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Search Results (3)

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Keywords = wearable health crowd-sensing

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26 pages, 1497 KiB  
Article
A Batch Processing Technique for Wearable Health Crowd-Sensing in the Internet of Things
by Abigail Akosua Addobea, Qianmu Li, Isaac Obiri Amankona and Jun Hou
Cryptography 2022, 6(3), 33; https://doi.org/10.3390/cryptography6030033 - 29 Jun 2022
Cited by 3 | Viewed by 3424
Abstract
The influx of wearable sensor devices has influenced a new paradigm termed wearable health crowd-sensing (WHCS). WHCS enables wearable data collection through active sensing to provide health monitoring to users. Wearable sensing devices capture data and transmit it to the cloud for data [...] Read more.
The influx of wearable sensor devices has influenced a new paradigm termed wearable health crowd-sensing (WHCS). WHCS enables wearable data collection through active sensing to provide health monitoring to users. Wearable sensing devices capture data and transmit it to the cloud for data processing and analytics. However, data sent to the cloud is vulnerable to on-path attacks. The bandwidth limitation issue is also another major problem during large data transfers. Moreover, the WHCS faces several anonymization issues. In light of this, this article presents a batch processing method to solve the identified issues in WHCS. The proposed batch processing method provides an aggregate authentication and verification approach to resolve bandwidth limitation issues in WHCS. The security of our scheme shows its resistance to forgery and replay attacks, as proved in the random oracle (ROM), while offering anonymity to users. Our performance analysis shows that the proposed scheme achieves a lower computational and communication cost with a reduction in the storage overhead compared to other existing schemes. Finally, the proposed method is more energy-efficient, demonstrating that it is suitable for the WHCS system. Full article
(This article belongs to the Special Issue Privacy-Preserving Techniques in Cloud/Fog and Internet of Things)
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Figure 1
<p>Wearable health crowd-sensing platform.</p> Full article ">Figure 2
<p>Wearable health crowd-sensing system architecture.</p> Full article ">Figure 3
<p>The process flow of system architecture.</p> Full article ">Figure 4
<p>Computational Cost.</p> Full article ">Figure 5
<p>Batch processing cost of n users.</p> Full article ">Figure 6
<p>Communication and overhead cost.</p> Full article ">Figure 7
<p>Energy Consumption of schemes.</p> Full article ">Figure 8
<p>Batch processing energy consumption (in mJ).</p> Full article ">
34 pages, 1441 KiB  
Review
Recent Advances in Wearable Sensing Technologies
by Alfredo J. Perez and Sherali Zeadally
Sensors 2021, 21(20), 6828; https://doi.org/10.3390/s21206828 - 14 Oct 2021
Cited by 54 | Viewed by 12353
Abstract
Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their [...] Read more.
Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and we discuss open challenges that must be addressed to further improve the efficacy of wearable sensing systems in the future. Full article
(This article belongs to the Special Issue Recent Advances in Sensing and IoT Technologies)
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<p>Consumer wearables device market share (2019–2022).</p> Full article ">Figure 2
<p>Wearable services market value.</p> Full article ">Figure 3
<p>Paper organization.</p> Full article ">Figure 4
<p>Wearable sensors based on intrusiveness level.</p> Full article ">Figure 5
<p>Typical components of a wearable sensing device. The red dotted line indicates possible connection.</p> Full article ">Figure 6
<p>Typical sensors available in wearable devices grouped by type of collected data.</p> Full article ">Figure 7
<p>General architecture of wearable sensing systems.</p> Full article ">
1030 KiB  
Review
Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities
by Alex Adim Obinikpo and Burak Kantarci
J. Sens. Actuator Netw. 2017, 6(4), 26; https://doi.org/10.3390/jsan6040026 - 20 Nov 2017
Cited by 50 | Viewed by 10829
Abstract
With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with [...] Read more.
With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category. Full article
(This article belongs to the Special Issue Sensors and Actuators in Smart Cities)
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Figure 1
<p>Smart health embedded within a smart city. An example scenario is illustrated to detect the air quality indicator to ensure healthier communities (figure produced by Creately Online Diagram, Cinergix Pvt. Ltd., Mentone, Australia).</p> Full article ">Figure 2
<p>A basic deep feed-forward network.</p> Full article ">Figure 3
<p>Autoencoder network.</p> Full article ">Figure 4
<p>CNN architecture.</p> Full article ">Figure 5
<p>Boltzmann network.</p> Full article ">Figure 6
<p>Data acquisition methods and processing techniques. (<b>a</b>) Taxonomy of sensory data acquisition and processing techniques; (<b>b</b>) types of wearables/carry-ons.</p> Full article ">
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