Daniel Retkowitz

Simulation of Smart Environments

Smart environments are pervasive systems that comprise integrable net-aware devices. This way, intelligent services can offer complex functionalities across multiple devices. Smart home environments, also called eHomes, constitute already an important research area. However, to build a real eHome is associated with high effort and financial costs. In this paper, we describe our software tool eHomeSimulator that allows us to simulate different smart environments. Thus, we can abstract from creating buildings and developing or purchasing devices. Instead, we can use the eHomeSimulator to research on software engineering aspects of eHomes, such as service development, specification, composition, deployment, and personalization.

Dependency Management in Smart Homes

In future smart homes functionality will be provided to the inhabitants by software services decoupled from the underlying hardware devices. While this will enhance flexibility and will allow to provide cross-functionalities across multiple devices it will also lead to resource conflicts. Future devices will provide basic functionalities which are used by separate higher level services. Each person will use a number of different services and each environment can be inhabited by multiple users at the same time. All respective services have to be executed based on a limited number of devices, which will result in resource conflicts. In this paper we describe how we extended our existing dependency management approach for smart home services with a mechanism for monitoring service bindings and handling access control based on priority groups.

Modeling eHome systems

New developments and decreasing costs of electronic appliances enable the realization of pervasive systems in our daily environment. In our work, we focus on eHome systems. The cost-intensive repetitive development process for every new eHome environment is one of the major problems preventing their widespread use. So, we transformed the repetitive development process to a single one, followed by a repetitive configuration process. To support this configuration process, we introduce a model capable of storing all the parameters relevant for this specific process. To enable semiautomatic configuration based on the model, a specification is required beforehand. In this paper, we will show how the necessary specification is covered by the introduced model, and how the model supports the eHome system configuration and context inferring at runtime.

Dynamic adaptability for smart environments

Software reuse and hardware integration are key factors to offer flexible, low-cost smart environments. Until now, we have been using a static process called the SCD-process to allow a tool-supported realization of such smart environments. The SCD-process is comprised of three different phases: specification, configuration, and deployment. As an initially specified environment is expected to change during run-time and the user may wish to influence certain aspects of the configuration, the static process had to be adapted. This paper describes a new process that supports continuous specification activities and allows for an automated adaptation of the smart homes configuration based on a model-driven approach. We enriched the specification of services with binding policies and constraints to allow for a flexible reconfiguration and a service-specific adaptation. The new configuration mechanism facilitates dynamic reconfiguration based on context information and the extended service specification. In addition, we present a visual tool, which is used to assist the developer and the end-user.

Ontology-based configuration of adaptive smart homes

To develop a market for low-cost Smart Homes reuse of ready-made software components is an essential requirement to reduce the development costs of such systems. Users will run services from a variety of different software producers, which will lead to a heterogeneous service environment. Therefore one of the major challenges is to guarantee interoperability among inconsistent service interfaces. In this paper we present an ontology-based approach to semantic service annotation, which we use for service matching and dynamic generation of service adapters.

Privacy-Friendly Smart Environments

In this paper we describe our approach on protecting user privacy in smart environments, particularly smart homes, which we call eHomes. These are environments with devices such as sensors, computational units, actors, which are seamlessly integrated in the environment, and objects we use in our everyday life. In order to provide more convenience to its users such environments can be personalized. As these environments become ubiquitous, thus supporting mobility of the users, new privacy threats arise. These are based on the digital traces and personal information which is left while visiting different environments. We provide a practical approach to minimize these traces and information disclosure by applying negotiation, identity management, and anonymous credentials. Also, we discuss the protection of eHomes from malicious users.

Graph Transformations for Dynamic Knowledge Processing

The conceptual design phase at the beginning of the building construction process is not adequately supported by any CAD-tool. Conceptual design support needs regarding two aspects: first, the architect must be able to develop conceptual sketches that provide abstraction from constructive details. Second, conceptually relevant knowledge should be available to check these conceptual sketches. The paper deals with knowledge to formalize for conceptual design. To enable domain experts formalizing knowledge, a graph-based specification is presented that allows the development of a domain ontology and design rules specific for one class of buildings at runtime. The provided tool support illustrates the introduced concepts and demonstrates the consistency analysis between knowledge and conceptual design.

Tool Support for the Integration of Light-Weight Ontologies

In many areas of computer science ontologies become more and more important. The use of ontologies for domain modeling often brings up the issue of ontology integration. The task of merging several ontologies, covering specific subdomains, into one unified ontology has to be solved. Many approaches for ontology integration aim at automating the process of ontology alignment. However, a complete automation is not feasible, and user interaction is always required. Nevertheless, most ontology integration tools offer only very limited support for the interactive part of the integration process. In this paper, we present a novel approach for the interactive integration of ontologies. The result of the ontology integration is incrementally updated after each definition of a correspondence between ontology elements. The user is guided through the ontologies to be integrated. By restricting the possible user actions, the integrity of all defined correspondences is ensured by the tool we developed. We evaluated our tool by integrating different regulations concerning building design.

Algorithm and Tool for Ontology Integration Based on Graph Rewriting

Ontologies are often used to define concepts of certain application domains. Using knowledge from several different subdomains then requires the integration of the defined ontologies. Since ontology integration is a difficult task, there is a need for adequate tool support. In the ConDes project, we developed a knowledge-based support for the conceptual design phase in building engineering. Thereby the problem of ontology integration had to be solved. In this paper, we describe these tools and demonstrate how they support the integration task.