Lightweight data and knowledge exchange for pervasive environments1

Abstract

Pervasive environments are physical spaces saturated with devices collecting data, controlling the environment, and interacting with users. These environments support human users in their everyday tasks so that the users can focus on their own tasks and access services and resources whenever and wherever they want. Such environments are also called smart spaces. Knowledgebased systems would enable realizing a variety of intelligent applications for pervasive environments. Generally, such systems recognize the situation in the environment from sensor data and utilize automated reasoning techniques to respond to the situation and the needs of the users.However, building knowledge-based systems for pervasive environments presents challenges. This dissertation focuses on the challenge of data and knowledge representations. Knowledgebased systems utilize expressive knowledge representations that are verbose and require sufficient resources in order to use them. Most devices in pervasive environments cannot handle these representations as the devices have limited resources for computation, storage, and communication. The main aim of this dissertation is to tackle this challenge. That is, on the one hand, pervasive environments demand data and knowledge representations that do not require many resources from the resource-constrained devices; and on the other hand, the representations should be compatible with the knowledge-based systems. Specifically, a general solution is required that enables many applications to use the same data with minimal effort from application developers.This dissertation presents a novel representation, Entity Notation (EN), to tackle these challenges. EN is designed as a general lightweight representation for data and knowledge. EN expresses entities, their properties, and property values. This structure resembles the triple structure of Resource Description Framework (RDF) and Web Ontology Language (OWL). Hence, sensor data in EN syntax can be transformed into common knowledge models in a straightforward manner and utilized with ease by knowledge-based systems. EN Schema is designed for transferring advanced knowledge models. Moreover, EN also offers an approach to shorten the format with templates and prefixes. This way, EN can be utilized by resourceconstrained devices and environments. Our evaluation verifies that small devices can utilize EN to transfer data and knowledge to devices realizing intelligent functions, such as inference. Moreover, the expressive power of EN is comparable with the alternative representations. Finally, resource consumption is verified by prototypes. Based on the evaluation, we can conclude that EN can facilitate harnessing the full potential of pervasive environments.Keywords: Entity Notation, knowledge-based systems, ontology, reasoning, Resource Description Framework, resourceconstrained devices, semantics Su, Xiang, Kevyen datan ja tietämyksen esitystavat jokapaikan tietotekniikan sovelluksille.Oulun yliopiston tutkijakoulu; Oulun yliopisto, Tieto-...

Distribution of Semantic Reasoning on the Edge of Internet of Things

“…This structure can be interpreted as: "object o stands in relationship p with subject s". RDF can be represented in different serialization formats including RDF/XML [16], JSON for Linked Data (JSON-LD) [17], N-Triples [18], N-Quads [19], Turtle [20], RDFa [21], Notation 3 (N3) [22] and Entity Notation (EN) [23] [24].…”

“…The first architecture, "Cloud Reasoning Architecture" (CRA), places a semantic reasoner on the Cloud (Figure 2). The IoT nodes encode the raw data into four alternative syntaxes of RDF model: RDF/XML, JSON-LD, N3, and short EN format [23]. Short EN format [23] compresses the data size by replacing constant information with templates and prefixes and we add one extra step to transform short EN to Turtle.…”

Transferring Remote Ontologies to the Edge of Internet of Things Systems

“…Entity Notation (EN) and Entity Notation Schema (EN Schema) [18] are lightweight knowledge representations for resource-constrained devices. Resource usage of CPU, memory, bandwidth, and energy for encoding and decoding these representations are considered at design time.…”