Sensor Placement Revisited in a Realistic Environment

Yang, Yong; Hou, I-Hong; Hou, Jennifer C.; Shankar, Millikarjun; Rao, Nageswara S.

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https://hdl.handle.net/2142/11325 Copy

Description

Title

Sensor Placement Revisited in a Realistic Environment

Author(s)

• Yang, Yong
• Hou, I-Hong
• Hou, Jennifer C.
• Shankar, Millikarjun
• Rao, Nageswara S.

Issue Date

2007-04

Keyword(s)

• ad hoc networks
• networking
• sensors

Abstract

Ad-hoc networks of devices and sensors with (limited) sensing and wireless communication capabilities are becoming increasingly available for commercial and military applications. Under a national SensorNet initiative, we have built prototype deployment of a detection, identification, and tracking sensor-cyber network in a variety of locations including Washington D.C. and Port of Memphis. One of the most important and up-front issues is where to place sensors in order to fulfill certain performance criteria, subject to the number of sensors to be deployed, the distribution of threats, the terrain and meteorological conditions, and the population distribution. In this paper, we revisit the sensor placement problem in a more realistic setting. Specifically, we consider three sensor placement problems and prove their equivalence. Then we focus on formulating/solving the third problem as an optimization problem: given the maximum detection time T and the coverage utility requirement C, how to place sensors so as to minimize the number of sensors. In particular, we allow the sensing area of a sensor to be anisotropic and of arbitrary shape, depending on the material released, its dosage fields and release patterns, the wind speed and direction, and the dispersion model. We define the utility function to quantify the utility of sensor coverage by considering its ability to manage potential threats. The coverage model can thus quantify the expected risks of insufficient coverage (or utilities of coverage) in different parts of the monitoring area, considering relevant environment and population data. We propose theoretically grounded solution algorithms for both the 1-coverage and k-coverage cases. The empirical study indicates that our proposed algorithms significantly outperform random and grid placement in terms of the detection time.

Type of Resource

text

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http://hdl.handle.net/2142/11325

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