Dynamic neural network based algorithm for context awareness in mobile computing

Authors:
Feng Gui;Nuannuan Zong;Malek Adjouadi
Affiliations:
Department of Electrical & Computer Engineering, Florida International University, Miami, FL;Department of Electrical & Computer Engineering, Florida International University, Miami, FL;Department of Electrical & Computer Engineering, Florida International University, Miami, FL
Venue:
ICCOM'05 Proceedings of the 9th WSEAS International Conference on Communications
Year:
2005

Citing 4
Cited 0

The LRU-K page replacement algorithm for database disk buffering

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Charting past, present, and future research in ubiquitous computing

ACM Transactions on Computer-Human Interaction (TOCHI) - Special issue on human-computer interaction in the new millennium, Part 1
Self-Organizing Maps

Self-Organizing Maps
Unsupervised clustering of ambulatory audio and video

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06

Quantified Score

Hi-index	0.00

Visualization

Abstract

Wireless network carriers are motivated to offer more services tailored to mobile users' needs. Context awareness is one of the value-added services that have attention from both mobile users and network carriers. However, context awareness requires collecting, analyzing, and understanding of mobile user characteristics, activities, location, surrounding environment, and so on. The objective of this paper is to design a dynamic neural network based algorithm updating the context cache weights. The context cache weights are generated to reflect the mobile user's context change. In addition, a data cache scheme is proposed to handle mobile user context. Particularly, we introduce the concept of virtual frame and reference frame on which the data cache scheme is based. The user context profiles that consist of mobile user characteristics, wireless device configuration, activities, and surrounding environment classify the context entities in the cache. As such, a systematic approach can be used to handle context awareness at the client site.