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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
The shared-medium multi-hop nature of wireless ad hoc networks poses fundamental challenges to the design of an effective resource allocation algorithm to maximize the aggregated utility of flows, while maintaining basic fairness among multiple flows. When previously proposed scheduling algorithms have been shown to perform well in providing fair shares of bandwidth among single-hop wireless flows, they did not consider multi-hop flows with an endto-end perspective. Moreover, the resource<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-44884-5_5">doi:10.1007/3-540-44884-5_5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fp7lefkpbnfrhfpyjhe5376jou">fatcat:fp7lefkpbnfrhfpyjhe5376jou</a> </span>
more »... tion strategies employed in the wireline network can not be applied directly in the context of ad hoc networks due to the unique characteristic of location dependent contention and spatial reuse of the shared wireless channel. In this paper, we propose a price-based resource allocation model to achieve maximized aggregated utility (i.e., social welfare) of flows. Our original contributions are: First, we propose to use maximal cliqueassociated shadow prices for wireless channel access coordination, rather than link-associated price for wireline link access arbitration. Second, we present a new pricing policy for end-to-end multi-hop flow. Using this model, different fairness goals can be realized in ad hoc networks for end-to-end flows. With a twotier distributed and iterative algorithm, scarce channel capacity is allocated fairly among multi-hop flows from an end-to-end perspective, using shadow prices as the mechanism to arbitrate channel access. Through extensive analysis and simulation results, we show that our proposed algorithm is able to fairly distribute resources among multi-hop flows, while simultaneously maximizing the aggregated utility of flows globally.
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