Database Reference
In-Depth Information
Chapter 13
ASURVEYOFDATAMININGMETHODS
FOR SENSOR NETWORK BUG
DIAGNOSIS
Tarek Abdelzaher
Department of Computer Science
University of Illinois at Urbana Champaign
zaher@cs.illinois.edu
Jiawei Han
Department of Computer Science
University of Illinois at Urbana Champaign
hanj@cs.illinois.edu
Abstract
This chapter surveys recent debugging tools for sensor networks that are
inspired by data mining algorithms. These tools are motivated by the
increased complexity and scale of sensor network applications, making
it harder to identify root causes of system problems. At a high level,
debugging solutions in the domain of sensor networks can be classified
according to their goal into two distinct categories; (i) solutions that
attempt to localize errors to a single node, component, or code snippet,
and (ii) solutions that attempt to identify a global pattern that causes
misbehavior to occur. The first category inherits the usual wisdom that
problems are often localized. It is unlikely for independent failures to
coinside. Hence, while many different trouble symptoms may occur si-
multaneously, they typically arise from a single misbehaving component
such as a failed radio or a crashed node that may, in turn, trigger a cas-
cade of other problems. In contrast, the second category of solutions
is motivated by interactive complexity problems. They seek to uncover
bugs in networked sensing systems that arise due to unexpected inter-
actions between components. The underlying assumption is that indi-
vidual components are easier to test, which ensures that they work well
in isolation. Therefore, practical software systems seldom fail due to a
single poorly-coded component. Rather, they fail due to an unexpected
interaction pattern between
individually well-behaved
components. The
