Determining and locating the closest available resources to mobile collaborators

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摘要

Nowadays, organizations include a large number of physical resources (e.g., meeting rooms, classrooms, and auditoriums) and computing ones (e.g., scanners, plotters, and handheld devices) distributed among different offices and buildings. Typically, these resources have to be shared among colleagues, because it is impossible for each collaborator to own private instances of all the different resources present in the organization. In this way, resource sharing gives collaborators the opportunity of not just lending their resources to other collaborators but also benefitting from the usage of resources they do not own. However, finding shared resources in a huge organization, without a proper technological support, can be a challenge for a member of staff and obviously really hard or even impossible for an external person. The main contribution of this paper is a service-oriented architecture called Resource Availability Management Services (RAMS), which is intended to facilitate the development of groupware applications that manage the availability and suitability of human, physical and computing resources. In the case of physical resources, the RAMS architecture allows determining the available resources closest to the requesting collaborator that satisfy his/her requirements and provides information about their physical location and the shortest path to reach them. To accomplish these goals, the proposed architecture relies on the services provided by three main components: (1) a Human Face Recognizer that allows identifying and locating collaborators in an organization, (2) an Ontology-based Matchmaker, which is able to determine a set of available and accessible resources that can satisfy a collaborator’s request, and (3) a Physical Resource Locator that relies on building topologies and the walking distance method to calculate high precision relative distances between collaborators and resources.

论文关键词:Context-aware groupware,Indoor location,Closest available resource,Human face recognition,Walking distance method,Topological model,Geographic information systems

论文评审过程:Available online 23 November 2012.

论文官网地址:https://doi.org/10.1016/j.eswa.2012.10.069