物件導向軟體之架構(Object-Oriented Software Construction)探討

An Introduction to MultiAgent Systems.

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.

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Context Aware Computing for The Internet of Things: A Survey

Advanced Mathematical Methods for Scientists and Engineers

To study the operational behaviour of λ-terms, we will use the denotational (mathematical) approach. A denotational semantics for a language is based on the choice of a space of semantics values, or denotations, where terms are to be interpreted. Choosing a space with nice mathematical properties can help in proving the semantic properties of terms, since to this aim standard mathematical techniques can be used.

λ Δ -Models

Ubiquitous computing environments are characterised by a high number of heterogeneous devices that generate a huge amount of context data. These data are used to adapt applications to changing execution contexts. However, legacy frameworks fail to process context information in a scalable and efficient manner. In this paper, we propose to organise the classical functionalities of a context manager to introduce a 3-steps cycle of data collection, interpretation, and situation identification. We propose the COSMOS framework, which is based on the concepts of context node and context management policies translated into software components in software architecture. This paper presents COSMOS and evaluates its efficiency throughout the example of the composition of context information to implement a caching/offloading adaptation situation.

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Scalable processing of context information with COSMOS

Ubiquitous applications are characterized by variations in their execution context. Their correct operation requires some continual adaptations based on the observation of this context. The design and implementation of these observation policies is thus the cornerstone of any ubiquitous application. COSMOS is a component-based framework for managing context information in ubiquitous context-aware applications. COSMOS decomposes context observation policies into fine-grained units called context nodes, which it implements as software components. These units perform basic context-related operations, such as gathering data from a system or network probe and computing threshold or average values. The framework assembles these units with a set of well-identified architectural design patterns. A mobile computing scenario illustrates the framework's benefits.

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Software Architecture Patterns for a Context-Processing Middleware Framework

Nowadays, context management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of context. Context management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) context management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale context management, (ii) management of extrafunctional concerns (quality of context and privacy), and (iii) autonomous deployment of context management entities.

INCOME – Multi-scale Context Management for the Internet of Things

Context-aware computing has to deal with a huge amount of context data. Taking into account the quality of these data becomes a corner stone of an efficient context management solution. Information on the quality of context helps taking appropriate decisions and allows to identify uncertain context information saving processing time for deriving a pertinent description of the observed phenomenon.

This paper presents a work in progress for integrating Quality of Context in COSMOS (COntext entitieS coMpositiOn and Sharing) [4,13], a component-based framework for managing context data in ubiquitous environments, and illustrates it throughout the example of the composition of context information to implement a network connectivity vs energy adaptation situation.

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A framework for quality of context management

Designers and developers try to build stable and multi-domain software components while the software infrastructure they use keeps evolving and thus being heterogeneous. This can be called “the business and technical life cycles mismatch”. The paper shows where and why middleware technologies fail to solve the business and technical life cycles mismatch. Then it demonstrates that lots of the nice features of software components are allowed thanks to the concept of container. The container is an entity visible as much during software component development as during execution. Some of its properties must be taken into account in software architectures and design models.

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Denis Conan

Papers

Scalable processing of context information with COSMOS

Software Architecture Patterns for a Context-Processing Middleware Framework

INCOME – Multi-scale Context Management for the Internet of Things

A framework for quality of context management

Integration of Non-Functional Properties in Containers