A Novel Information Acquisition Technique for Mobile-Assisted Wireless Sensor Networks

Fan Bai, Kumudu MUNASINGHE, Abbas Jamalipour

Research output: Contribution to journalArticle

13 Citations (Scopus)
3 Downloads (Pure)

Abstract

In this paper, we propose an adaptive data-harvesting approach for mobile-agent-assisted data collection in wireless sensor networks (WSNs) inspired by Behavioral Ecology. By using the marginal value theorem, we divide the entire sensor field into small patches and gather the correlated data from each patch. Each observation X gathered by a given sensor node to be considered to be a marginal information source with a relative standard deviation sigma(x|Y, I), where Y is a set of previously collected observations by the mobile agent, and I is the background knowledge learned from the sensor field. The mobile agent estimates the correlation based on the available knowledge gathered from the current patch and the previous patches and then chooses the next visiting sensor node. The next node should have the maximum information gain obtained until sigma(x|Y, I) is smaller than a predefined threshold (TH). Since, in a dynamically changing environment, the correlation varies among different patches, an efficient way to understand the correlation model is the key to efficient data harvesting. The proposed estimation technique of the marginal value theorem, which is called estimation technique based on the marginal value theorem (EMVT), is used to maintain the fidelity of the interested data with relatively fewer collected sensor observations
Original languageEnglish
Pages (from-to)1752-1761
Number of pages10
JournalIEEE Transactions on Vehicular Technology
Volume61
Issue number4
DOIs
Publication statusPublished - 2012
Externally publishedYes

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Mobile agents
Patch
Wireless Sensor Networks
Wireless sensor networks
Sensor nodes
Mobile Agent
Sensor
Sensors
Harvesting
Ecology
Vertex of a graph
Theorem
Correlated Data
Information Gain
Standard deviation
Fidelity
Divides
Choose
Acquisition
Entire

Cite this

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title = "A Novel Information Acquisition Technique for Mobile-Assisted Wireless Sensor Networks",
abstract = "In this paper, we propose an adaptive data-harvesting approach for mobile-agent-assisted data collection in wireless sensor networks (WSNs) inspired by Behavioral Ecology. By using the marginal value theorem, we divide the entire sensor field into small patches and gather the correlated data from each patch. Each observation X gathered by a given sensor node to be considered to be a marginal information source with a relative standard deviation sigma(x|Y, I), where Y is a set of previously collected observations by the mobile agent, and I is the background knowledge learned from the sensor field. The mobile agent estimates the correlation based on the available knowledge gathered from the current patch and the previous patches and then chooses the next visiting sensor node. The next node should have the maximum information gain obtained until sigma(x|Y, I) is smaller than a predefined threshold (TH). Since, in a dynamically changing environment, the correlation varies among different patches, an efficient way to understand the correlation model is the key to efficient data harvesting. The proposed estimation technique of the marginal value theorem, which is called estimation technique based on the marginal value theorem (EMVT), is used to maintain the fidelity of the interested data with relatively fewer collected sensor observations",
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A Novel Information Acquisition Technique for Mobile-Assisted Wireless Sensor Networks. / Bai, Fan; MUNASINGHE, Kumudu; Jamalipour, Abbas.

In: IEEE Transactions on Vehicular Technology, Vol. 61, No. 4, 2012, p. 1752-1761.

Research output: Contribution to journalArticle

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AU - Jamalipour, Abbas

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AB - In this paper, we propose an adaptive data-harvesting approach for mobile-agent-assisted data collection in wireless sensor networks (WSNs) inspired by Behavioral Ecology. By using the marginal value theorem, we divide the entire sensor field into small patches and gather the correlated data from each patch. Each observation X gathered by a given sensor node to be considered to be a marginal information source with a relative standard deviation sigma(x|Y, I), where Y is a set of previously collected observations by the mobile agent, and I is the background knowledge learned from the sensor field. The mobile agent estimates the correlation based on the available knowledge gathered from the current patch and the previous patches and then chooses the next visiting sensor node. The next node should have the maximum information gain obtained until sigma(x|Y, I) is smaller than a predefined threshold (TH). Since, in a dynamically changing environment, the correlation varies among different patches, an efficient way to understand the correlation model is the key to efficient data harvesting. The proposed estimation technique of the marginal value theorem, which is called estimation technique based on the marginal value theorem (EMVT), is used to maintain the fidelity of the interested data with relatively fewer collected sensor observations

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