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 simultaneously, they typically arise from a single misbehaving component such as a failed radio or a crashed node that may, in turn, trigger a cascade 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 interactions between components. The underlying assumption is that individual 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 challenge is to uncover the global interaction patterns that leads to the problem, as opposed to chasing a local root cause. The chapter describes the above different techniques and concludes with a brief review of other troubleshooting work, not inspired by data mining literature.
| Original language | English (US) |
|---|---|
| Title of host publication | Managing and Mining Sensor Data |
| Publisher | Springer US |
| Pages | 429-458 |
| Number of pages | 30 |
| Volume | 9781461463092 |
| ISBN (Electronic) | 9781461463092 |
| ISBN (Print) | 1461463084, 9781461463085 |
| DOIs | |
| State | Published - Jul 1 2013 |
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Keywords
- Sensor networks
- data mining
- debugging
- interactive complexity
ASJC Scopus subject areas
- Computer Science(all)
Cite this
A survey of datamining methods for sensor network bug diagnosis. / Abdelzaher, Tarek; Han, Jiawei.
Managing and Mining Sensor Data. Vol. 9781461463092 Springer US, 2013. p. 429-458.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - A survey of datamining methods for sensor network bug diagnosis
AU - Abdelzaher, Tarek
AU - Han, Jiawei
PY - 2013/7/1
Y1 - 2013/7/1
N2 - 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 simultaneously, they typically arise from a single misbehaving component such as a failed radio or a crashed node that may, in turn, trigger a cascade 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 interactions between components. The underlying assumption is that individual 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 challenge is to uncover the global interaction patterns that leads to the problem, as opposed to chasing a local root cause. The chapter describes the above different techniques and concludes with a brief review of other troubleshooting work, not inspired by data mining literature.
AB - 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 simultaneously, they typically arise from a single misbehaving component such as a failed radio or a crashed node that may, in turn, trigger a cascade 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 interactions between components. The underlying assumption is that individual 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 challenge is to uncover the global interaction patterns that leads to the problem, as opposed to chasing a local root cause. The chapter describes the above different techniques and concludes with a brief review of other troubleshooting work, not inspired by data mining literature.
KW - Sensor networks
KW - data mining
KW - debugging
KW - interactive complexity
UR - http://www.scopus.com/inward/record.url?scp=84949178014&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949178014&partnerID=8YFLogxK
U2 - 10.1007/978-1-4614-6309-2_13
DO - 10.1007/978-1-4614-6309-2_13
M3 - Chapter
AN - SCOPUS:84949178014
SN - 1461463084
SN - 9781461463085
VL - 9781461463092
SP - 429
EP - 458
BT - Managing and Mining Sensor Data
PB - Springer US
ER -