Fakultät für Informatik und Mathematik
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The main research question of this thesis is to develop a theory that would provide foundations for the development of Web of Things (WoT) systems. A theory for WoT shall provide a model of the ‘things’ WoT agents relate to such that these relations determine what interactions take place between these agents. This thesis presents a knowledge-based approach in which the semantics of WoT systems is given by a transformation (an homomorphism) between a graph representing agent interactions and a knowledge graph describing ‘things’. It focuses on three aspects of knowledge graphs in particular: the vocabulary with which assertions can be made, the rules that can be defined over this vocabulary and its serialization to efficiently exchange pieces of a knowledge graph. Each aspect is developed in a dedicated chapter, with specific contributions to the state-of-the-art.
The need for a unified vocabulary to describe ‘things’ in WoT and the Internet of Things (IoT) has been identified early on in the literature. Many proposals have been consequently published, in the form of Web ontologies. In Ch. 2, a systematic review of these proposals is being developed, as well as a comparison with the data models of the principal IoT frameworks and protocols. The contribution of the thesis in that respect is an alignment between the Thing Description (TD) model and the Semantic Sensor Network (SSN) ontology, two standards of the World Wide Web Consortium (W3C). The scope of this thesis is generally limited to Web standards, especially those defined by the Resource Description framework (RDF).
Web ontologies do not only expose a vocabulary but also rules to extend a knowledge graph by means of reasoning. Starting from a set of TD documents, new relations between ‘things’ can be “discovered” this way, indicating possible interactions between the servients that relate to them. The experiments presented in Ch. 3 were done on the basis of this semantic discovery framework on two use cases: a building automation use case provided by Intel Labs and an industrial control use case developed internally at Siemens. The relations to discover often involve anonymous nodes in the knowledge graph: the chapter also introduces a novel skolemization algorithm to correctly process these nodes on a well-defined fragment of the Web Ontology Language (OWL).
Finally, because this semantic discovery framework relies on the exchange of TD documents, Ch. 4 introduces a binary format for RDF that proves efficient in serializing TD assertions such that even the smallest WoT agents, i.e. micro-controllers, can store and process them. A formalization for the semantics-preserving compaction and querying of TD documents is also introduced in this chapter, at the basis of an embedded RDF store called the µRDF store. The ability of all WoT agents to query logical assertions about themselves and their environment, as found in TD documents, is a first step towards knowledge-based intelligent systems that can operate autonomously and dynamically in a decentralized way. The µRDF store is an attempt to illustrate the practical outcomes of the theory of WoT developed throughout this thesis.
Allowing users to control access to their data is paramount for the success of the Internet of Things; therefore, it is imperative to ensure it, even when data has left the users' control, e.g. shared with cloud infrastructure. Consequently, we propose several state of the art mechanisms from the security and privacy research fields to cope with this requirement.
To illustrate how each mechanism can be applied, we derive a data-centric architecture providing access control and privacy guaranties for the users of IoT-based applications. Moreover, we discuss the limitations and challenges related to applying the selected mechanisms to ensure access control remotely. Also, we validate our architecture by showing how it empowers users to control access to their health data in a quantified self use case.
The Internet of Things (IoT) is a network of computational services, devices, and people, which share information with each other. In IoT, inter-system communication is possible and human interaction is not required. IoT devices are penetrating the home and office building environments. According to current estimates, about 35 billion IoT devices will be connected by the year 20212. In the IoT business model, value comes from integrating devices into applications, e.g., home and office automation. In general, an IoT application associates different information sources with actions which can modify the environment, e.g., change the room’s temperature, inform a person, e.g., send an e-mail, or activate other services, e.g., buy milk on-line.
In this thesis, we focus on the commissioning and verification processes of IoT devices used in building automation applications. Within a building’s lifespan, new devices are added, interior spaces are refurbished, and faulty devices are replaced. All of these changes are currently made manually. Furthermore, consider that a context-aware Building Management System (BMS) is an IoT application, which measures direct-context from the building’s sensors to characterize environmental conditions, user locations, and state. Additionally, a BMS combines sensor information to derive inferred-context, such as user activity. Similar to IoT devices, inferred-context instances have to be created manually. As the number of devices and inferred-context instances increases, keeping track of all associations becomes a time-consuming and error-prone task.
The hypothesis of the thesis is that users who interact with the building create use-patterns in the data, which describe functional relations between devices and inferred-context instances, e.g., which desk-movement sensor is used to infer desk-presence and controls which overhead light; additionally, use-patterns can also provide structural relations, e.g., the relative position of spatial sensors. To test the hypothesis, this thesis presents an extension to the new IoT class rule programming paradigm, which simplifies rule creation based on classes. The proposed extension uses a semantic compiler to simplify the device and inferred-context associations. Using direct-context information and template classes, the compiler creates all possible inferredcontext instances. Buildings using context-aware BMSs will have a dynamic response to user behaviour, e.g., required illumination for computer-work is provided by adjusting blinds or increasing the dim setting of overhead ceiling lamps. We propose a rule mining framework to extract use-patterns and find the functional and structural relationships between devices. The rule mining framework uses three stages: (1) event extraction, (2) rule mining, (3) structure creation. The event extraction combines the building’s data into a time-series of device events. Then, in the rule mining stage, rules are mined from the time series, where we use the established algorithm temporal interval tree association rule learner. Additionally, we proposed a rule extraction algorithm for spatial sensor’s data. The algorithm is based on statistical analysis of user transition times between adjacent sensors. We also introduce a new rule extraction algorithm based on increasing belief. In the last stage, structure creation uses the extracted rules to produce device association groups, hierarchical representation of the building, or the relative location of spatial sensors. The proposed algorithms were tested using a year-long installation in a living-lab consisting of a four-person office, a 12-person open office, and a meeting room. For the spatial sensors, four locations within public buildings were used: a meeting room, a hallway, T-crossing, and a foyer. The recording times range from two weeks to two months depending on scenario complexity.
We found that user-generated patterns appear in building data. The rule mining framework produced structures that represent functional and spatial relationships of building’s devices and provide sufficient information to automate maintenance tasks, e.g., automatic device naming. Furthermore, we found that environmental changes are also a source of device data patterns, which provide additional associations. For example, using the framework we found the façade group for exterior light sensors. The façade group can be used to automatically find an alternative signal source to replace broken outdoor light sensors. Finally, the rule mining framework successfully retrieved the relative location of spatial sensors in all locations but the foyer.