scispace - formally typeset
Open AccessJournal ArticleDOI

Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0

Melanie Swan
- 08 Nov 2012 - 
- Vol. 1, Iss: 3, pp 217-253
Reads0
Chats0
TLDR
This paper provides a comprehensive review of the current and rapidly emerging ecosystem of the Internet of Things (IOT) and outlines four critical functional steps: data creation, information generation, meaning-making, and action-taking.
Abstract
The number of devices on the Internet exceeded the number of people on the Internet in 2008, and is estimated to reach 50 billion in 2020. A wide-ranging Internet of Things (IOT) ecosystem is emerging to support the process of connecting real-world objects like buildings, roads, household appliances, and human bodies to the Internet via sensors and microprocessor chips that record and transmit data such as sound waves, temperature, movement, and other variables. The explosion in Internet-connected sensors means that new classes of technical capability and application are being created. More granular 24/7 quantified monitoring is leading to a deeper understanding of the internal and external worlds encountered by humans. New data literacy behaviors such as correlation assessment, anomaly detection, and high-frequency data processing are developing as humans adapt to the different kinds of data flows enabled by the IOT. The IOT ecosystem has four critical functional steps: data creation, information generation, meaning-making, and action-taking. This paper provides a comprehensive review of the current and rapidly emerging ecosystem of the Internet of Things (IOT).

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading

TL;DR: This paper studies resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-divisionmultiple access (OFDMA), for which the optimal resource allocation is formulated as a mixed-integer problem.
Journal ArticleDOI

Internet of Things: Architectures, Protocols, and Applications

TL;DR: This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly.
Journal ArticleDOI

Secure integration of IoT and Cloud Computing

TL;DR: A survey of IoT and Cloud Computing with a focus on the security issues of both technologies is presented, and it shows how the Cloud Computing technology improves the function of the IoT.
Journal ArticleDOI

The Rise of Consumer Health Wearables: Promises and Barriers

TL;DR: This work considers whether wearable technology can become a valuable asset for health care and investigates the role that smartwatches can play in this process.

Fundamental Disruption in Big Data Science and Biological Discovery

Melanie Swan
TL;DR: In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses.
References
More filters
Journal ArticleDOI

The Internet of Things: A survey

TL;DR: This survey is directed to those who want to approach this complex discipline and contribute to its development, and finds that still major issues shall be faced by the research community.
Journal ArticleDOI

Quantitative analysis of culture using millions of digitized books

TL;DR: This work surveys the vast terrain of ‘culturomics,’ focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000, and shows how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology and the pursuit of fame.
Posted Content

Building high-level features using large scale unsupervised learning

TL;DR: In this paper, a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization was used to train a face detector without having to label images as containing a face or not.
Journal ArticleDOI

The Unreasonable Effectiveness of Data

TL;DR: A trillion-word corpus - along with other Web-derived corpora of millions, billions, or trillions of links, videos, images, tables, and user interactions - captures even very rare aspects of human behavior.
Proceedings Article

Building high-level features using large scale unsupervised learning

TL;DR: In this paper, a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization was used to learn high-level, class-specific feature detectors from only unlabeled data.
Related Papers (5)