Journal of Ambient Intelligence and Smart Environments

Goals and policies are presented as special kinds of rules for managing systems in a flexible way without requiring specialised technical knowledge. However, it is not always possible to rely on exact information for such an approach. Policies should therefore not have to be formulated in terms of precise inputs and outputs. Instead, it is desirable to allow loose goals and policies that accommodate probabilistic system inputs/outputs and fuzziness in rules. This is a general solution that is relevant to many different kinds of applications. The paper uses automated home care management as a concrete illustration of how the approach works. The overall system architecture is presented, along with an overview of the language for expressing goals and policies. The extensions made to allow looser formulations are described. An extended worked example explains various aspects of the approach. The paper concludes with a user evaluation and a discussion of the work.

The basic concept of the Internet of Things (IoT), to uniquely identify objects and to create a virtual representation based on technologies of the Internet, can be extended to so called digital object memories (DOMe), by attaching a virtual storage space to each physical object. This allows for collecting all object-related information generated along the life-cycle chain of this object. The research question, how an infrastructure for digital object memories has to be designed is addressed in this article. Primary goal is to identify and develop components and processes of an architecture concept particularly suited to represent, manage, and use digital object memories. In order to leverage acceptance and deployment of this novel technology, the envisioned infrastructure has to include tools for integration of new systems, and for migration with existing systems. Special requirements to object memories result from the heterogeneity of data in so-called open-loop scenarios. On the one hand, they have to be flexible enough to handle different data types. On the other hand, a simple and structured data access is required. Depending on the application scenario, the latter one needs to be complemented with concepts for a rights- and role-based access and version control. We present a framework based on a structuring data model and a set of tools to create new and to migrate existing applications to digital object memories.

Speech interfaces within Intelligent Environments (IEs) must be rendered adaptive to external and internal factors, among those the complexity of the dialogue. Hence, we present HIS-OwlSpeak, a model-driven dialogue manager for Intelligent Environments. It meets the challenges arising from engineering IEs by providing a unified platform comprising adaptivity to a variety of internal and external factors. This work addresses internal adaptivity realized by different modes of dialogue control, i.e., rule-based and probabilistic. For this, the Hidden Information State (HIS) approach – featuring inherent handling of uncertainty in dialogue systems – is applied to a model-driven, solely rule-based dialogue manager. It uses ontologies to specify the dialogue thus separating the specification from the dialogue control. Consequently, all necessary aspects for merging the world of model-driven dialogue management with the HIS approach are presented in detail. Furthermore, the system has been evaluated using two concurrent dialogues of different complexity successfully validating the implementation.

The Internet of Things (IoT) is rapidly gaining ground as can be witnessed by the pervasive presence of the many things or objects around us that turn our surroundings into Intelligent Environments. These objects interact on a large scale in wired and wireless sensor and actuator networks using advanced communication protocols. Hence, IoT is an open ended and highly dynamic ecosystem with heterogeneous workloads and fluctuating resource availability. Distributed intelligence for smart objects and platforms is a vital enabling factor for IoT, but finding the best strategy to deploy and configure applications – especially those that require contextual intelligence – in a smart environment with dynamic and heterogeneous resource availability is far from straightforward.

Our experiments using context-aware applications for Intelligent Environments show that many resource and performance trade-offs exist and that current deployment schemes for these kind of applications are rough around the edges. We illustrate how a modular design philosophy for smart IoT applications enables a more optimal deployments. Furthermore, we present a methodology to inspect and learn the trade-offs of different deployment schemes of IoT applications in order to autonomously optimize their configuration. We validated our methodology on different use cases and scenarios, and the results demonstrate the feasibility of our approach to automate the efficient deployment of IoT applications in the presence of multiple conflicting Quality of Service (QoS) objectives and varying runtime circumstances.

The present article summarises the doctoral dissertation of Leticia Zamora-Cadenas.