TY - GEN
T1 - AdapCode
T2 - INFOCOM 2008: 27th IEEE Communications Society Conference on Computer Communications
AU - Hou, I. Hong
AU - Tsai, Yu En
AU - Abdelzaher, Tarek F.
AU - Gupta, Indranil
PY - 2008
Y1 - 2008
N2 - Code updates, such as those for debugging purposes, are frequent and expensive in the early development stages of wireless sensor network applications. We propose AdapCode, a reliable data dissemination protocol that uses adaptive network coding to reduce broadcast traffic in the process of code updates. Packets on every node are coded by linear combination and decoded by Gaussian elimination. The core idea in AdapCode is to adaptively change the coding scheme according to the link quality. Our evaluation shows that AdapCode uses up to 40% less packets than Deluge in large networks. In addition, AdapCode performs much better in terms of load balancing, which prolongs the system lifetime, and has a slightly shorter propagation delay. Finally, we show that network coding is doable on sensor networks in that (i) it imposes only a 3 byte header overhead, (ii) it is easy to find linearly independent packets, and (3) Gaussian elimination needs only 1KB of memory.
AB - Code updates, such as those for debugging purposes, are frequent and expensive in the early development stages of wireless sensor network applications. We propose AdapCode, a reliable data dissemination protocol that uses adaptive network coding to reduce broadcast traffic in the process of code updates. Packets on every node are coded by linear combination and decoded by Gaussian elimination. The core idea in AdapCode is to adaptively change the coding scheme according to the link quality. Our evaluation shows that AdapCode uses up to 40% less packets than Deluge in large networks. In addition, AdapCode performs much better in terms of load balancing, which prolongs the system lifetime, and has a slightly shorter propagation delay. Finally, we show that network coding is doable on sensor networks in that (i) it imposes only a 3 byte header overhead, (ii) it is easy to find linearly independent packets, and (3) Gaussian elimination needs only 1KB of memory.
UR - http://www.scopus.com/inward/record.url?scp=51349100852&partnerID=8YFLogxK
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U2 - 10.1109/INFOCOM.2007.211
DO - 10.1109/INFOCOM.2007.211
M3 - Conference contribution
AN - SCOPUS:51349100852
SN - 9781424420261
T3 - Proceedings - IEEE INFOCOM
SP - 2189
EP - 2197
BT - INFOCOM 2008
Y2 - 13 April 2008 through 18 April 2008
ER -