A common feature of ambient intelligence is that many objects are inter-connected and act in unison, which is also a challenge in the Internet of Things. There has been a shift in research towards integrating both concepts, considering the Internet of Things as representing the future of computing and communications. However, the efficient combination and management of heterogeneous things or devices in the ambient intelligence domain is still a tedious task, and it presents crucial challenges. Therefore, to appropriately manage the inter-connection of diverse devices in these systems requires: (1) specifying and efficiently implementing the devices (e.g., as services); (2) handling and verifying their heterogeneity and composition; and (3) standardizing and managing their data, so as to tackle large numbers of systems together, avoiding standalone applications on local servers. To overcome these challenges, this paper proposes a platform to manage the integration and behavior-aware orchestration of heterogeneous devices as services, stored and accessed via the cloud, with the following contributions: (i) we describe a lightweight model to specify the behavior of devices, to determine the order of the sequence of exchanged messages during the composition of devices; (ii) we define a common architecture using a service-oriented standard environment, to integrate heterogeneous devices by means of their interfaces, via a gateway, and to orchestrate them according to their behavior; (iii) we design a framework based on cloud computing technology, connecting the gateway in charge of acquiring the data from the devices with a cloud platform, to remotely access and monitor the data at run-time and react to emergency situations; and (iv) we implement and generate a novel cloud-based IoT platform of behavior-aware devices as services for ambient intelligence systems, validating the whole approach in real scenarios related to a specific ambient assisted living application.

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Abstract.We present the open reference architecture of the SeaClouds solution. It aims at enabling a seamless adaptive multi-cloud management of complex applications by supporting the distribution, monitoring and reconfiguration of app modules over heterogeneous cloud providers. Motivation and Objectives of SeaCloudsCloud computing is a model for enabling convenient and on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Many private and public clouds have emerged during the last years, offering a range of different services at SaaS, PaaS levels aimed at matching different user requirements. Current cloud technologies suffer from a lack of standardization, with different providers offering similar resources in a different manner, which results in the vendor lock-in problem. This problem affects all stages of the cloud applications' lifecycle, ranging from their design to their operation. Application developers must know the features of the services to be used, and have a deep knowledge of the providers' API. To reduce the need of using deep knowledge, we can find solutions based on the use of standards, such as OASIS CAMP or OASIS TOSCA , DMTF CIMI , unified APIs, such as jClouds 5 , or solutions like Docker 6 . These solutions are indeed very different, for example, whereas jClouds provides a cloud agnostic API library to provision and configure secure communications with cloud virtual machines, container-based solutions like Docker allow describing and deploying applications and their dependencies through containers on machines with the corresponding engine. Furthermore, different vendors (e.g., Dell, BMC, Abiquo) are currently commercialising tools for This work has been partly supported by the EU-FP7-ICT-610531 SeaClouds project. 5 https://jclouds.apache.org 6 https://www.docker.com

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In the Future Internet, applications based on Wireless Sensor Networks will have to support reconfiguration with minimum human intervention, depending on dynamic context changes in their environment. These situations create a need for building these applications as adaptive software and including techniques that allow the context acquisition and decisions about adaptation. However, contexts use to be made up of complex information acquired from heterogeneous devices and user characteristics, making them difficult to manage. So, instead of building context-aware applications from scratch, we propose to use FamiWare, a family of middleware for Ambient Intelligence specifically designed to be aware of contexts in sensor and smartphone devices. It provides both, several monitoring services to acquire contexts from devices and users, and a context-awareness service to analyze and detect context changes. However, the current version of FamiWare does not allow the automatic incorporation related to the management of new contexts into the FamiWare family. To overcome this shortcoming, in this work, we first present how to model the context using a metamodel to define the contexts that must to be taken into account in an instantiation of FamiWare for a certain Ambient Intelligence system. Then, to configure a new context-aware version of FamiWare and to generate code ready-to-install within heterogeneous devices, we define a mapping that automatically transforms metamodel elements defining contexts into elements of the FamiWare family, and we also use the FamiWare configuration process to customize the new context-aware variant. Finally, we evaluate the benefits of our process, and we analyze both that the new version of the middleware works as expected and that it manages the contexts in an efficient way.

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