Clustering for transmission range control and connectivity assurance for self configured ad hoc networks

Authors:
Kyriakos Manousakis;John S. Baras
Affiliations:
Electrical and Computer Engineering Department and the Institute for Systems Research, University of Maryland College Park, College Park, MD;Electrical and Computer Engineering Department and the Institute for Systems Research, University of Maryland College Park, College Park, MD
Venue:
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume II
Year:
2003

Citing 3
Cited 1

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WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks

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Adaptive clustering for mobile wireless networks

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Improved network survivability using multi-threshold adaptive range clustering M-TRAC algorithm for energy balancing in wireless sensor networks

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Abstract

Ad hoc networks have been the new networking technology trend because of their promising characteristics. These characteristics fit better the requirements of today's army and the needs of today's commercial world. Most of the applications that have been referred in the bibliography assume devices that are of finite power. The latter is true because of the existing technology (web enabled cell phones, PDAs, laptops, PPCs). The goal for ad hoc networks is to accommodate light weight, battery powered portable devices. Because of the finite power limitation, we have to design efficient ways to use the existing power. A first step towards efficient utilization of power is to eliminate its unnecessary usage wherever possible. For a networking device, significant part of power is consumed for communications and more specifically for transmissions. In this work we focus on eliminating the transmission power consumed into the network subject to the network's connectivity. The assurance of network connectivity is essential in this problem since the usage of limited transmission power from the nodes may result in a partitioned network. We provide solutions to the efficient usage of transmission power by clustering the nodes based on their proximity and ensuring the intra-cluster and inter-cluster connectivity of the network. The algorithms presented in this paper achieve their objective as the collected results from their simulation show.