HCC: Small: Integrating User Context and Conceptual Perceptions for Understandable Wireless Access on Open-Platform Smartphones

University Of Massachusetts Lowell, Lowell MA

Investigators

Abstract

This project will provide algorithms and toolkits to empower end users for better application experience when using open-platform smartphones over dynamic wireless access networks. A novel model-based diagnosis engine helps users to understand the access problems as application performance degrades. The cognition-aware feedback completes an execution-evaluation loop for the users to construct correct conceptual perceptions of the troubleshooting process and the root cause. Smartphones with customized software will be distributed to conduct user studies and evaluate the user-interaction effectiveness when dealing with wireless access problems. The combination of formal causality and state models is leveraged to troubleshoot complex wireless access problems. A unique contribution is to integrate application-level user context with network-level diagnostic models, as user mobility and physical environment are important factors of wireless access performance. Machine-learning algorithms are used for robust context inference using noisy data from on-device sensors. Norman's cognition framework is used for iterative presentation and perception of the troubleshooting process on small-screen smartphones with limited input methods. This work fills in the gap of providing diagnostic tools to the mobile users. The success of this project will result in new perception tools, which can be disseminated directly to the public through mobile application stores, to significantly enhance the often-misleading signal indicator on smartphones today. An interactive Web-based education portal will be developed to better involve students and online course modules will be introduced through UMass Lowell's Division of Continuing Studies and Corporate Education to reach IT professionals.