Olena Kaykova

UbiRoad: Semantic Middleware for Context-Aware Smart Road Environments

A smart road environment is such a traffic environment that is equipped with all necessary facilities to enable seamless mobile service provisioning to the users. However, advanced sensors and network architectures deployed within the traffic environment are insufficient to make mobile service provisioning autonomous and proactive, thus minimizing drivers’ distraction during their presence in the environment. For that, an Intelligent Transportation System, which is operating on top of numerous sensor and access networks and governing the process of mobile services provisioning to the users in self-managed and proactive way, must be deployed. Specifically, such system should provide solutions to the following two interoperability problems: interoperability between the in-car and roadside devices produced and programmed by different vendors and/or providers, and the need for seamless and flexible collaboration (including discovery, coordination, conflict resolution and possibly even negotiation) amongst the smart road devices and services. To tackle these problems, in this paper we propose UbiRoad middleware intending utilization of semantic languages and semantic technologies for declarative specification of devices’ and services’ behavior, application of software agents as engines executing those specifications, and establishment of common ontologies to facilitate and govern seamless interoperation of devices, services and humans. The main contribution of the paper includes the requirements and the architecture of complex traffic management systems and shows how such systems may benefit from utilization of semantic and agent technologies.

General Adaption Framework: Enabling Interoperability for Industrial Web Resources

Integration of heterogeneous applications and data sources into an interoperable system is one of the most relevant challenges for many knowledge-based corporations nowadays. Development of a global environment that would support knowledge transfer from human experts to automated Web services, which are able to learn, is a very profit-promising and challenging task. The domain of industrial maintenance is not an exception. This paper outlines in detail an approach for adaptation of heterogeneous Web resources into a unified environment as a first step toward interoperability of smart industrial resources, where distributed human experts and learning Web services are utilized by various devices for self monitoring and self diagnostics. The proposed General Adaptation Framework utilizes a potential of the Semantic Web technology and primarily focuses on the aspect of a semantic adaptation (or mediation) of existing widely used models of data representation to RDF-based semantically rich format. To perform the semantic adaptation of industrial resources, the approach of two-stage transformation (syntactical and semantic) is elaborated and implemented for monitoring of a concrete industrial device with underlying XML-based data representation model as a use case.

RgbDF: Resource Goal and Behaviour Description Framework

Agent-oriented approach has proven to be very efficient in engineering complex distributed software environments with dynamically changing conditions. The efficiency of underlying modelling framework for this domain is undoubtedly of a crucial importance. Currently, a model-driven architecture has been the most popular and developed for purposes of modelling different aspects of multi-agent systems, including behaviour of individual agents. UML is utilized as a basis for this modelling approach and variety of existing UML-based modelling tools after slight extension are reused. This paper proposes an ontology-driven approach to modelling agent behaviour as an emerging paradigm that originates from the Semantic Web wave. The proposed approach aims at modelling a proactive behaviour of (web-)resources through their representatives: software agents. In general, the presented research puts efforts into investigation of beneficial features of ontology-based agent modelling in comparison with conventional model-driven approaches.