Abstract
Previous studies for applying bio-inspired algorithms to resolve the traditional issues in wireless networks were motivated by some excellent characteristics of the bio-inspired algorithms including convergence, scalability, adaptability, and stability. In this paper, we apply the bio-inspired flocking algorithm to fair resource allocation in vehicle-mounted mobile relay (VMR) deployed networks. Although a VMR-deployed network has an advantage of the provision of high-quality communication services to mobile devices inside the vehicle, it is more susceptible to inter-VMR and base station (BS)-VMR interferences because both the mobility and geographical position of the VMRs and pedestrian mobile stations (MSs) cannot be artificially controlled. Therefore, the proposed flocking-inspired algorithm is designed to achieve the adaptive alleviation of the inter-VMR and BS-VMR interferences, and the attainment of a fair and distributed resource allocation among competing VMRs. We verify its self-adaptiveness under the dynamically changing network topology. The results show that the proposed flocking-inspired resource allocation method adaptively alleviates the inter-VMR and BS-VMR interferences.
Notes
- 1.
Hereafter, we assume that the total amount of available resources is 1024.
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Acknowledgements
This research was partly supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01060207) and by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2015-H8501-15-1007) supervised by the IITP (Institute for Information and communications Technology Promotion).
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Choi, HH., Lee, JR. (2017). Applying a Flocking-Inspired Algorithm to Fair Resource Allocation of Vehicle-Mounted Mobile Relays. In: Lee, JH., Pack, S. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-319-60717-7_24
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