Towards an Improved Hoarding Procedure in a Mobile Environment

Frequent disconnection has been a critical issue in wireless network communication therefore causing excessive delay in data delivery. In this paper, we formulated a management mechanism based on computational optimization to achieve efficient and fast computation in order to reduce inherent delay during the hoarding process. The simulated result obtained is evaluated based on hoard size and delivery time. Keywords: Hoarding Procedure, Mobile computing Environment and Computational Optimization

Supporting disconnection operations through cooperative hoarding

Mobile clients often need to operate while disconnected from the network due to limited battery life and network coverage. Hoarding supports this by fetching frequently accessed data into clients' local caches prior to disconnection. Existing work on hoarding have focused on improving data accessibility for individual mobile clients. However, due to storage limitations, mobile clients may not be able to hoard every data object they need. This leads to cache misses and disruption to clients' operations. In this paper, a new concept called cooperative hoarding is introduced to reduce the risks of cache misses for mobile clients. Cooperative hoarding takes advantage of group mobility behaviour, combined with peer cooperation in ad-hoc mode, to improve hoard performance. Two cooperative hoarding approaches are proposed that take into account access frequency, connection probability, and cache size of mobile clients so that hoarding can be performed cooperatively. Simulation results show that the proposed methods significantly improve cache hit ratio and provides better support for disconnected operations compared to existing schemes

Adaptive Image Classification on Mobile Phones

The advent of high-performance mobile phones has opened up the opportunity to develop new context-aware applications for everyday life. In particular, applications for context-aware information retrieval in conjunction with image-based object recognition have become a focal area of recent research. In this thesis we introduce an adaptive mobile museum guidance system that allows visitors in a museum to identify exhibits by taking a picture with their mobile phone. Besides approaches to object recognition, we present different adaptation techniques that improve classification performance. After providing a comprehensive background of context-aware mobile information systems in general, we present an on-device object recognition algorithm and show how its classification performance can be improved by capturing multiple images of a single exhibit. To accomplish this, we combine the classification results of the individual pictures and consider the perspective relations among the retrieved database images. In order to identify multiple exhibits in pictures we present an approach that uses the spatial relationships among the objects in images. They make it possible to infer and validate the locations of undetected objects relative to the detected ones and additionally improve classification performance. To cope with environmental influences, we introduce an adaptation technique that establishes ad-hoc wireless networks among the visitors’ mobile devices to exchange classification data. This ensures constant classification rates under varying illumination levels and changing object placement. Finally, in addition to localization using RF-technology, we present an adaptation technique that uses user-generated spatio-temporal pathway data for person movement prediction. Based on the history of previously visited exhibits, the algorithm determines possible future locations and incorporates these predictions into the object classification process. This increases classification performance and offers benefits comparable to traditional localization approaches but without the need for additional hardware. Through multiple field studies and laboratory experiments we demonstrate the benefits of each approach and show how they influence the overall classification rate.Die Einführung von Mobiltelefonen mit eingebauten Sensoren wie Kameras, GPS oder Beschleunigungssensoren, sowie Kommunikationstechniken wie Bluetooth oder WLAN ermöglicht die Entwicklung neuer kontextsensitiver Anwendungen für das tägliche Leben. Insbesondere Applikationen im Bereich kontextsensitiver Informationsbeschaffung in Verbindung mit bildbasierter Objekterkennung sind in den Fokus der aktuellen Forschung geraten. Der Beitrag dieser Arbeit ist die Entwicklung eines bildbasierten, mobilen Museumsführersystems, welches unterschiedliche Adaptionstechniken verwendet, um die Objekterkennung zu verbessern. Es wird gezeigt, wie Ojekterkennungsalgorithmen auf Mobiltelefonen realisiert werden können und wie die Erkennungsrate verbessert wird, indem man zum Beispiel ad-hoc Netzwerke einsetzt oder Bewegungsvorhersagen von Personen berücksichtigt