Base station allocation for users with overlapping coverage in wirelessly networked disaster areas

Yu Wang, Michael Conrad Meyer, Junbo Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

After major disasters, temporary deployable cellular networks are often used to construct an emergency communication network. These networks do not have the same level of performance as a typical fog or cloud network, and in order to serve the users in a way that is sustainable, utilization of optimization algorithms must be considered. We proposed using a genetic algorithm (GA) to optimally allocate these users in the overlapping areas to a base station so that the system could provide an improved user experience. We tested our proposed algorithm against a greedy algorithm, a random algorithm, and allocating users to the closest MBS. The greedy algorithm outperformed the two other baseline algorithms, but the proposed algorithm was able to reduce the average and worst-case delay of the system by 80% compared to the greedy algorithm. The genetic algorithm had completely settled by 150 generations. This algorithm provided such an advantage that it warrants deeper study.

Original languageEnglish
Title of host publicationProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages954-960
Number of pages7
ISBN (Electronic)9781728130248
DOIs
Publication statusPublished - 2019 Aug
Event17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 - Fukuoka, Japan
Duration: 2019 Aug 52019 Aug 8

Publication series

NameProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

Conference

Conference17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
CountryJapan
CityFukuoka
Period19/8/519/8/8

Keywords

  • Big data processing
  • Fog computing
  • Genetic algorithm
  • Minimal delay
  • Networks
  • Optimization
  • User allocation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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  • Cite this

    Wang, Y., Meyer, M. C., & Wang, J. (2019). Base station allocation for users with overlapping coverage in wirelessly networked disaster areas. In Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 (pp. 954-960). [8890266] (Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00175