Stabilizing Sensor Data Collection for Control of Environment-Friendly Clean Technologies Using Internet of Things

The increasing number of Internet of Things (IoT) devices with various sensors has resulted in a focus on Cloud-based sensing-as-a-service (CSaaS) as a new value-added service, for example, providing temperature-sensing data via a cloud computing system. However, the industry encounters various challenges in the dynamic provisioning of on-demand CSaaS on diverse sensor networks. We require a system that will provide users with standardized access to various sensor networks and a level of abstraction that hides the underlying complexity. In this study, we aim to develop a cloud-based solution to address the challenges mentioned earlier. Our solution, SenseCloud, includes asensor virtualizationmechanism that interfaces with diverse sensor networks, amultitenancymechanism that grants multiple users access to virtualized sensor networks while sharing the same underlying infrastructure, and adynamic provisioningmechanism to allow the users to leverage the vast pool of resources on demand and on a pay-per-use basis. We implement a prototype of SenseCloud by using real sensors and verify the feasibility of our system and its performance. SenseCloud bridges the gap between sensor providers and sensor data consumers who wish to utilize sensor data.

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Data collection of safety accidents in sports training of athletes with Internet of Things technology

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219100 ◽

2021 ◽

pp. 1-7

Author(s):

Zhiping Wang ◽

Xinxin Zheng ◽

Zhichen Yang

Keyword(s):

Information Technology ◽

Data Collection ◽

Internet Of Things ◽

The Internet ◽

Sports Training ◽

Accident Data ◽

And Performance ◽

Control Technologies ◽

Internet Of Things Technology ◽

The Internet Of Things

The Internet of Things (IoT) technology is an information technology developed in recent years with the development of electronic sensors, intelligence, network transmission and control technologies. This is the third revolution in the development of information technology. This article aims to study the algorithm of the Internet of Things technology, through the investigation of the hazards of athletes’ sports training, scientifically and rationally use the Internet of Things technology to collect data on safety accidents in athletes’ sports training, thereby reducing the risk of athletes’ sports training and making athletes better. In this article, the methods of literature research, analysis and condensing, questionnaire survey, theory and experiment combination, etc., investigate the safety accident data collection of the Internet of Things technology in athletes’ sports training, and provide certain theories and methods for future in-depth research practice basis. The experimental results in this article show that 82% of athletes who are surveyed under the Internet of Things technology will have partial injuries during training, reducing the risk of safety accidents in athletes’ sports training, and better enabling Chinese athletes to achieve a consistent level of competition and performance through a virtuous cycle of development.

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Traffic Flow Data Collection and Signal Control System Based on Internet of Things and Cloud Computing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1608 ◽

2013 ◽

Vol 846-847 ◽

pp. 1608-1611 ◽

Cited By ~ 1

Author(s):

Hui Jie Ding

Keyword(s):

Cloud Computing ◽

Control System ◽

Data Collection ◽

Internet Of Things ◽

Traffic Flow ◽

Signal Control ◽

Traffic Signal Control ◽

Flow Data ◽

Traffic Jam ◽

Sensing Data

As more and more cars are in service, the traffic jam becomes a serious problem in our society. At the same time, more and more sensors make the cars more and more intelligent, and this promotes the development of Internet of things. Real time monitoring the cars will produce massive sensing data, the Cloud computing gives us a good manner to solve this problem. In this paper, we propose a traffic flow data collection and traffic signal control system based on Internet of things and the Cloud computing. The proposed system contains two main parts, sensing data collection and traffic status control subsystem.

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Towards Self-Awareness Privacy Protection for Internet of Things Data Collection

Journal of Applied Mathematics ◽

10.1155/2014/827959 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 8

Author(s):

Kok-Seng Wong ◽

Myung Ho Kim

Keyword(s):

Data Collection ◽

Internet Of Things ◽

Personal Data ◽

Self Awareness ◽

Privacy Concern ◽

Goods And Services ◽

Data Collector ◽

Inaccurate Data ◽

Secure Protocol ◽

The Internet Of Things

The Internet of Things (IoT) is now an emerging global Internet-based information architecture used to facilitate the exchange of goods and services. IoT-related applications are aiming to bring technology to people anytime and anywhere, with any device. However, the use of IoT raises a privacy concern because data will be collected automatically from the network devices and objects which are embedded with IoT technologies. In the current applications, data collector is a dominant player who enforces the secure protocol that cannot be verified by the data owners. In view of this, some of the respondents might refuse to contribute their personal data or submit inaccurate data. In this paper, we study a self-awareness data collection protocol to raise the confidence of the respondents when submitting their personal data to the data collector. Our self-awareness protocol requires each respondent to help others in preserving his privacy. The communication (respondents and data collector) and collaboration (among respondents) in our solution will be performed automatically.

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Getting Enough Sleep: On the Importance of Collecting Longitudinal Data from Wearables to Assess Sleep Quality and Seasonal Effects on Variability in Daily Activity (Preprint)

10.2196/preprints.31807 ◽

2021 ◽

Author(s):

María Óskarsdóttir ◽

Anna Sigridur Islind ◽

Elias August ◽

Erna Sif Arnardóttir ◽

Francois Patou ◽

...

Keyword(s):

Physical Activity ◽

Heart Rate ◽

Data Collection ◽

Sleep Quality ◽

Daily Activity ◽

Hospital Environment ◽

Sensor Data ◽

Seasonal Patterns ◽

Sleep Patterns ◽

Sleep Habits

BACKGROUND The method considered the gold standard for recording sleep is a polysomnography, where the measurement is performed in a hospital environment for 1-3 nights. This requires subjects to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. For longer studies with actigraphy, 3-14 days of data collection is typically used for both clinical and research studies. OBJECTIVE The primary goal of this paper is to investigate if the aforementioned timespan is sufficient for data collection, when performing sleep measurements at home using wearable and non-wearable sensors. Specifically, whether 3-14 days of data collection sufficient to capture an individual’s sleep habits and fluctuations in sleep patterns in a reliable way for research purposes. Our secondary goals are to investigate whether there is a relationship between sleep quality, physical activity, and heart rate, and whether individuals who exhibit similar activity and sleep patterns in general and in relation to seasonality can be clustered together. METHODS Data on sleep, physical activity, and heart rate was collected over a period of 6 months from 54 individuals in Denmark aged 52-86 years. The Withings Aura sleep tracker (non-wearable) and Withings Steel HR smartwatch (wearable) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. RESULTS Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We show specifically that in order to get more robust individual assessment of sleep and physical activity patterns through wearable and non-wearable devices, a longer evaluation period than 3-14 days is necessary. Additionally, we found seasonal patterns in sleep data related to changing of the clock for Daylight Saving Time (DST). CONCLUSIONS We demonstrate that over two months worth of self-tracking data is needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3-14 days for sleep quality assessment and call for rethinking standards when collecting data for research purposes. Seasonal patterns and DST clock change are also important aspects that need to be taken into consideration, and designed for, when choosing a period for collecting data. Furthermore, we suggest using consumer-grade self-trackers (wearable and non-wearable ones) to support longer term evaluations of sleep and physical activity for research purposes and, possibly, clinical ones in the future.

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