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Maximizing Submodular Set Function With Connectivity Constraint: Theory and Application to Networks

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PROJECT TITLE :

Maximizing Submodular Set Function With Connectivity Constraint: Theory and Application to Networks

ABSTRACT:

During this paper, we have a tendency to investigate the wireless network deployment problem, that seeks the most effective deployment of a given limited variety of wireless routers. We notice that several goals for network deployment, such as maximizing the amount of lined users, the scale of the coverage area, or the full throughput of the network, will be modeled with a submodular set operate. Specifically, given a group of routers, the goal is to seek out a set of locations S, each of that is provided with a router, such that S maximizes a predefined submodular set perform. However, this deployment drawback is additional tough than the traditional most submodular set function downside, e.g., the most coverage downside, as a result of it needs all the deployed routers to create a connected network. Additionally, deploying a router in different locations may consume totally different prices. To address these challenges, this paper introduces two approximation algorithms, one for homogeneous deployment value situations and the other for heterogeneous deployment value eventualities. Our simulations, using artificial information and real traces of census in Taipei, Taiwan, show that the proposed algorithms achieve better performances than other heuristics.


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Maximizing Submodular Set Function With Connectivity Constraint: Theory and Application to Networks - 4.9 out of 5 based on 78 votes

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