324 research outputs found
Semantic-driven Configuration of Internet of Things Middleware
We are currently observing emerging solutions to enable the Internet of
Things (IoT). Efficient and feature rich IoT middeware platforms are key
enablers for IoT. However, due to complexity, most of these middleware
platforms are designed to be used by IT experts. In this paper, we propose a
semantics-driven model that allows non-IT experts (e.g. plant scientist, city
planner) to configure IoT middleware components easier and faster. Such tools
allow them to retrieve the data they want without knowing the underlying
technical details of the sensors and the data processing components. We propose
a Context Aware Sensor Configuration Model (CASCoM) to address the challenge of
automated context-aware configuration of filtering, fusion, and reasoning
mechanisms in IoT middleware according to the problems at hand. We incorporate
semantic technologies in solving the above challenges. We demonstrate the
feasibility and the scalability of our approach through a prototype
implementation based on an IoT middleware called Global Sensor Networks (GSN),
though our model can be generalized into any other middleware platform. We
evaluate CASCoM in agriculture domain and measure both performance in terms of
usability and computational complexity.Comment: 9th International Conference on Semantics, Knowledge & Grids (SKG),
Beijing, China, October, 201
Sensor Search Techniques for Sensing as a Service Architecture for The Internet of Things
The Internet of Things (IoT) is part of the Internet of the future and will
comprise billions of intelligent communicating "things" or Internet Connected
Objects (ICO) which will have sensing, actuating, and data processing
capabilities. Each ICO will have one or more embedded sensors that will capture
potentially enormous amounts of data. The sensors and related data streams can
be clustered physically or virtually, which raises the challenge of searching
and selecting the right sensors for a query in an efficient and effective way.
This paper proposes a context-aware sensor search, selection and ranking model,
called CASSARAM, to address the challenge of efficiently selecting a subset of
relevant sensors out of a large set of sensors with similar functionality and
capabilities. CASSARAM takes into account user preferences and considers a
broad range of sensor characteristics, such as reliability, accuracy, location,
battery life, and many more. The paper highlights the importance of sensor
search, selection and ranking for the IoT, identifies important characteristics
of both sensors and data capture processes, and discusses how semantic and
quantitative reasoning can be combined together. This work also addresses
challenges such as efficient distributed sensor search and
relational-expression based filtering. CASSARAM testing and performance
evaluation results are presented and discussed.Comment: IEEE sensors Journal, 2013. arXiv admin note: text overlap with
arXiv:1303.244
City Data Fusion: Sensor Data Fusion in the Internet of Things
Internet of Things (IoT) has gained substantial attention recently and play a
significant role in smart city application deployments. A number of such smart
city applications depend on sensor fusion capabilities in the cloud from
diverse data sources. We introduce the concept of IoT and present in detail ten
different parameters that govern our sensor data fusion evaluation framework.
We then evaluate the current state-of-the art in sensor data fusion against our
sensor data fusion framework. Our main goal is to examine and survey different
sensor data fusion research efforts based on our evaluation framework. The
major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed
Systems and Technologies (IJDST), 201
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
IntegraDos: facilitating the adoption of the Internet of Things through the integration of technologies
También, han sido analizados los componentes para una integración del IoT y cloud computing, concluyendo en la arquitectura Lambda-CoAP. Y por último, los desafÃos para una integración del IoT y Blockchain han sido analizados junto con una evaluación de las posibilidades de los dispositivos del IoT para incorporar nodos de Blockchain. Las contribuciones de esta tesis doctoral contribuyen a acercar la adopción del IoT en la sociedad, y por tanto, a la expansión de esta prominente tecnologÃa.
Fecha de lectura de Tesis: 17 de diciembre 2018.El Internet de las Cosas (IoT) fue un nuevo concepto introducido por K. Asthon en 1999 para referirse a un conjunto identificable de objetos conectados a través de RFID. Actualmente, el IoT se caracteriza por ser una tecnologÃa ubicua que está presente en un gran número de áreas, como puede ser la monitorización de infraestructuras crÃticas, sistemas de trazabilidad o sistemas asistidos para el cuidado de la salud. El IoT está cada vez más presente en nuestro dÃa a dÃa, cubriendo un gran abanico de posibilidades con el fin de optimizar los procesos y problemas a los que se enfrenta la sociedad. Es por ello por lo que el IoT es una tecnologÃa prometedora que está continuamente evolucionando gracias a la continua investigación y el gran número de dispositivos, sistemas y componentes emergidos cada dÃa. Sin embargo, los dispositivos involucrados en el IoT se corresponden normalmente con dispositivos embebidos con limitaciones de almacenamiento y procesamiento, asà como restricciones de memoria y potencia. Además, el número de objetos o dispositivos conectados a Internet contiene grandes previsiones de crecimiento para los próximos años, con unas expectativas de 500 miles de millones de objetos conectados para 2030. Por lo tanto, para dar cabida a despliegues globales del IoT, además de suplir las limitaciones que existen, es necesario involucrar nuevos sistemas y paradigmas que faciliten la adopción de este campo. El principal objetivo de esta tesis doctoral, conocida como IntegraDos, es facilitar la adopción del IoT a través de la integración con una serie de tecnologÃas. Por un lado, ha sido abordado cómo puede ser facilitada la gestión de sensores y actuadores en dispositivos fÃsicos sin tener que acceder y programar las placas de desarrollo. Por otro lado, un sistema para programar aplicaciones del IoT portables, adaptables, personalizadas y desacopladas de los dispositivos ha sido definido
A Model-driven Architecture for Multi-protocol OBD Emulator
The Internet of Things (IoT) might be the next revolutionary technology to mark a generation. It could have a particularly strong influence on the automotive industry, changing people’s perception of what a vehicle can do. By connecting several things in a car, IoT empowers it to sense and communicate. Furthermore, this technology clearly opens the way to emerging applications such as automated driving, Vehicle-to-Vehicle and Vehicleto-Infrastructure communication.
