Multi-sector multi-range control for self-organizing wireless networks
- Authors
- Ryoo, Jihoon; Kim, Hwangnam
- Issue Date
- 11월-2011
- Publisher
- ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
- Keywords
- Spatial reusability; Network capacity; Transmission range control; Self-organization
- Citation
- JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, v.34, no.6, pp.1848 - 1860
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- Volume
- 34
- Number
- 6
- Start Page
- 1848
- End Page
- 1860
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/111299
- DOI
- 10.1016/j.jnca.2010.12.002
- ISSN
- 1084-8045
- Abstract
- In this paper, we propose a distributed multi-sector multi-range (MSMR) control algorithm for supporting self-organizing wireless networks. The algorithm enables us to reduce the unnecessary coverage with fine-tuned range control and also to increase the network-wide capacity with enhanced spatial reusability. The proposed algorithm discovers neighboring nodes within the maximum transmission range at every node, divides its transmission area into multiple non-overlapping angular sectors of a given degree, chooses the home sector for each neighboring node according to its relative position, and constructs a spanning subgraph per sector by determining appropriate transmission range to maintain connectivity. Since the range control influences on network connectivity directly, we prove in the first place that the proposed algorithm preserves both network-wide and local connectivity as far as both connectivity exist in the network that uses the maximum transmission range. In order to investigate the performance of the proposed algorithm, we implemented it in the ns-2 simulator, and performed an extensive set of simulation study in comparison with other transmission range control schemes. The simulation results indicate that the proposed scheme is superior to other schemes with respect to the network-wide throughput and its normalized value per energy in various simulation configurations. In specific, the algorithm achieves minimally one order and maximally two orders of magnitude improvement in those performance evaluations. The improvement becomes more salient as the number of nodes increases and is immune to traffic type, network size, node distribution, or node density. (C) 2010 Elsevier Ltd. All rights reserved.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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