Semantic Methods for Data Mining in Smart Spaces

This chapter shows the role of semantic methods in delivering AmI. The smart spaces paradigm applies ontological modeling for representing available IoT resources as shared information. This way, resources are virtualized by local information hubs, which are deployed on existing devices. The virtualization benefits from semantics since relations between resources are also represented, forming a semantic network. In turn, various ranking models can be implemented for information search and knowledge reasoning (e.g., based on such well-known algorithms as PageRank). The structural properties of the semantic network leads to advanced AmI support for constructing proactive services: discovery of certain structures (e.g., cycles) can be interpreted as formation of specific knowledge that initiates service construction and delivery.

Fog Computing Technology for Cooperative Information Processing in Edge-Centric Internet of Things Environments

This chapter introduces advances in fog computing technology for involving various participants—either small or large in capacity, either local or remote—into the service construction. Non-typical computational devices (compared with traditional computers, for example, laptops, desktops, servers)—such as smartphones, wireless routers, multimedia equipment, and consumer electronics—become aware of information processing in order to construct services essentially based on local resources of the IoT environment.

Evolution of the Smart Spaces Paradigm Toward the Semantic Web of Things

This chapter describes how the Smart-M3 platform evolved in the direction of supporting web standards (e.g., HTTP and Websockets) to be ready for the (Semantic) Web of Things. The latest step in the Smart-M3 progress is named SEPA (SPARQL Event Processing Architecture). Employing SEPA as a mean for semantic interoperability in the Web of Things means allowing heterogeneous devices to be discovered, accessed, and controlled through a set of SPARQL queries, subscriptions, and updates according to a given ontology. In this chapter, an ontology for the (Semantic) Web of Things is presented. Using web standards solves the issues of interoperability but poses new challenges with respect to the typical constraints of IoT applications.

Profiling and Personalization in Internet of Things Environments

The chapter considers problems of user personalization and resources competence modeling in the internet of things (IoT) environments. Creation of the user profiles and its utilization during the interaction of the user with IoT resources significantly increase the efficiency of such interaction. When the user generates a task to perform by the IoT resources, the formal model of this task is expanded by the relevant information in accordance with the user profile model. The obtained results should be presented to the user in accordance to his/her preferences from the user profile model. Resource competence profile should store information about the resource competencies and constraints that have to be satisfied to enable these competences. In this case, resource competence profiles automate their interaction in IoT environments.

Emerging Case Studies of Ambient Intelligence Services

Analysis of currently dominating and emerging use cases provide extremely valuable information for understanding of the key drivers of a technology development. In this chapter, the authors particularly focus on introducing and making an overview of the emerging case studies on top of the ambient intelligence technology. They discuss several case studies that illustrate opportunities and design details for development of highly personalized smart services. The chapter provides definition of the key enablers of the service smartness (e.g., location-awareness, design principles, and restrictions, etc.). The discussion on provided definitions and presented enablers is supported by a few use case examples in the field of e-tourism and e-healthcare. In addition, the chapter introduces the general principles of ZeroUI concept and role of virtual and augmented reality in delivering the new user experience. The chapter is targeted to clearly ground the book scope to the real-life use cases and most relevant trends.

Internet of Things Resources Interaction for Service Construction and Delivery

Semantic interaction support for internet of things (IoT) resources is a key point of the service construction and delivery for the users. Semantic interoperability between interacted resources provides possibilities for them to understand each other. One of the possible approaches to enrich the semantic interoperability is the ontology modeling. Every resource is described by an ontology. The ontology formally represents knowledge as a set of concepts within a domain, using a shared vocabulary to denote the types, properties, and interrelationships of those concepts. Based on the ontology matching techniques, resource ontologies are matched, and resources operate in accordance with this matching. Context is any information that can be used to characterize the situation of an entity. An entity in the considered case is the resource of IoT environment. It is proposed to use the ontologies to describe the context of resource and take this information for task performing. For the service construction, the coalitions of IoT resources that can jointly provide the needed service for a task performing need to be created.

Introduction to Ambient Intelligence in Internet of Things Environments and Cyber-Physical Systems

This chapter discusses current role and future prospects of ambient intelligence in development of applications for the internet of things environments. The authors provide an introduction to the field of ambient intelligence and specifically discuss what makes it so important to be a core element of cyber-physical systems. The main focus of the chapter is on providing analysis and the reasoning for development of smart spaces and delivering ambient intelligence to internet of things environments. The chapter provides definition and overview of recent trends and advances for service development with all identified key technological enablers of modern info-communication technologies, including data mining, big data analysis, recommendation systems, and so on. The main messages of the chapter are summarized by the conclusion section.