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Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks
March 2011 (vol. 22 no. 3)
pp. 489-500
ASCII Text
x 
 
Hüseyin Ozgür Tan, Ibrahim Korpeoglu, Ivan Stojmenovic, "Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 3, pp. 489-500, March, 2011.
 
 
BibTex
x
 
@article{ 10.1109/TPDS.2010.68,
author = {Hüseyin Ozgür Tan and Ibrahim Korpeoglu and Ivan Stojmenovic},
title = {Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {22},
number = {3},
issn = {1045-9219},
year = {2011},
pages = {489-500},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2010.68},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks
IS - 3
SN - 1045-9219
SP489
EP500
EPD - 489-500
A1 - Hüseyin Ozgür Tan,
A1 - Ibrahim Korpeoglu,
A1 - Ivan Stojmenovic,
PY - 2011
KW - Wireless networks
KW - sensor networks
KW - data gathering
KW - minimum energy control
KW - distributed algorithms.
VL - 22
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 
Hüseyin Ozgür Tan, Bilkent University, Ankara
Ibrahim Korpeoglu, Bilkent University, Ankara
Ivan Stojmenovic, University of Ottawa, Ottawa
We propose localized, self organizing, robust, and energy-efficient data aggregation tree approaches for sensor networks, which we call Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs). They are based on topologies, such as LMST and RNG, that can approximate minimum spanning tree and can be efficiently computed using only position or distance information of one-hop neighbors. The actual routing tree is constructed over these topologies. We also consider different parent selection strategies while constructing a routing tree. We compare each topology and parent selection strategy and conclude that the best among them is the shortest path strategy over LMST structure. Our solution also involves route maintenance procedures that will be executed when a sensor node fails or a new node is added to the network. The proposed solution is also adapted to consider the remaining power levels of nodes in order to increase the network lifetime. Our simulation results show that by using our power-aware localized approach, we can almost have the same performance of a centralized solution in terms of network lifetime, and close to 90 percent of an upper bound derived here.

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Index Terms:
Wireless networks, sensor networks, data gathering, minimum energy control, distributed algorithms.
Citation:
Hüseyin Ozgür Tan, Ibrahim Korpeoglu, Ivan Stojmenovic, "Computing Localized Power-Efficient Data Aggregation Trees for Sensor Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 3, pp. 489-500, March 2011, doi:10.1109/TPDS.2010.68
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