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Data Gathering with Multi-Attribute Fusion in Wireless Sensor Networks

Data Gathering with Multi-Attribute Fusion in Wireless Sensor Networks

Kai Lin, Lei Wang, Lei Shu, Al-Sakib Khan Pathan
Copyright: © 2012 |Pages: 23
ISBN13: 9781613501108|ISBN10: 1613501102|EISBN13: 9781613501115
DOI: 10.4018/978-1-61350-110-8.ch008
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MLA

Lin, Kai, et al. "Data Gathering with Multi-Attribute Fusion in Wireless Sensor Networks." Advancements in Distributed Computing and Internet Technologies: Trends and Issues, edited by Al-Sakib Khan Pathan, et al., IGI Global, 2012, pp. 159-181. https://doi.org/10.4018/978-1-61350-110-8.ch008

APA

Lin, K., Wang, L., Shu, L., & Khan Pathan, A. (2012). Data Gathering with Multi-Attribute Fusion in Wireless Sensor Networks. In A. Pathan, M. Pathan, & H. Lee (Eds.), Advancements in Distributed Computing and Internet Technologies: Trends and Issues (pp. 159-181). IGI Global. https://doi.org/10.4018/978-1-61350-110-8.ch008

Chicago

Lin, Kai, et al. "Data Gathering with Multi-Attribute Fusion in Wireless Sensor Networks." In Advancements in Distributed Computing and Internet Technologies: Trends and Issues, edited by Al-Sakib Khan Pathan, Mukaddim Pathan, and Hae Young Lee, 159-181. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-110-8.ch008

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Abstract

This chapter addresses the problem of data gathering with multi-attribute fusion over a bandwidth and energy constrained wireless sensor network (WSN). As there are strong correlations between data gathered from sensor nodes in close physical proximity, effective in-network fusion schemes involve minimizing such redundancy and hence reducing the load in wireless sensor networks. Considering a complicated environment, each sensor node must be equipped with more than one type of sensor module to monitor multi-targets; hence, the complexity for the fusion process is increased due to the existence of various physical attributes. In this chapter, by investigating the process and performance of multi-attribute fusion in data gathering of WSNs, we design a self-adaptive threshold to balance the different change rates of each attributive data. Furthermore, we present a method to measure the energy-conservation efficiency of multi-attribute fusion. Then, a novel energy equilibrium routing method is proposed to balance and save energy in WSNs, which is named multi-attribute fusion tree (MAFT). The establishment of MAFT is determined by the remaining energy of sensor nodes and the energy-conservation efficiency of data fusion. Finally, the energy saving performance of the scheme is demonstrated through comprehensive simulations. The chapter concludes by identifying some open research issues on this topic.

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