Mining wireless sensor network data streams using distributed algorithms and granularity algorithms framework

Pao Yue-kong Library Electronic Theses Database

Mining wireless sensor network data streams using distributed algorithms and granularity algorithms framework

 

Author: Ding, Xiaoping
Title: Mining wireless sensor network data streams using distributed algorithms and granularity algorithms framework
Degree: M.Sc.
Year: 2007
Subject: Hong Kong Polytechnic University -- Dissertations.
Sensor networks.
Wireless LANs.
Data mining.
Department: Dept. of Computing
Pages: ix, 112 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2128871
URI: http://theses.lib.polyu.edu.hk/handle/200/1843
Abstract: Wireless Sensor Networks (WSN) is networks of wireless sensor nodes, sensory devices with limited energy, and a limited capacity to compute or communicate wirelessly. Data Streams Mining (DSM) is the process of extracting knowledge structures represented in models and patterns in continuous streams of information. DSM programs usually require a mass of computing and communication. Typically, all data streams in all nodes on WSN are collected and clustered at one center node using traditional centralized algorithms such as K-Means Clustering. However, such centralized algorithms require large volumes of inter-node communication and this shortens the life of a WSN. In this dissertation, we propose Distributed K-Means Clustering (DKMC), which distributes the average task of clustering computing to every node in a WSN, and which collects the results in every node to K central nodes for averaging energy and addressing constraints from WSN. As the basis of the DKMC algorithm, we developed three algorithms, Count, Sum, and Average. We also present the Difference-Value monitoring, a novel and more stable type of data stream monitoring that addresses the fact that different sensory devices have different error ranges. This paper also seeks to make better use of limited WSN node resources by adjusting the granularity, or precision, of data at each stage of data mining process. Previous, [MAS2004] resource aware, approaches have improved the efficiency of resources use by controlling the granularity of just one phase of DSM, the output phase. In this work, we propose a Granularity Algorithm Framework (GAF). The GAF saves energy by adjusting data granularities at WSN nodes in three phases: the data stream input phase, the data streams mining phase, and the data stream output phase. The GAF adjusts granularities using three parameters: task (e.g. precision of mining result), environment (e.g. bandwidth and network topology) and resource ( e.g. CPU, memory). This is a significant improvement on the resource aware approach which adjusts granularity according to only one parameter, resources.

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