Vehicle’s information about its environment and surroundings is crucial to the development of existing and emerging applications. It is already possible to communicate directly (on-site) with vehicles through a built-in On Board Diagnostics (OBD), making it possible to obtain crucial information about the state of the vehicle in real environments. However, there is zero tolerance for error when developing new applications for vehicles that are, a priori, extremely costly and that must also safeguard human lives. Therefore, there is an increasing need for OBD emulators which can allow the development of new applications.
This Thesis proposes a model-driven architecture for multi-protocol OBD emulator, encouraging the development of new emerging OBD systems in a safety environment, to promote the creation of applications to interact or use vehicles’ data. In this sense, the addressed specifications are: Less expensive comparing with today’s solutions; Compatible with different OBD protocols communication; Open Source Hardware and Software suitable for Do-It-Yourself (DIY) development
Advancing IoT Platforms Interoperability
The IoT European Platforms Initiative (IoT-EPI) projects are addressing the topic of Internet of Things and Platforms for Connected Smart Objects and aim to deliver an IoT extended into a web of platforms for connected devices and objects that supports smart environments, businesses, services and persons with dynamic and adaptive configuration capabilities. The specific areas of focus of the research activities are architectures and semantic interoperability, which reliably cover multiple use cases. The goal is to deliver dynamically-configured infrastructure and integration platforms for connected smart objects covering multiple technologies and multiple intelligent artefacts. The IoT-EPI ecosystem has been created with the objective of increasing the impact of the IoT-related European research and innovation, including seven European promising projects on IoT platforms: AGILE, BIG IoT, INTER-IoT, VICINITY, SymbIoTe, bIoTope, and TagItSmart.This white paper provides an insight regarding interoperability in the IoT platforms and ecosystems created and used by IoT-EPI. The scope of this document covers the interoperability aspects, challenges and approaches that cope with interoperability in the current existing IoT platforms and presents some insights regarding the future of interoperability in this context. It presents possible solutions, and a possible IoT interoperability platform architecture
Advancing IoT Platforms Interoperability
The IoT European Platforms Initiative (IoT-EPI) projects are addressing the topic of Internet of Things and Platforms for Connected Smart Objects and aim to deliver an IoT extended into a web of platforms for connected devices and objects that supports smart environments, businesses, services and persons with dynamic and adaptive configuration capabilities. The specific areas of focus of the research activities are architectures and semantic interoperability, which reliably cover multiple use cases. The goal is to deliver dynamically-configured infrastructure and integration platforms for connected smart objects covering multiple technologies and multiple intelligent artefacts. The IoT-EPI ecosystem has been created with the objective of increasing the impact of the IoT-related European research and innovation, including seven European promising projects on IoT platforms: AGILE, BIG IoT, INTER-IoT, VICINITY, SymbIoTe, bIoTope, and TagItSmart.This white paper provides an insight regarding interoperability in the IoT platforms and ecosystems created and used by IoT-EPI. The scope of this document covers the interoperability aspects, challenges and approaches that cope with interoperability in the current existing IoT platforms and presents some insights regarding the future of interoperability in this context. It presents possible solutions, and a possible IoT interoperability platform architecture
Distributed Hybrid Simulation of the Internet of Things and Smart Territories
This paper deals with the use of hybrid simulation to build and compose
heterogeneous simulation scenarios that can be proficiently exploited to model
and represent the Internet of Things (IoT). Hybrid simulation is a methodology
that combines multiple modalities of modeling/simulation. Complex scenarios are
decomposed into simpler ones, each one being simulated through a specific
simulation strategy. All these simulation building blocks are then synchronized
and coordinated. This simulation methodology is an ideal one to represent IoT
setups, which are usually very demanding, due to the heterogeneity of possible
scenarios arising from the massive deployment of an enormous amount of sensors
and devices. We present a use case concerned with the distributed simulation of
smart territories, a novel view of decentralized geographical spaces that,
thanks to the use of IoT, builds ICT services to manage resources in a way that
is sustainable and not harmful to the environment. Three different simulation
models are combined together, namely, an adaptive agent-based parallel and
distributed simulator, an OMNeT++ based discrete event simulator and a
script-language simulator based on MATLAB. Results from a performance analysis
confirm the viability of using hybrid simulation to model complex IoT
scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487
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