spChains: A Declarative Framework for Data Stream Processing in Pervasive Applications

Pervasive applications rely on increasingly complex streams of sensor data continuously captured from the physical world. Such data is crucial to enable applications to ``understand'' the current context and to infer the right actions to perform, be they fully automatic or involving some user decisions. However, the continuous nature of such streams, the relatively high throughput at which data is generated and the number of sensors usually deployed in the environment, make direct data handling practically unfeasible. Data not only needs to be cleaned, but it must also be filtered and aggregated to relieve higher level algorithms from near real-time handling of such massive data flows. We propose here a stream-processing framework (spChains), based upon state-of-the-art stream processing engines, which enables declarative and modular composition of stream processing chains built atop of a set of extensible stream processing blocks. While stream processing blocks are delivered as a standard, yet extensible, library of application-independent processing elements, chains can be defined by the pervasive application engineering team. We demonstrate the flexibility and effectiveness of the spChains framework on two real-world applications in the energy management and in the industrial plant management domains, by evaluating them on a prototype implementation based on the Esper stream processo

Context-aware and resource efficient sensing infrastructure for context-aware applications

Middleware for wireless sensor networks and middleware for context-aware applications both provide information abstraction and programming support for gathering, pre-processing, and managing sensor data. However the former mostly concentrates on optimising the operations of the resource constrained hardware and simplifying access to the raw sensor data while the latter focuses on gathering sensor data, pre-processing it to the abstract context information required by the applications and providing reasoning on this data. In this paper, we explore the idea of enhancing middleware for context-aware applications with solutions from sensor networks middle ware to allow resource efficient and contextaware management of sensing infrastructure. The decisions on which sensor data needs to be delivered to the middleware for evaluation are based on current contextual situations. The approach allows to trade the level of confidence in context information for resource efficiency in context provisioning without a detrimental effect on the functionality of contextaware applications. © 2010 IEEE

Proactive Assistance Within Ambient Environment. Towards intelligent agent server that anticipate and provide users' needs.

International audienceUser needs are expanding and becoming more and more complex with the emergence of newly adopted technologies. As a result, the convergence of smart devices, having the capability to communicate as well as sharing information and ensuring user need satisfaction, leads to profoundly change the way we interact with our environment. They should provide an adaptive assistance in both reactive and proactive mode and new communication methods focused on multimodal and multichannel interfaces. However, most of existing context-aware systems have extremely tight coupling between applications' semantic and sensor's details. So, the objective of our research is to implement an approach which can support the ability to reuse sensors and to evolve existing applications to use new context types. In this paper, we illustrate our approach for proactive intelligent assistance and we describe our architecture based on three principal layers. These layers are designed in order to build applications which can increase the welfare of the user situated in intelligent environment

Jabber-based cross-domain efficient and privacy-ensuring context management framework.

In pervasive environments, context-aware applications require a global knowledge of the context information distributed in different spatial domains in order to establish context-based interactions. Therefore, the design of distributed storage, retrieval, and dissemination mechanisms of context information across domains becomes vital. In such environments, we envision the necessity of collaboration between different context servers distributed in different domains; thus, the need for generic APIs and protocol allowing context information exchange between different entities: context servers, context providers, and context consumers. As a solution this paper proposes ubique, a distributed middleware for contextaware computing that allows applications to maintain domain-based context interests to access context information about users, places, events, and things - all made available by or brokered through the home domain server. This paper proposes also a new cross-domain protocol for context management which ensures the privacy and the efficiency of context information dissemination. It has been robustly built upon the Jabber protocol which is a widely adopted open protocol for instant messaging and is designed for near real-time communication. Simulation and experimentation results show that ubique framework well supports robust cross-domain context management and collaboratio

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

A Language for Online State Processing of Binary Sensors, Applied to Ambient Assisted Living

International audienceThere is a large variety of binary sensors in use today, and useful context-aware services can be defined using such binary sensors. However, the currently available approaches for programming context-aware services do not conveniently support binary sensors. Indeed, no existing approach simultaneously supports a notion of state, central to binary sensors, offers a complete set of operators to compose states, allows to define reusable abstractions by means of such compositions, and implements efficient online processing of these operators. This paper proposes a new language for event processing specifically targeted to binary sensors. The central contributions of this language are a native notion of state and semi-causal operators for temporal state composition including: Allen's interval relations generalized for handling multiple intervals, and temporal filters for handling delays. Compared to other approaches such as CEP (complex event processing), our language provides less discontinued information, allows less restricted compositions, and supports reusable abstractions. We implemented an interpreter for our language and applied it to successfully rewrite a full set of real Ambient Assisted Living services. The performance of our prototype interpreter is shown to compete well with a commercial CEP engine when expressing the same services