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

Comparison of Data-driven Link Estimation Methods in Low-power Wireless Networks. Hongwei Zhang Lifeng Sang Anish Arora. From sensor networks to cyber-physical systems (CPS). Sensing, networking, and computing tightly coupled with the physical world Automotive

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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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