Context awareness

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

Delineating ‘pervasiveness’ in pervasive information systems: a taxonomical framework and design implications

Abstract: Pervasive Information Systems (IS) exemplify a paradigm where Information Technology is embedded in the physical environment, capable of accommodating user needs and wants when desired. Pervasive IS differ from IS that are based on the desktop paradigm in that they encompass a complex, dynamic environment composed of multiple artifacts, capable of perceiving contextual information and supporting user mobility. Building on the novel properties of pervasive IS, we propose a taxonomical framework assessing the level of ‘pervasiveness’ in a given information system. The framework consists of three interweaving dimensions, namely ubiquity (encompassing mobility and heterogeneity), diffusion (encompassing invisibility and interactivity) and contextual awareness. The framework may be employed either to prospectively inform the design of pervasive IS (by pointing towards pertinent design considerations) or to retrospectively assess the pervasiveness of a system and identify improvement opportunities. We demonstrate both uses, firstly by discussing design priorities for ex ante IS evaluation and secondly by showcasing ex post assessments of a domestic and a corporate pervasive IS.

A user context approach for adaptive and distributed GIS

Abstract: The research presented in this paper introduces a user context approach for collaborative and adaptive Geographical Information System (GIS). The proposed model is part of a framework based on a multi-dimensional contextual approach that combines user, geographical and device contexts. The spatial properties of the GIS components categorize different configurations as a support for the derivation of user groups. The interfaces and functionalities offered by the adaptive GIS are generalized within each group, and derived from the interface usages. The spatial behaviors that reflect user experiences within a group favor collaborative exchanges. A prototype applied to maritime navigation validates the approach and the algorithms developed.

Bio-Interactive Healthcare Service System Using Lifelog Based Context Computing

Abstract: Intelligent bio-sensor information processing was developed using lifelog based context aware technology to provide a flexible and dynamic range of diagnostic capabilities to satisfy healthcare requirements in ubiquitous and mobile computing environments. To accomplish this, various noise signals were grouped into six categories by context estimation and effectively reconfigured noise reduction filters by neural network and genetic algorithm. The neural network-based control module effectively selected an optimal filter block by noise context-based clustering in running mode, and filtering performance was improved by genetic algorithm in evolution mode. Due to its adaptive criteria, genetic algorithm was used to explore the action configuration for each identified bio-context to implement our concept. Our proposed Bio-interactive healthcare service system adopts the concepts of biological context-awareness with evolutionary computations in working environments modeled and identified as bio-sensors based environmental contexts. We used an unsupervised learning algorithm for lifelog based context modeling and a supervised learning algorithm for context identification.

Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies, Multiagent System Paradigm, and Natural Ecosystems

Abstract: Wireless sensor networks (WSNs) are key components in the emergent cyber physical systems (CPSs). They may include hundreds of spatially distributed sensors which interact to solve complex tasks going beyond their individual capabilities. Due to the limited capabilities of sensors, sensor actions cannot meet CPS requirements while controlling and coordinating the operations of physical and engineered systems. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and communication mechanisms of living organisms. By mapping these organisms onto sensors and ecosystems onto WSNs, we highlight shortcomings that prevent WSNs from delivering the capabilities of ecosystems at several levels, including structure, topology, goals, communications, and functions. We then propose an agent-based architecture that migrates complex processing tasks outside the physical sensor network while incorporating missing characteristics of autonomy, intelligence, and context awareness to the WSN. Unlike existing works, we use software agents to map WSNs to natural ecosystems and enhance WSN capabilities to take advantage of bioinspired algorithms. We extend our architecture and propose a new intelligent CPS framework where several control levels are embedded in the physical system, thereby allowing agents to support WSNs technologies in enabling CPSs.

Ambient intelligence framework for context aware adaptive applications

Abstract: Despite recent turbulence of the digital economy, the information society continues its progress. Information and communication technologies (ICT) are increasingly entering in all aspects of our life and in all sectors, opening a world of unprecedented scenarios where people interact with electronic devices embedded in environments that are sensitive and responsive to the presence of users. These context-aware environments combine ubiquitous information, communication, with enhanced personalization, natural interaction and intelligence. A critical issue, common in most of applications framed inside domotic systems or ambient intelligence, is the approach to automatically detect context from wearable or environmental sensor systems and to transform such information for achieving personalized and adaptive services. Most of the flexible and robust systems use probabilistic detection algorithms that require extensive libraries of training; in this work we experiment with a prototype framework based on intelligent agents skilled to capture user habits, identify requests, and apply the artefact-mediated activity through hybrid approaches, featuring adaptive fuzzy control strategy and biometric techniques.