369 research outputs found
Ambient Intelligence for Next-Generation AR
Next-generation augmented reality (AR) promises a high degree of
context-awareness - a detailed knowledge of the environmental, user, social and
system conditions in which an AR experience takes place. This will facilitate
both the closer integration of the real and virtual worlds, and the provision
of context-specific content or adaptations. However, environmental awareness in
particular is challenging to achieve using AR devices alone; not only are these
mobile devices' view of an environment spatially and temporally limited, but
the data obtained by onboard sensors is frequently inaccurate and incomplete.
This, combined with the fact that many aspects of core AR functionality and
user experiences are impacted by properties of the real environment, motivates
the use of ambient IoT devices, wireless sensors and actuators placed in the
surrounding environment, for the measurement and optimization of environment
properties. In this book chapter we categorize and examine the wide variety of
ways in which these IoT sensors and actuators can support or enhance AR
experiences, including quantitative insights and proof-of-concept systems that
will inform the development of future solutions. We outline the challenges and
opportunities associated with several important research directions which must
be addressed to realize the full potential of next-generation AR.Comment: This is a preprint of a book chapter which will appear in the
Springer Handbook of the Metavers
Federated Sensor Network architectural design for the Internet of Things (IoT)
An information technology that can combine the physical world and virtual world is desired. The Internet of Things (IoT) is a concept system that uses Radio Frequency Identification (RFID), WSN and barcode scanners to sense and to detect physical objects and events. This information is shared with people on the Internet. With the announcement of the Smarter Planet concept by IBM, the problem of how to share this data was raised. However, the original design of WSN aims to provide environment monitoring and control within a small scale local network. It cannot meet the demands of the IoT because there is a lack of multi-connection functionality with other WSNs and upper level applications. As various standards of WSNs provide information for different purposes, a hybrid system that gives a complete answer by combining all of them could be promising for future IoT applications.
This thesis is on the subject of `Federated Sensor Network' design and architectural development for the Internet of Things. A Federated Sensor Network (FSN) is a system that integrates WSNs and the Internet. Currently, methods of integrating WSNs and the Internet can follow one of three main directions: a Front-End Proxy solution, a Gateway solution or a TCP/IP Overlay solution. Architectures based on the ideas from all three directions are presented in this thesis; this forms a comprehensive body of research on possible Federated Sensor Network architecture designs. In addition, a fully compatible technology for the sensor network application, namely the Sensor Model Language (SensorML), has been reviewed and embedded into our FSN systems. The IoT as a new concept is also comprehensively described and the major technical issues discussed. Finally, a case study of the IoT in logistic management for emergency response is given. Proposed FSN architectures based on the Gateway solution are demonstrated through hardware implementation and lab tests. A demonstration of the 6LoWPAN enabled federated sensor network based on the TCP/IP Overlay solution presents a good result for the iNET localization and tracking project. All the tests of the designs have verified feasibility and achieve the target of the IoT concept
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
Selected Papers from the 5th International Electronic Conference on Sensors and Applications
This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications
A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions
peer reviewedThe rapid advances in the Internet of Things (IoT) have promoted a revolution
in communication technology and offered various customer services. Artificial
intelligence (AI) techniques have been exploited to facilitate IoT operations
and maximize their potential in modern application scenarios. In particular,
the convergence of IoT and AI has led to a new networking paradigm called
Intelligent IoT (IIoT), which has the potential to significantly transform
businesses and industrial domains. This paper presents a comprehensive survey
of IIoT by investigating its significant applications in mobile networks, as
well as its associated security and privacy issues. Specifically, we explore
and discuss the roles of IIoT in a wide range of key application domains, from
smart healthcare and smart cities to smart transportation and smart industries.
Through such extensive discussions, we investigate important security issues in
IIoT networks, where network attacks, confidentiality, integrity, and intrusion
are analyzed, along with a discussion of potential countermeasures. Privacy
issues in IIoT networks were also surveyed and discussed, including data,
location, and model privacy leakage. Finally, we outline several key challenges
and highlight potential research directions in this important area
Marine biodiversity assessments using aquatic internet of things
While Ubiquitous Computing remains vastly applied in urban environments, it is still scarce in
oceanic environments. Current equipment used for biodiversity assessments remain at a high cost,
being still inaccessible to wider audiences. More accessible IoT (Internet of Things) solutions need
to be implemented to tackle these issues and provide alternatives to facilitate data collection
in-the-wild. While the ocean remains a very harsh environment to apply such devices, it is still
providing the opportunity to further explore the biodiversity, being that current marine taxa is
estimated to be 200k, while this number can be actually in millions.
The main goal of this thesis is to provide an apparatus and architecture for aerial marine
biodiversity assessments, based on low-cost MCUs (Microcontroller unit) and microcomputers. In
addition, the apparatus will provide a proof of concept for collecting and classifying the collected
media. The thesis will also explore and contribute to the latest IoT and machine learning techniques
(e.g. Python, TensorFlow) when applied to ocean settings. The final product of the thesis will
enhance the state of the art in technologies applied to marine biology assessments.A computação ubĂqua Ă© imensamente utilizada em ambientes urbanos, mas ainda Ă© escassa em
ambientes oceânicos. Os equipamentos atuais utilizados para o estudo de biodiversidade são de
custo alto, e geralmente inacessĂveis para o pĂşblico geral. Uma solução IoT mais acessĂvel necessita
de ser implementada para combater estes problemas e fornecer alternativas para facilitar a recolha
de dados na natureza. Embora o oceano seja um ambiente severo para aplicar estes dispositivos,
este fornece mais oportunidades para explorar a biodiversidade, sendo que a taxa de marinha atual
é estimada ser 200 mil, mas este número pode estar nos milhões.
O objetivo principal desta tese é fornecer um aparelho e uma arquitetura para estudos aéreos
de biodiversidade marinha, baseado em microcontroladores low-cost e microcomputadores. Adi cionalmente, este aparelho irá fornecer uma prova de conceito para coletar e classificar a mĂdia
coletada. A tese irá também explorar e contribuir para as técnicas mais recentes de machine learn ing (e.g. Python, TensorFlow) quando aplicadas num cenário de oceano. O produto final desta
tese vai elevar o estado da arte em tecnologias aplicadas a estudos de biologia marinha
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