Sensor Data Fusion for Context-Aware Computing Using Dempster-Shafer Theory

Towards having computers understand human users’ “context” information, this dissertation proposes a systematic context-sensing implementation methodology that can easily combine sensor outputs with subjective judgments. The feasibility of this idea is demonstrated via a meeting-participant’s focus-of-attention analysis case study with several simulated sensors using prerecorded experimental data and artificially generated sensor outputs distributed over a LAN network. The methodology advocates a top-down approach: (1) For a given application, a context information structure is defined; all lower-level sensor fusion is done locally. (2) Using the context information architecture as a guide, a context sensing system with layered and modularized structure is developed using the Georgia Tech Context Toolkit system, enhanced with sensor fusion modules, as its building-blocks. (3) Higher-level context outputs are combined through “sensor fusion mediator” widgets, and the results populate the context database. The key contribution of this thesis is introducing the Dempster-Shafer theory of evidence as a generalizable sensor fusion solution to overcome the typical contextsensing difficulties, wherein some of the available information items are subjective, sensor observations’ probability (objective chance) distribution is not known accurately, and the sensor set is dynamic in content and configuration. In the sensor fusion implementation, this method is further extended in two directions: (1) weight factors are introduced to adjust each sensor's voting influence, thus providing an “objective” sensor performance justification; and (2) when the ground truth becomes available, it is used to dynamically adjust the sensors' voting weights. The effectiveness of the improved Dempster-Shafer method is demonstrated with both the prerecorded experimental data and the simulated data.