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rise to unique interactions and failure modalities that are harder to learn
from and generalize.
Since a user or a system analyst may have good knowledge on where
to find bugs or how to control the search space in the debugging process,
it could also be interesting to investigate how to integrate user-specified
constraints into the debugging process. Constraint-based mining of as-
sociation or correlation rules has been studied in data mining literature.
However, there is still lack of study on how to perform constraint-based
bug analysis in sensor networks, that is, how to use user-specified con-
straints to influence the process of search and analysis of potential bugs.
This is an important challenge in integrating data mining with sensor
network debugging.
More generally, the interaction between sensor network debugging
tools and users is an important topic. Rather than aiming to replace the
human entirely, tools should leverage and augment human capabilities.
Interactions with a user could, for example, take the form of a progres-
sive drill-down pattern, where the cause of a problem is incrementally
unveiled via a series of progressive refinement steps. This pattern has
significant scalability implications. Rather than searching a very large
problem space exhaustively at the outset, the tool would cover it at a
high level, then prune significant portions before drilling down into a
subspace at the next level of detail. Hence, an interesting challenge is
to design tools that use minimum resources by exloiting some form of
progressive drill-down. Solving the above challenges can significantly
impact the cost of development and ownership of networked sensing sys-
tems, which may in turn increase the scope of potential applications, as
well as eventually the reliability of deployed sensing systems.
References
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S. George, L. Gu, T. He, S. Krishnamurthy, L. Luo, S. Son,
J. Stankovic, R. Stoleru, and A. Wood. Envirotrack: Towards
an environmental computing paradigm for distributed sensor net-
works. In
Proceedings of the 24th International Conference on
Distributed Computing Systems (ICDCS'04)
, ICDCS '04, pages
582-589, Washington, DC, USA, 2004. IEEE Computer Society.
[2] R. Agrawal and R. Srikant. Fast algorithms for mining association
rules. In
Proceedings of the Twentieth International Conference on
Very Large Data Bases (VLDB'94)
, pages 487-499, 1994.
[3] M. K. Aguilera, J. C. Mogul, J. L. Wiener, P. Reynolds, and
A. Muthitacharoen. Performance debugging for distributed sys-
