IEEE Internet of Things Journal · 2015 · 255 citations · 17 references
ComputingComputer SystemsInformation TechnologyInternet Of ThingsMobile ComputingSensor FusionContext-aware ComputingIot SensorsMobile Human-computer InteractionMobile SensingMuseum ArchitectureWearable Internet Of ThingsBluetooth Low EnergyBluetooth Low Energy LocalizationBluetooth Low Energy SensingContext AwarenessEnergy-efficient LocalizationInternet Of Things ApplicationsLocation AwarenessLocation-based Services
The new technologies characterizing the Internet of Things (IoT) allow realizing real smart environments able to provide advanced services to the users. Recently, these smart environments are also being exploited to renovate the users' interest on the cultural heritage, by guaranteeing real interactive cultural experiences. In this paper, we design and validate an indoor location-aware architecture able to enhance the user experience in a museum. In particular, the proposed system relies on a wearable device that combines image recognition and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. The localization information is obtained by a Bluetooth low energy (BLE) infrastructure installed in the museum. Moreover, the system interacts with the Cloud to store multimedia contents produced by the user and to share environment-generated events on his/her social networks. Finally, several location-aware services, running in the system, control the environment status also according to users' movements. These services interact with physical devices through a multiprotocol middleware. The system has been designed to be easily extensible to other IoT technologies and its effectiveness has been evaluated in the MUST museum, Lecce, Italy.
17
Martin A. Fischler, Robert C. Bolles · Communications of the ACM · 1981 · 25.5K citations · Full text
Object recognition from local scale-invariant features
David Lowe · 1999 · 16.2K citations
Robust wide-baseline stereo from maximally stable extremal regions
Jiřı́ Matas, Ondřej Chum, M. Urban et al. · Image and Vision Computing · 2004 · 3.7K citations