MapUme: Scalable middleware for location aware computing applications

Najib, Warsun; Klepal, Martin; Wibowo, Sigit Basuki

doi:10.1109/ipin.2011.6071950

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…The eventual goal is to integrate this approach with occupant localization systems such as [12], and predictive control systems such as [13]. Using occupant localization data, a system based on our approach could provide the predictions necessary for more energy efficient building control.…”

Section: Discussionmentioning

confidence: 99%

Predicting Occupant Locations Using Association Rule Mining

Ryan

Brown

2013

Research and Development in Intelligent Systems XXX

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Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. In this paper we present two approaches to occupant location prediction based on association rule mining which allow prediction based on historical occupant movements and any available real time information, or based on recent occupant movements. We show how association rule mining can be adapted for occupant prediction and evaluate both approaches against existing approaches on two sets of real occupants.

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Section: Discussionmentioning

confidence: 99%

Predicting Occupant Locations Using Association Rule Mining

Ryan

Brown

2013

Research and Development in Intelligent Systems XXX

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“…Al. [32] é apresentado um middleware para localização em ambientes internos chamado de MapUme. O MapUme permite a fusão de dados provenientes de diversos tipos de sensores, em que esse processo acontece através da implementação de uma engine de fusão que é construída através do padrão de projeto fábrica abstrata.…”

Section: Figura 1 Modelo Arquitetural Da Location Stackunclassified

IndoLoR -- Uma Plataforma Adaptável para Localização em Ambientes Internos

Melo

Aquino

2018

RITA

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Resumo: Location systems have become increasingly part of people's lives. For outdoor environments, GPS appears as standard technology, widely disseminated and used. However, people usually spend most of their daily time in indoor environments, such as: hospitals, universities, factories, buildings, etc. In these environments, GPS does not work properly causing an inaccurate positioning. Currently, to perform the location of people or objects in indoor environments no single technology could reproduce for indoors the same result achieved by GPS for outdoors environments. Due to this, it is necessary to consider use of information from multiple sources using different technologies. Thus, this work aims to build an Adaptable Platform for Indoor location. Based on this goal, the IndoLoR platform is proposed. This platform aims to allow information reception from different sources, data processing, data fusion, data storage and data retrieval for the indoor location context. The proposed platform is evaluated through a case study examining aspects related to easiness of adaptation and performance.

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“…In Najib et al [8], a middleware for location in indoor environments called MapUme was presented. The MapUme allows the fusion of data from various types of sensors.…”

Section: Related Workmentioning

confidence: 99%

An Adaptable Approach for Indoor Location

Melo

Aquino

2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-In general people often spend 80-90% of their time in indoor environments, which include shopping malls, libraries, airports, universities, schools, offices, factories, hospitals, among others. In these environments, GPS does not work properly, causing inaccurate positioning. Currently, when performing the location of people or objects in indoor environments, no single technology can reproduce the same results achieved by the GPS for outdoor environments. One of the main reasons for this is the high complexity of indoor environments where, unlike outdoor spaces, there is a series of obstacles such as walls, equipment and even people. Due to this, it is necessary to consider the use of information from multiple sources using different technologies. Thus, this work proposes an adaptable approach for indoor location, which allows the use and combination of different technologies, techniques and methods in this context.

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MapUme: Scalable middleware for location aware computing applications

Cited by 7 publications

References 5 publications

Predicting Occupant Locations Using Association Rule Mining

Predicting Occupant Locations Using Association Rule Mining

IndoLoR -- Uma Plataforma Adaptável para Localização em Ambientes Internos

An Adaptable Approach for Indoor Location

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