A New Representation Structure for Mining Association Rules from Wireless Sensor Networks

Boukerche, Azzedine; Samarah, Samer

doi:10.1109/wcnc.2007.529

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…Boukerche et al [16,17] introduced sensor-association rules as an attempt to extract a pattern regarding the sensor nodes, rather than the area monitored by the WSN. The main difference between sensor association rules and the other techniques was that the data used in the mining process were behavioral data (i.e., meta-data describing the nodes' activities).…”

Section: Related Workmentioning

confidence: 99%

“…Loo et al [19] and Romer et al [21] have focused on extracting pattern regarding the phenomenon monitored by the sensor nodes, in which the mining techniques are applied to the sensed data received from the sensor nodes and stored in a central database. Sensor-association rules was proposed in [16,17,18] where patterns are extract regarding the sensor nodes rather than the area monitored by the WSN. An example of sensor association rules could be (s 1 , s 2 → s 3 , 85%, λ) which means that if sensor s 1 and s 2 detect events within λ time interval, then there is 85% of chance that s 3 detects events within same time interval.…”

Section: Introductionmentioning

confidence: 99%

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A novel algorithm for mining behavioral patterns from wireless sensor networks

Rashid

Gondal

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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Due to recent advances in wireless sensor networks (WSNs) and their ability to generate huge amount of data in the form of streams, knowledge discovery techniques have received a great deal of attention to extract useful knowledge regarding the underlying network. Traditionally sensor association rules measure occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. In this paper, we propose a new type of sensor behavioral pattern called associated sensor patterns that captures association-like co-occurrences and the strong temporal correlations implied by such co-occurrences in the sensor data. We also propose a novel tree structure called as associated sensor pattern tree (ASPT) and a mining algorithm, associated sensor pattern (ASP) which facilitates frequent pattern (FP) growth-based technique to generate all associated sensor patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our algorithm is very efficient in finding associated sensor patterns than the existing significant algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel algorithm for mining behavioral patterns from wireless sensor networks

Rashid

Gondal

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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“…The problem of mining sensors' association rules [14] is a variation of the association rules problem proposed in the domain of transactional databases [1], and can be formally defined as follows: Let S = {s1, s2, . .…”

Section: Problem Formulationmentioning

confidence: 99%

“…temp =60 and light = "On" are some examples of the objects of these rules). In [14], the authors have proposed sensor association rules in which the sensors are the main objects of the rules regardless of their values. To mine the frequent patterns efficiently; a compressed data structure is proposed; however, there were no assumptions about how to collect the sensor data that is needed to mine frequent patterns .…”

Section: Related Workmentioning

confidence: 99%

“…These techniques aim to extract patterns that describe the behavior of the network, in order to use these patterns to enhance the performance of the sensor network and thus improve the overall QoS. Sensor association rules [14] and sensor chronological patterns [13] are some examples of the data mining techniques that have been used with WSNs.…”

Section: Introductionmentioning

confidence: 99%

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A new in-network data reduction mechanism to gather data for mining wireless sensor networks

Boukerche

Samarah

2007

Proceedings of the 10th ACM Symposium on Modeling, Analysis, and Simulation of Wireless and Mobile Systems

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Recently, association rules for sensors have received a great deal of attention due to their importance in capturing the temporal relations between sensor nodes in wireless sensor networks (WSNs). Because of this capability, these rules can be used to improve the Quality of Service (QoS) of wireless sensor networks by participating in the resource management process. To mine sensor association rules, behavioral data that describes the sensors' activities over time must be extracted and accumulated at the central node (the Sink) for further analysis. Given the limited resources of sensor nodes, a well designed data gathering algorithm is required for gathering the behavioral data efficiently. In this paper, an in-network data reduction technique is proposed to reduce the amount of data that needs to be routed to the Sink by exploiting the redundancy between sensors' activities. The in-network reduction technique will be implemented on top of a data gathering tree that we refer to as the Minimum Nodes Data Gathering Tree (MNDGT), which consists of the nodes that will participate in formulating the sensor rules and those that are necessary for maintaining the minimum distance to the Sink. To report on the performance of the reduction mechanism, a comparison analysis with other two gathering schemas is introduced. Indeed, the results show that the in-network reduction technique is able to reduce the number of messages by a factor ranging from 10% to 70% compared to the other techniques.

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Wireless Sensor Network nodes correlation method in coal mine tunnel based on Bayesian decision

Chen

Jiang

et al. 2013

Measurement

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A New Representation Structure for Mining Association Rules from Wireless Sensor Networks

Cited by 8 publications

References 6 publications

A novel algorithm for mining behavioral patterns from wireless sensor networks

A novel algorithm for mining behavioral patterns from wireless sensor networks

A new in-network data reduction mechanism to gather data for mining wireless sensor networks

Wireless Sensor Network nodes correlation method in coal mine tunnel based on Bayesian decision

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