Hierarchical distributed data classification in wireless sensor networks

Cheng, Xu; Ji, Xu; Pei, Jian; Liu, Jiangchuan

doi:10.1016/j.comcom.2010.01.027

Cited by 44 publications

(23 citation statements)

References 21 publications

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“…In most of these systems energy is a scarce resource. Consequently, research on distributed classification in such systems mainly deals with energy conserving mechanisms [7] or the fusion of the data of different sources in an efficient way [5]. The recent advances in transceiver and embedded hardware make it possible to produce even smaller devices than are currently typical for nodes of existing sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Learning classifier systems to evolve classification rules for systems of memory constrained components

Scheidler

Middendorf²

2011

Evol. Intel.

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In this paper we study how to solve classification problems in computing systems that consist of distributed, memory constrained components. Interacting Pittsburgh-style Learning Classifier Systems are used to generate sets of classification rules that can be deployed on the components. We show that this approach distributes the knowledge and enables the components to solve complex classification problems in cooperation. We study the structure and properties of the evolved rule sets and analyse the way the components share their knowledge. Moreover, we investigate the influence of different communication topologies and the introduction of communication costs on the emerging patterns of cooperation and on the classification performance of the whole system.

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Section: Introductionmentioning

confidence: 99%

Learning classifier systems to evolve classification rules for systems of memory constrained components

Scheidler

Middendorf²

2011

Evol. Intel.

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“…For example, [12] studies hierarchical data classification in sensor networks. In this paper, local classifiers built by individual sensors are iteratively enhanced along the routing path, by strategically combining generated pseudo data and new local data.…”

Section: Related Workmentioning

confidence: 99%

“…In habitat monitoring, sensor networks may distinguish different behaviors of monitored species [9,10,11]. In environmental monitoring, it may be desired that the environmental (e.g., weather) conditions be classified based on their impact on humans, animals, or crops [12]. In health care or assisted living, an intelligent sensor network may automatically evaluate the health status of residents, and react when they are in danger [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical aggregate classification with limited supervision for data reduction in wireless sensor networks

Gao

Yang

et al. 2011

Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems

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The main challenge of designing classification algorithms for sensor networks is the lack of labeled sensory data, due to the high cost of manual labeling in the harsh locales where a sensor network is normally deployed. Moreover, delivering all the sensory data to the sink would cost enormous energy. Therefore, although some classification techniques can deal with limited label information, they cannot be directly applied to sensor networks since they are designed for centralized databases. To address these challenges, we propose a hierarchical aggregate classification (HAC) protocol which can reduce the amount of data sent by each node while achieving accurate classification in the face of insufficient label information. In this protocol, each sensor node locally makes cluster analysis and forwards only its decision to the parent node. The decisions are aggregated along the tree, and eventually the global agreement is achieved at the sink node. In addition, to control the tradeoff between the communication energy and the classification accuracy, we design an extended version of HAC, called the constrained hierarchical aggregate classification (cHAC) protocol. cHAC can achieve more accurate classification results compared with HAC, at the cost of more energy consumption. The advantages of our schemes are demonstrated through the experiments on not only synthetic data but also a real testbed.

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“…Classification is a process to classify sampled data into different classes or clusters. Some classification techniques, such as SVM and artificial neural network (ANN), have been utilized in the outlier detection in WSN [21][22][23]. As one of the simplest classification algorithm, the -Means technique also has been proved to be effective in the outlier detection in other areas [24][25][26], which groups data with length points into clusters, where represents the number of clusters and is used to distinguish the compression ratio .…”

Section: Classify By the Algorithm Of -Meansmentioning

confidence: 99%

An Anomaly Detection Based on Data Fusion Algorithm in Wireless Sensor Networks

Guo

Wang

Chen

2015

International Journal of Distributed Sensor Networks

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In recent years, with the development of wireless sensor networks (WSN), it has been applied in more and more areas. However, energy consumption and outlier detection have been always the hot topics in WSN. In order to solve the above problems, this paper proposes a timely anomaly detection algorithm which is based on the data fusion algorithm. This algorithm firstly employs the piecewise aggregate approximation (PAA) to compress the original data so that the energy consumption can be reduced. It then combines an improved unsupervised detection algorithm of -Means and artificial immune system (AIS) to classify the compressed data to normal and abnormal data. Finally, relevant experiments on virtual and actual sensor databases show that our algorithm can achieve a high outlier detection rate while the false alarm rate is low. In addition, our detection algorithm can effectively prolong the life because it is based on data fusion algorithm.

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Hierarchical distributed data classification in wireless sensor networks

Cited by 44 publications

References 21 publications

Learning classifier systems to evolve classification rules for systems of memory constrained components

Learning classifier systems to evolve classification rules for systems of memory constrained components

Hierarchical aggregate classification with limited supervision for data reduction in wireless sensor networks

An Anomaly Detection Based on Data Fusion Algorithm in Wireless Sensor Networks

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