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An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

Xu Gong UGent, David Plets UGent, Emmeric Tanghe UGent, Toon De Pessemier UGent, Luc Martens UGent and Wout Joseph UGent (2018) APPLIED SOFT COMPUTING. 65. p.243-259
abstract
The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), there is an unavoidable need to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To fulfill this need, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small- and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC in function of varying target coverage percentage as well as number and placement direction of dominant obstacles. (C) 2018 Elsevier B.V. All rights reserved.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
SENSOR NETWORKS, OPTIMIZATION, COVERAGE, LIFETIME, Genetic algorithms (GAs), Evolutionary optimization, Large-scale, optimization, Cyber-physical system (CPS), Internet of things (IoT)
journal title
APPLIED SOFT COMPUTING
Appl. Soft. Comput.
volume
65
pages
17 pages
publisher
Elsevier Science Bv
place of publication
Amsterdam
Web of Science type
Article
Web of Science id
000427687300019
ISSN
1568-4946
1872-9681
DOI
10.1016/j.asoc.2018.01.016
language
English
UGent publication?
yes
classification
A1
id
8558729
handle
http://hdl.handle.net/1854/LU-8558729
date created
2018-04-10 13:19:32
date last changed
2018-05-17 11:25:08
@article{8558729,
  abstract     = {The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), there is an unavoidable need to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To fulfill this need, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small- and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC in function of varying target coverage percentage as well as number and placement direction of dominant obstacles. (C) 2018 Elsevier B.V. All rights reserved.},
  author       = {Gong, Xu and Plets, David and Tanghe, Emmeric and De Pessemier, Toon and Martens, Luc and Joseph, Wout},
  issn         = {1568-4946},
  journal      = {APPLIED SOFT COMPUTING},
  keyword      = {SENSOR NETWORKS,OPTIMIZATION,COVERAGE,LIFETIME,Genetic algorithms (GAs),Evolutionary optimization,Large-scale,optimization,Cyber-physical system (CPS),Internet of things (IoT)},
  language     = {eng},
  pages        = {243--259},
  publisher    = {Elsevier Science Bv},
  title        = {An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments},
  url          = {http://dx.doi.org/10.1016/j.asoc.2018.01.016},
  volume       = {65},
  year         = {2018},
}

Chicago
Gong, Xu, David Plets, Emmeric Tanghe, Toon De Pessemier, Luc Martens, and Wout Joseph. 2018. “An Efficient Genetic Algorithm for Large-scale Transmit Power Control of Dense and Robust Wireless Networks in Harsh Industrial Environments.” Applied Soft Computing 65: 243–259.
APA
Gong, X., Plets, D., Tanghe, E., De Pessemier, T., Martens, L., & Joseph, W. (2018). An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments. APPLIED SOFT COMPUTING, 65, 243–259.
Vancouver
1.
Gong X, Plets D, Tanghe E, De Pessemier T, Martens L, Joseph W. An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments. APPLIED SOFT COMPUTING. Amsterdam: Elsevier Science Bv; 2018;65:243–59.
MLA
Gong, Xu, David Plets, Emmeric Tanghe, et al. “An Efficient Genetic Algorithm for Large-scale Transmit Power Control of Dense and Robust Wireless Networks in Harsh Industrial Environments.” APPLIED SOFT COMPUTING 65 (2018): 243–259. Print.