and in healthy subjects: what is essential can be visible to the eyes

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
F Anselmi ◽  
L Cavigli ◽  
A Pagliaro ◽  
S Valente ◽  
F Valentini ◽  
...  
Keyword(s):  
Exercise Intensity ◽  
Healthy Subjects ◽  
Ventilatory Threshold ◽  
Cardiopulmonary Exercise Testing ◽  
Cardiac Patients ◽  
Funding Sources ◽  
Peak Vo2 ◽  
Definition Of ◽  
Very High ◽  
Sedentary Subjects

Abstract Funding Acknowledgements Type of funding sources: None. Background. Although structured exercise training is strongly recommended in cardiac patients, uncertainties exist about the methods for determining exercise intensity (EI) and their correspondence with effective EI obtained by ventilatory thresholds. We aimed to determine the first (VT1) and second ventilatory threshold (VT2) in cardiac patients, sedentary subjects and athletes comparing VT1 and VT2 with EI defined by recommendations. Methods. We prospectively enrolled 350 subjects (mean age: 50.7 ± 12.9 years; 167 cardiac patients, 150 healthy sedentary subjects, 33 competitive endurance athletes). Each subject underwent ECG, echocardiography, and cardiopulmonary exercise testing. The percentages of peak VO2, peak heart rate (HR), and HR reserve were obtained at VT1 and VT2, and compared with EI definition proposed by the recommendations. Results. VO2 at VT1 corresponded to high rather than moderate EI in 67.1% and in 79.6% of cardiac patients, applying the definition of moderate exercise by the previous recommendations and the 2020 guidelines, respectively. Most of cardiac patients had VO2 values at VT2 corresponding to very-high rather than high EI (59.9% and 50.3%, by previous recommendations and 2020 guidelines, respectively). A better correspondence between ventilatory-thresholds and recommended EI domains was observed in healthy subjects and in athletes (90% and 93.9%, respectively). Conclusions. EI definition based on percentages of peak HR and peak VO2 may misclassify the effective EI and the discrepancy between the individually determined and the recommended EI is particularly relevant in cardiac patients. A ventilatory threshold-based rather than a range-based approach is advisable in order to define an appropriate level of EI. Abstract Figure.

Anion packing density: a universal quantity for both dense and porous crystalline inorganic phases

2002 ◽  
Vol 58 (3) ◽  
pp. 457-462 ◽  
Author(s):  
F. Liebau ◽  
H. Küppers
Keyword(s):  
High Pressure ◽  
Packing Density ◽  
Three Dimensional ◽  
Ionic Radii ◽  
Generalized Framework ◽  
Molal Volumes ◽  
Definition Of ◽  
Anion Packing ◽  
Very High ◽  
Framework Density

To compare densities of inorganic high-pressure phases their molal volumes or specific gravities are usually employed, whereas for zeolites and other microporous materials the so-called framework density, FD, is applied. The definition of FD, which refers only to phases with three-dimensional tetrahedron frameworks, is extended to a `generalized framework density' d f, which is independent of the dimensionality of the framework and the coordination number(s) of the framework cations. In this paper the anion packing density, d ap, is introduced as a new quantity which is not only applicable to any inorganic phase but, in contrast to FD and d f, also allows quantitative comparisons to be made for crystalline inorganic phases of any kind. The anion packing density can readily be calculated if the volume and content of the unit cell and the radii of the anions of a phase are known. From d ap values calculated for high-pressure silica polymorphs studied under very high pressure, it is concluded that Shannon–Prewitt effective ionic radii do not sufficiently take into account the compressibility of the anions.

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