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Issue No. 11 - November (2010 vol. 9)
ISSN: 1536-1233
pp: 1634-1648
Lifeng Sang , Ohio State University, Columbus
Anish Arora , Ohio State University, Columbus
Hongwei Zhang , Wayne State University, Detroit
Link estimation is a basic element of routing in low-power wireless networks, and data-driven link estimation using unicast MAC feedback has been shown to outperform broadcast-beacon-based link estimation. Nonetheless, little is known about how different data-driven link estimation methods affect routing behaviors. To address this issue, we classify existing data-driven link estimation methods into two broad categories: L-NT that uses aggregate information about unicast and L-ETX that uses information about the individual unicast-physical-transmissions. Through mathematical analysis and experimental measurement in a testbed of 98 XSM motes (an enhanced version of MICA2 motes), we examine the accuracy and stability of L-NT and L-ETX in estimating the ETX routing metric. We also experimentally study the routing performance of L-NT and L-ETX. We discover that these two representative, seemingly similar methods of data-driven link estimation differ significantly in routing behaviors: L-ETX is much more accurate and stable than L-NT in estimating the ETX metric, and accordingly, L-ETX achieves a higher data delivery reliability and energy efficiency than L-NT (for instance, by 25.18 percent and a factor of 3.75, respectively, in our testbed). These findings provide new insight into the subtle design issues in data-driven link estimation that significantly impact the reliability, stability, and efficiency of wireless routing, thus shedding light on how to design link estimation methods for mission-critical wireless networks which pose stringent requirements on reliability and predictability.
Low-power wireless networks, sensor networks, link estimation and routing, data driven, beacon based, distance vector routing, geographic routing.
Lifeng Sang, Anish Arora, Hongwei Zhang, "Comparison of Data-Driven Link Estimation Methods in Low-Power Wireless Networks", IEEE Transactions on Mobile Computing, vol. 9, no. , pp. 1634-1648, November 2010, doi:10.1109/TMC.2010.126
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