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COLLECT: COLLaborativE ConText-aware service oriented architecture for intelligent decision-making in the Internet of Things

Internet of Things (IoT) has radically transformed the world; currently, every device can be connected to the Internet and provide valuable information for decision-making. In spite of the fast evolution of technologies accompanying the grow of IoT, one of the remaining challenges in this scope is the design of a Service Oriented Architecture (SOA) for IoT, which facilitates the inclusion of data coming from several IoT devices as well the delivery of such data among system agents, real-time data processing and service provision to users. Furthermore, context-aware data processing and architec-tures still pose a challenge, regardless of being key requirements in order to get stronger IoT architectures. Besides, with the objective of sharing information across platforms, collaborative architectures for data sharing in the scope of the IoT are an essential re-quirement for giving additional value to any decision-making process. To sum up, IoT architectures should provide essential elements such as sensor devices, offered services, communication networks and event context processing; always promoting key features such as interoperability, reliability and scalability.
To face this challenge, we propose a COLLaborative ConText Aware Service Ori-ented Architecture (COLLECT), which facilitates: (1) Implementing reliable collabo-ration among several nodes through a collaborative Event Driven SOA. (2) Ensuring system scalability and interoperability through the opportunity of federating Enterprise Service Buses (ESB) in the cloud and through distributed Complex Event Processing (CEP). (3) Facilitating the task of processing information and publishing and subscrib-ing to distributed complex events of interest in the context of the application.

Air4People: a Smart Air Quality Monitoring and Context-Aware Notification System (Summary)

Air quality is one of the key topics in the focus of Internet of Things (IoT) appli-cations and smart cities, since it plays an essential role for citizens nowadays and is currently a worldwide concern. Indeed, air pollution can seriously affect citi-zens’ health; particularly, air pollution may worsen and favour certain illnesses or even cause death to specific risk groups. The fact is that due to this worldwide concern, several IoT systems for air quality monitoring have been created over the last years. Nevertheless, the problem is that monitoring alone is not enough; it is necessary to ensure compliance with the following requirements: (1) air quali-ty information and alerts have to be updated in real time; (2) the information has to be actively provided to citizens in a user-friendly way; (3) the information provided to users, in particular to risks groups, needs to be adapted to their spe-cific features and (4) the system should also take into account the type of activity the user is going to be involved in and adapt notifications accordingly.
Currently, most systems providing air quality information lack several of such key characteristics; as a result, information does not reach citizens in a sim-ple way and notifications neither consider citizens’ specific characteristics nor take their physical activity into account. In order to tackle these challenges effec-tively, and to pay special attention to context-awareness issues, we present Air4People: an air quality monitoring and context-aware notification system, which permits obtaining the user’s air quality relevant context, processing both the data coming from IoT air information sources and from the user context, and notifying users in real time when a health risk for their particular context is de-tected.

Context-Aware Recommendations in Mobile Environments

Traditional recommendation systems offer relevant items (e.g., books, movies, music, etc.) to users, but they are not designed for mobile environments. In those environments, the context (e.g., the location, the time, the weather, the presence of other people, etc.) and the movements of the users may be important factors to obtain relevant and helpful recommendations. The emergence of context-aware recommendation systems has prompted the growth of recommendation algorithms that incorporate context information. However, most existing research in this field considers only static context information, despite the fact that exploiting dynamic context information would be very helpful in mobile computing scenarios. Moreover, the design and implementation of generic frameworks to support an easy development of context-aware recommendation systems has been relatively unexplored. In this paper, we present our ongoing work to develop a context-aware recommendation framework for distributed and mobile environments, which will allow suggesting relevant items to mobile users.