The Internet of Things (IoT) is an emergent paradigm characterized by a plethora of smart objects connected to the Internet. An inherent characteristic of IoT is the high heterogeneity and the wide distribution of objects, thereby calling for ways to describe in an unambiguous and machine-interpretable way the resources provided by objects, their properties, and the services they offer. In this context, discovery services play a significant role as they allow clients (middleware platforms, end-users, applications) to retrieve available resources based on appropriate search criteria, such as resource type, capabilities, location, and Quality of Context (QoC) parameters. To cope with these concerns, we introduce QoDisco, a QoC-aware discovery service relying on multipleattribute searches, range queries, and synchronous/asynchronous operations. QoDisco also comprises an ontology-based information model for semantically describing resources, services, and QoC-related information. In this paper, we describe the QoDisco architecture and information model as well as an evaluation of the search procedure in an urban air pollution monitoring scenario.
Systems-of-systems (SoS) represent a class of systems resulted from the interaction among independent systems that cooperate to form a larger and more complex system aiming at accomplishing global missions. An inherent characteristic of SoS is the high heterogeneity of their constituent systems, which are distributed, independent, and developed with different technologies. In addition, SoS are highly dynamic, so that their constituents are often partially known or even unknown at design time. As a consequence, these constituent systems need to be discovered, selected, and composed at runtime towards identifying the proper arrangements that contribute to the accomplishment of the global missions of the SoS. In this paper, we present the results of a systematic mapping aimed to investigate the existing approaches to discover and compose constituent systems within an SoS. Besides providing an overview of the state of the art on these topics, we shed light on important issues to be addressed by future research towards a more effective development of SoS.
With the Internet of Things (IoT), applications should interact with a huge number of devices and retrieve context data produced by those objects, which have to be discovered and selected a priori. Due to the number, heterogeneity, and dynamicity of resources, discovery services are required to consider many selection criteria, e.g., device capabilities, location, context data type, contextual situations, and quality. In this paper, we describe QoDisco, a semantic-based discovery service that addresses this requirement in IoT. QoDisco is composed of a set of repositories storing resource descriptions according to an ontology-based information model and it provides multi-attribute and range querying capabilities. We have evaluated different approaches to reduce the inherent cost of semantic search, namely parallel interactions with multiple repositories and publish-subscribe interactions. This paper also reports the results of some performance experiments on QoDisco with respect to these approaches to handle resource discovery requests in IoT.
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