Context awareness

About: Context awareness is a research topic. Over the lifetime, 5790 publications have been published within this topic receiving 119944 citations.

A Survey on Artificial Intelligence Techniques in Cognitive Radio Networks

Abstract: Cognitive radio (CR) is the solution for the current spectral underutilized problems, Context awareness and environment awareness are the key functions of CR nowadays. A software radio with reconfiguration capacity will become Cognitive Radio by imparting intelligence to SDR using Artificial Intelligence Techniques. There are processes in CR such as spectrum sensing, monitoring, and management involves the use of AI techniques. Artificial Intelligence (AI) is directed along with “cognitive” functions for better learning and classification. Recently deep learning involves more “self-learning” algorithms without any supervision. Hence it is important to discuss the various AI techniques for better resource optimization in CR networks. Optimization parameters are different for different techniques depend upon the radio environment. The type of AI technique for a particular application is random.

Open mobile miner: a toolkit for building situation-aware data mining applications

Abstract: In organizational computing and information systems, data mining techniques have been widely used for analyzing customer behavior and discovering hidden patterns. Mobile Data Mining is the process of intelligently analyzing continuous data streams on mobile devices. The use of mobile data mining for real-time business intelligence applications can be greatly advantageous. Past research has shown that resource-aware adaptation of data stream mining can significantly improve the continuity of data mining operations in mobile environments. The key underlying premise is that by varying the accuracy of the analysis process in accordance with changing available resource levels, the longevity and continuity of mobile data mining applications is ensured. In this article we qualitatively extend the notion of resource-aware adaptation of mobile data mining to holistically enable situation-awareness feature for user applications. We then present a novel generic toolkit that enables building situation and resource-aw...

Semantically Augmented Exploitation of Pervasive Environments by Intelligent Agents

Abstract: Mobile devices can be used to improve the exploitation of reality by the user in pervasive environments. In this paper we present a software framework for augmenting the user's perception of reality by innovative applications and services. Mobile applications enable handheld devices to get information from pervasive sensors, as their own perceptors, in order to let the users benefit of context awareness. Furthermore they exploit information from the field to improve delivery of ubiquitous services and presentation of multimedia contents. We implemented tools to support experts in the domain of the Cultural Heritage to augment the archaeological site with these kind of services. Besides we developed a tourist guide that supports user's visit and enhances his cultural experience by an intelligent discovering and delivery of those information, which are relevant to the user's profile and to the environment conditions. In our approach software agents adapt themselves in response to a changing environment and are able to respond differently, creating entirely new plans, and thus changing their own behavior depending on their perceptions.

Schema matching for context-aware computing

Abstract: Context-aware computing is a key paradigm of ubiquitous computing in which applications automatically adapt their operations to dynamic context data from multiple sources. Managing a number of distributed sources, a middleware that facilitates the development of context-aware applications must provide a uniform view of all these sources to the applications. Local schemas of context data from individual sources need to be matched into a set of global schemas in the middleware, upon which applications can issue context queries to acquire data. In this paper, we study this problem of schema matching for context-aware computing. We propose a multi-criteria algorithm to determine candidate attribute matches between two schemas. The algorithm adaptively adjusts the priorities of different criteria based on previous matching results to improve the efficiency and accuracy of succeeding operations. We further develop an algorithm to categorize a new local schema into one of the global schemas whenever possible via a shared attribute dictionary. Our results based on schemas from real-world websites demonstrate the good matching accuracy achieved by our algorithms.