For example, if the knowledgebase contains an object classified as jammer, the reasoner will be able to infer that it has a transmitter. The reasoner then will be able to process the input facts and the axioms and answer queries based only on partial information about an object. e.g., if the knowledge base describes the radio communications domain, it will encode facts about some of the domain objects as axioms expressed in the language. An ontological language must have a program called “ reasoner (or inference engine)”, which can do more than just retrieve information from its knowledgebase, but it should be able to extract information that is only implicit in the knowledgebase. For an ontological language, we require more than a database. The answers need to be sound, i.e., the answers must be correct with respect to what the database contains. For example, in the context of relational databases, queries are expressed in the SQL language, and a database management system answers them by retrieving data stored in the database. Roughly, it means that there needs to be a computer program that can answer queries about information encoded in the language.
To be “understandable”, the language must also have formal, computer processable semantics. These definitions are then interpreted by generic inference engines that are bound to the same language (such as OWL), thus avoiding the need to rewrite the underlying software.įor ontologies to be processable by computers, they must be described in a language that has formal semantics. This is the consequence of bounding ontologies to a highly expressive language (such as OWL ) in which new terms can be defined on the fly. On the contrary, this kind of capability can be achieved by an ontological approach in which new terms and their definitions can be introduced dynamically.
#Hyperspaces baltimore software#
The above requirements cannot be achieved via static definitions of vocabularies (e.g., in XML or a relational database) since adding new terms to such vocabularies would require developing new software (for interpreting XML tags or relational schema and data). In case the access cannot be granted, the PE may respond with a list of opportunities, i.e., other channels, in which opportunistic access is possible. The interaction inside the radio between the main Controller and the PE requires a language for expressing the spectrum access requests and the responses to such requests. In the use cases considered in this paper, the objective of collaboration is to make a collaborative decision about the use of the RF spectrum-a crucial step in Electromagnetic Spectrum Management (ESM). The organization of the collaboration process must follow some policies that all the entities should obey. Collaboration implies the willingness of the entities to interact and make trade-offs with each other.
#Hyperspaces baltimore how to#
Thus, the question is how to implement collaboration rather than whether to support it. Collaboration is an inherent part of communications.
“Collaboration is defined as the process of two or more entities or organizations working together to complete a task or achieve a goal”. Since in this paper we are primarily focusing on the dynamic spectrum sharing (DSS) among the radios operating in the same RF environment-not necessarily being part of the same Radio Network-we need to cover the issues of collaboration. Two radios shown in the figure interact with the User, Sensor, Knowledge Base and other radios within a Radio Network using Application Programming Interfaces (APIs), depicted as bidirectional arrows. Figure 1 shows the context for a cognitive radio.
0 kommentar(er)
