Using ZigBee in Ambient Intelligence Learning Scenarios

Using ZigBee in Ambient Intelligence Learning Scenarios

Óscar García (University of Salamanca, Spain), Ricardo S. Alonso (University of Salamanca, Spain), Dante I. Tapia (University of Salamanca, Spain) and Juan M. Corchado (University of Salamanca, Spain)
Copyright: © 2012 |Pages: 13
DOI: 10.4018/jaci.2012070103
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Abstract

The inclusion of Information and Communication Technologies, especially mobile devices, in learning environments has allowed both the emergence of new ways of learning and the adaptation of traditional teaching methods. In this sense, Ambient Intelligence (AmI) paradigm represents a promising approach that can be successfully applied to education. Pervasive computing, context and location awareness are AmI features that can allow students to receive customized information in a transparent way. Fortunately, there are several technologies that can help to gather such information. In this regard, Real-Time Locating Systems (RTLS) is a key technology that can improve context-awareness in AmI-based systems. This paper presents the use of a novel RTLS based on ZigBee technology that provides users’ positions in order to enhance context information in learning applications. This way, this system allows customizing the content offered to the users without their explicit interaction, as well as the granularity level provided by the system.
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1. Introduction

Our society is undergoing a technological evolution difficult to measure, moreover when new technologies, devices or services evolve even faster than the users’ needs. Over the past years, the advances on personal computers, communication protocols, Internet or mobile devices have changed our world in a political, economic and social way. In this sense, mobile devices are becoming more ubiquitous and usable, allowing new ways of interaction and adding context-awareness and location-based capabilities for a wide diversity of application scenarios (Aarts & de Ruyter, 2009).

Ambient Intelligence (AmI) is a multidisciplinary area focused on new interaction ways between people and technology (Remagnino & Foresti, 2005). The main objective of Ambient Intelligence is enhancing the relation between users and their environment. In this sense, AmI-based systems have to take into consideration the context in which they are used (Traynor, Xie, & Curran, 2010). That is, they must have context-awareness properties and adapt their behavior without the need of users to make an explicit decision or interact with them, allowing applications to be more usable and efficient (Baldauf, Dustdar, & Rosenberg, 2007). People, places and objects are recognized as the three main entities when dealing with Ambient Intelligence. The place where the user is and the objects that surround him determine the behavior of the system, thus obtaining in a natural way personalized, adaptive and immersive applications (Weber, Rabaey, & Aarts, 2005).

On the other hand, mobile devices, such as laptops, tablets or smart phones, offer a wide range of possibilities to create new AmI-based systems. One important feature of these devices is the ability to know their position, which includes the location of users themselves and any other object that is part of the environment (Weber, Rabaey, & Aarts, 2005). However, the use of these devices in context-aware applications requires locating them more precisely. In this sense, Real-Time Locating Systems (RTLS) acquire a great importance in order to improve the applications based on the knowledge of the relative position of each user or object at any time.

Education has not stood aside from these advances, allowing the emergence of new ways of learning by improving classic methods through the use of technology or simply by sharing information in a different way. Mobile Learning has become the umbrella under which new ways of learning have emerged, including areas such as Mobile Computer Supported Collaborative Learning (MCSCL), based on traditional CSCL, Context-Aware Pervasive Learning or, more recently, Location-Based Learning. There are several approaches proposed by the scientific community in these research areas which share a common element: the use of mobile devices and wireless communications (Roschelle, 2003). In this sense, museums are one of the places where AmI offers all its potential for education (Ramos, Augusto, & Shapiro, 2008).

Context-aware and Location-based learning benefit any activity that takes place in a museum as these scenarios are environments where users receive a wealth of information from many sources. New information and communication technologies facilitate that the characteristics and other related information about art-works can be offered in a more understandable, attractive and easy way to students. In this sense, the context information becomes relevant in order to personalize any activity for each student at every moment (Raptis, Tselios, & Avouris, 2005). Thus, RTLS are presented as a resource that greatly improves context-awareness in AmI applications as these systems provide the position of every static or dynamic object that interacts throughout the scenario. There are different technologies that can be used when designing and deploying an RTLS, such as Global Positioning System (GPS) (Abowd, Atkenson, Hong, Long, Kooper, & Pinkerton, 2006), Infrared (IR) Pointing Systems (Oppermann & Specht, 2000), Passive and Active Radio-Frequency Identification (RFID) (Curran & Norrby, 2009), Wireless Local Area Networks (WLANs) (Cheverst, Davies, Mitchell, & Smith, 2000), or Near Field Communication (NFC) (Blöckner, Danti, Forrai, Broll, & De Luca, 2009).

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