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Comparison of Data-driven Link Estimation Methods in Low-power Wireless Networks

This study evaluates the performance of two data-driven link estimation methods, L-NT and L-ETX, in low-power wireless networks. It explores factors influencing their accuracy and implications for routing behaviors. Experimental verification using Mica2 motes in a grid setup illustrates how these methods fare in different scenarios, impacting routing stability and energy efficiency. The research identifies the significance of estimating link parameters accurately for improving network reliability and latency in sensing and control applications.

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Comparison of Data-driven Link Estimation Methods in Low-power Wireless Networks

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  1. Comparison of Data-driven Link Estimation Methods in Low-power Wireless Networks Hongwei Zhang Lifeng Sang Anish Arora

  2. From sensor networks to cyber-physical systems (CPS) • Sensing, networking, and computing tightly coupled with the physical world • Automotive • Alternative energy grid • Industrial monitoring and control • Wireless networks as carriers of mission-critical sensing and control information • Stringent requirements on predictable QoS such as reliability and latency

  3. 5.5 meters (2 secs) transitional region (unstable & unreliable) Dynamic wireless links Link estimation becomes a basic element of routing in wireless networks.

  4. Why not beacon-based link estimation?

  5. Sampling error due to traffic-induced interference Unicast ETX in different traffic/interference scenarios

  6. Sampling error due to temporal link correlation Errors in estimating unicast ETX via broadcast reliability:estimated unicast ETX minus actual unicast ETX and then divided by actual unicast ETX mean reliability of each unicast-physical-transmission minus that of broadcast

  7. Data-driven link estimation • Unicast MAC feedback • {NTi}: # of physical transmissions for the i-th unicast • As a simple, low cost mechanism to address the sampling errors of beacon-based link estimation

  8. Two representative methods for estimating ETX • L-NT • uses aggregate unicast feedback {NTi} • represents SPEED, LOF, CARP • L-ETX • uses derived information for individual unicast-physical-transmission • represents four-bit-estimation, EAR, NADV, MintRoute {NTi} ETX EWMA {NTi} {PDRj} PDR PDR calculation ETX 1/PDR EWMA

  9. Won’t L-NT and L-ETX behave the same?

  10. COV[xi] DEk is approximately proportional to COV[xi]. Accuracy of EWMA estimators • Given {xi: i = 1, 2, …} where xi is a random variable with mean  and variance 2, the EWMA estimator for  is • Degree of estimation error (DEk) for using estimator

  11.  DEk(L-ETX) Relative accuracy in L-NT and L-ETX where P0 is the failure probability of a unicast-physical-transmission, and W is the window size for calculating PDRj;  COV[NTi] > COV[PDRj] if (which generally holds), thus DEk(L-NT) > DEk(PDR) L-ETX tends to be more accurate than L-NT in estimating link ETX.

  12. Can we experimentally verify the analytical results?

  13. Testbed based link-level experimentation • We use Mica2 motes that are deployed in a 147 grid • Focus on links of the middle row • Interferers randomly distributed in the rest 6 rows, with 7 motes on each row on average; interfering traffic is controlled by the probability d of generating a packet at an arbitrary time

  14. L-NT vs. L-ETX: when d = 0.1 Estimated ETX values in L-NT and L-ETX for a link 9.15 meters (i.e., 30 feet)long COV[NTi] vs. COV[PDRj]

  15. Variants of L-NT and L-ETX L-NADV (variant of L-ETX): estimate PER instead of PDR Variant/stabilized L-NT: L-WNT

  16. L-NT vs. L-ETX: forwarders used

  17. Implications for routing behaviors?

  18. Testbed based routing experiments Convergecast routing in a 77 grid • A node at one corner as the sink • Other 48 nodes as sources generating packets based on the event traffic trace from “A Line in the Sand” sink

  19. L-NT vs. L-ETX: routing performance Number of transmissions per packet received Event reliability Seemingly similar methods may differ significantly in routing behaviors (e.g., stability, optimality, and energy efficiency)

  20. L-NT vs. L-ETX: routing stability

  21. Other experimental results • Related data-driven protocols • L-ETX-geo, L-ETX • Periodic traffic, other event traffic load • Sparser network • Random network • Network throughput

  22. Concluding remarks • Two seemingly methods L-ETX and L-NT differ significantly in routing performance • Variability of parameters being estimated significantly affects the reliability, stability, latency, and energy efficiency of data-driven link estimation and routing • Future work • Other metrics (e.g., RT oriented) • Opportunistic routing and biased-link-sampling

  23. Backup slides

  24. Traffic pattern affects temporal link correlation • Autocorrelation tends to decrease, especially for smaller lags, as interference load increases, partly due to increased randomization as a result of random traffic Autocorrelation coefficient for a link of length 9.15 meters (i.e., 30 feet) Autocorrelation coefficient for lag 4

  25. Beacon-based vs. data-driven routing Event reliability Number of transmissions per packet received

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