Inconsistency Detection and Resolution for Context-Aware Pervasive Computing

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Inconsistency Detection and Resolution for Context-Aware Pervasive
Computing"

By

Mr. Chang Xu


Abstract

Pervasive computing environments are often noisy and subject to change.
Although software should be responsive to contexts by changing their
behaviors, the contexts themselves may be abnormal or imprecise. This
results in context inconsistencies, that is, the conflicts among contexts.
Context inconsistencies may set such software into a wrong state or lead
software to wrongly adjust their behaviors. It is desirable to detect and
then resolve these inconsistencies in time to prevent software from
misbehaving.

One popular approach is to detect inconsistencies when contexts breach
certain consistency constraints. Existing constraint checking techniques
recheck the entire expression of each affected constraint upon context
changes. However, when a changed context affects only a constraint's
sub-expression, rechecking the entire expression introduces delays to
detecting other inconsistencies. This thesis proposes a formal model and
its supporting algorithms that identify the parts of previous checking
results that are reusable without missing context inconsistencies
identifiable via entire rechecking. This enables us to combine the
reusable and rechecked parts to produce the final results efficiently. Our
evaluation reports a more than fifteenfold performance improvement with
our approach against conventional approaches for detecting context
inconsistencies.

An important, follow-up step is to resolve detected inconsistent contexts
automatically for applications. However, the effectiveness of existing
resolution strategies is compromised by their formulated assumptions that
do not fully hold in practice. To different extents, this makes
applications using the resolved contexts less context-aware than the ideal
case. This thesis proposes the drop-bad and the impact-oriented
strategies. With the same goal of protecting context-awareness, the two
strategies are formulated on an intuitive observation and the
applications' situation specifications, respectively. Our evaluation
reports that both strategies protected at least 15% more situations for
context-aware applications than existing resolution strategies without
affecting context inconsistency resolution.


Date:			Thursday, 31 July 2008

Time:			10:30a.m.-12:30p.m.

Venue:			Room 3501
			Lifts 25-26

Chairman:		Prof. Mitchell Tseng (IELM)

Committee Members:	Prof. Shing-Chi Cheung (Supervisor)
			Prof. Lionel Ni
			Prof. Charles Zhang
			Prof. Danny Tsang (ECE)
			Prof. Francis Lau (Comp. Sci., HKU)


**** ALL are Welcome ****