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

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 mean reliability of each unicast-physical- transmission minus that of broadcast Errors in estimating unicast ETX via broadcast reliability: estimated unicast ETX minus actual unicast ETX and then divided by actual unicast ETX

7. Data-driven link estimation Unicast MAC feedback  {NT i }: # 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 {NT i }  represents SPEED, LOF, CARP L-ETX  uses derived information for individual unicast-physical-transmission  represents four-bit-estimation, EAR, NADV, MintRoute EWMA {NT i }ETX PDR calculation {NT i }{PDR j } EWMA PDR 1/PDR ETX

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

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

11. Relative accuracy in L-NT and L-ETX where P 0 is the failure probability of a unicast-physical-transmission, and W is the window size for calculating PDR j ;  COV[NT i ] > COV[PDR j ] if (which generally holds), thus DE k (L-NT) > DE k (PDR) L-ETX tends to be more accurate than L-NT in estimating link ETX.  DE k (L-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 Variant/stabilized L-NT: L-WNT L-NADV (variant of L-ETX): estimate PER instead of PDR

16. L-NT vs. L-ETX: forwarders used MethodForwarderPercentage(%)Cost ratio L-NT 5 6 7 8 10 0.1 4.14 7.17 21.26 67.33 2.3 1.3 1.5 1.3 1 L-ETX 6 7 8 10 5.91 0.2 5.1 88.79 1.3 1.5 1.3 1

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 Event reliability Number of transmissions per packet received Seemingly similar methods may differ significantly in routing behaviors (e.g., stability, optimality, and energy efficiency)

20. L-NT vs. L-ETX: routing stability Two consecutive routes (%) L-NTL-WNTL-ETXL-NADV Same36.554299.9499.97 Diff. routes but same hop count 17.0811.180.03 Increased hop count23.9624.190.030 Decreased hop count22.4122.6300

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 reliabilityNumber of transmissions per packet received