Ibrahim Armac

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.

Modeling and analysis of functionality in eHome systems: dynamic rule-based conflict detection

The domain of eHome systems is a special application-area for pervasive computing. Many different kinds of devices are introduced to the home area to provide functionality for enhanced comfort or security. A similar level of heterogeneity can be found at the software level: many different vendors supply eHome systems with drivers and services, which intend to compute sensor information and trigger devices in the eHome. This multi-level heterogeneity leads to system faults in terms of deadlocks and unpredictable or disillusioning behavior. We call these error conditions conflicts. Pervasive systems, especially eHome systems, is only useful and thus successful, if such conflicts can be handled properly. In this paper, we analyze eHome systems with respect to types of conflicts and discuss how conflicts can be detected. We show that the dynamic conflict detection is reasonable and possible by a rule-based conflict detection. The detection is well-founded on a formal specification and is seamlessly integrated into the paradigm of component-based software construction

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.

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.

Client side personalization of smart environments

In this paper we will describe our approach for supporting users by personalizing the multiple environments they visit in their daily lives. In our approach, user preferences are stored on a handheld device which can assist the user in selecting, installing and parameterizing personal services in the environments.

The ehomeconfigurator tool suite

In contrast to decreasing costs of electronic appliances enabling the realization of pervasive systems, the price of individual development of the software making up eHome systems is one of the major problems preventing their large-scale adoption By introducing a specification, configuration, and deployment process, the environment-specific development effort is reduced We support this process by our tool suite, the eHomeConfigurator which is introduced in this paper It creates a configuration graph, capable of describing dependencies and contexts of components in the eHome field The tool suite is used to configure and deploy various eHome services on different home environments Compared to the classical development process, the effort for setting up eHome systems is reduced significantly and opens up the possibility to decrease the development costs for eHome systems.

Privacy-friendly user modelling for smart environments

In this paper we will describe how we model personal data in smart environments, which are user adaptive systems. Personal services in such smart environments can access user data by a consistent interface of our user modelling system. Considering inter-home mobility (i.e. users moving across multiple smart environments), the user model is flexible in the sense that it can dynamically deal with users entering or leaving a smart environment. Applying anonymous credentials and pseudonymity in combination with role-based access control, we can protect the privacy of users while protecting the environments against malicious users.