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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
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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.
Keywords
SENSOR NETWORKS, OPTIMIZATION, COVERAGE, LIFETIME, Genetic algorithms (GAs), Evolutionary optimization, Large-scale, optimization, Cyber-physical system (CPS), Internet of things (IoT)

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Citation

Please use this url to cite or link to this publication:

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.
@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},
}

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