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Mobility increases the connectivity of K-hop clustered wireless networks
Author(s) -
Qingsi Wang,
Xinbing Wang,
Xiaojun Lin
Publication year - 2009
Publication title -
proceedings of the 28th annual international conference on mobile computing and networking
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/1614320.1614334
Subject(s) - computer science , wireless ad hoc network , computer network , random walk , mobility model , energy consumption , cluster analysis , network packet , hop (telecommunications) , transmission delay , wireless network , transmission (telecommunications) , wireless , topology (electrical circuits) , mathematics , telecommunications , engineering , statistics , combinatorics , machine learning , electrical engineering
In this paper we investigate the connectivity for large-scale clustered wireless sensor and ad hoc networks. We study the effect of mobility on the critical transmission range for asymptotic connectivity in k-hop clustered networks, and compare to existing results on non-clustered stationary networks. By introducing k-hop clustering, any packet from a cluster member can reach a cluster head within k hops, and thus the transmission delay is bounded as Θ(1) for any finite k. We first characterize the critical transmission range for connectivity in mobile k-hop clustered networks where all nodes move under either the random walk mobility model with non-trivial velocity or the i.i.d. mobility model. By the term non-trivial velocity, we mean that the velocity of nodes v is Θ(1). We then compare with the critical transmission range for stationary k-hop clustered networks. We also study the transmission power versus delay trade-off and the average energy consumption per flow among different types of networks. We show that random walk mobility with non-trivial velocity increases connectivity in k-hop clustered networks, and thus significantly decreases the energy consumption and improves the power-delay trade-off. The decrease of energy consumption per flow is shown to be Θ(logn/nd}) in clustered networks. These results provide insights on network design and fundamental guidelines on building a large-scale wireless network.

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