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An evolution strategy based approach for cover scheduling problem in wireless sensor networks

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Abstract

Cover scheduling problem in wireless sensor networks (WSN-CSP) aims to find a schedule of covers which minimizes the longest continuous duration of time for which no sensor in the network is able to monitor a target. This problem arises in those sensing environments which permit the coverage breach, i.e., at any instant of time, all targets need not be monitored. The coverage breach may occur owing to either technical restrictions or intentionally. It is an \(\mathcal {NP}\)-hard problem. This paper presents a \((1 + 1)\)-evolution strategy based approach to address WSN-CSP problem. We have compared our approach with the state-of-art approaches available in literature. Computational results show that our approach is significantly superior in comparison to the existing approaches for WSN-CSP.

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Acknowledgements

The first two authors acknowledge their respective Senior Research Fellowships received from the Council of Scientific and Industrial Research, Government of India. Authors are also thankful to four anonymous reviewers for their valuable comments and suggestions which helped in improving the quality of this manuscript.

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Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500 046, India

    Gaurav Srivastava, Pandiri Venkatesh & Alok Singh

Authors
  1. Gaurav Srivastava
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  2. Pandiri Venkatesh
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  3. Alok Singh
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Correspondence to Alok Singh.

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Appendix

Appendix

This appendix provides the objective function values achieved by various approaches (CSGA, CSABC, CSIWO and ES-CSP) on each instance. In addition, we have provided the value of the lower bound (LB) on each instance. Each instance has a name of the form covers_instsAAAnBBBrCCCwDDDiEE.dat, where

AAA:

Number of sensors

BBB:

Number of targets

CCC:

Sensing range which is 150 in all the instances

DDD:

bandwidth (\(\omega\))

EE:

Instance number (between 0 and 29)

Results are presented in Tables 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14. Each of these tables corresponds to one row of Tables 1 and 2.

Table 3 Results on instances with number of sensors (s) = 50 and bandwidth (\(\omega\)) = 5
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Table 4 Results on instances with number of sensors (s) = 50 and bandwidth (\(\omega\)) = 10
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Table 5 Results on instances with number of sensors (s) = 50 and bandwidth (\(\omega\)) = s
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Table 6 Results on instances with number of sensors (s) = 100 and bandwidth (\(\omega\)) = 5
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Table 7 Results on instances with number of sensors (s) = 100 and bandwidth (\(\omega\)) = 10
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Table 8 Results on instances with number of sensors (s) = 100 and bandwidth (\(\omega\)) = s
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Table 9 Results on instances with number of sensors (s) = 150 and bandwidth (\(\omega\)) = 5
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Table 10 Results on instances with number of sensors (s) = 150 and bandwidth (\(\omega\)) = 10
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Table 11 Results on instances with number of sensors (s) = 150 and bandwidth (\(\omega\)) = s
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Table 12 Results on instances with number of sensors (s) = 200 and bandwidth (\(\omega\)) = 5
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Table 13 Results on instances with number of sensors (s) = 200 and bandwidth (\(\omega\)) = 10
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Table 14 Results on instances with number of sensors (s) = 200 and bandwidth (\(\omega\)) = s
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Srivastava, G., Venkatesh, P. & Singh, A. An evolution strategy based approach for cover scheduling problem in wireless sensor networks. Int. J. Mach. Learn. & Cyber. 11, 1981–2006 (2020). https://doi.org/10.1007/s13042-020-01088-5

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