Lecture 4: Link Characteristics

1. Lecture 4: Link Characteristics Anish Arora CIS788.11J Introduction to Wireless Sensor Networks Material uses slides from Alberto Cerpa, ZhaoGovindan, WooCuller, ZhangArora

2. References • Temporal Properties of Low Power Wireless Links: Modeling and Implications on Multi-Hop Routing, Alberto Cerpa, Jennifer L. Wong, Miodrag Potkonjak and Deborah Estrin Mobihoc 2005 • Understanding Packet Delivery Performance In Dense Wireless Sensor Networks Jerry Zhao and Ramesh Govindan, Sensys03 • Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks, Alec Woo, Terence Tong, and David Culler, SenSys 2003 Los Angeles, California • Statistical Model of Lossy Links in Wireless Sensor Networks, Alberto Cerpa, Jennifer L. Wong, Louane Kuang, Miodrag Potkonjak and Deborah Estrin, IPSN'05 • Impact of Radio Irregularity on Wireless Sensor Networks Gang Zhou, Tian He, Sudha Krishnamurthy, and John A. Stankovic, ACM MOBISYS 2004 • LOF, Hongwei Zhang and Anish Arora

3. Outline • Link Characterization • results • summary • Why? • reality guides algorithm development & protocol parameter tuning • data for better propagation models used in simulations

4. Noise Variability Across Nodes

5. Radio Channel Features* • Asymmetrical links:  connectivity from node a to node b might differ significantly from b to a • Non-isotropical connectivity:  connectivity need not be same in all directions (at same distance from source) • Non-monotonic distance decay:  nodes geographically far away from source may get better connectivity than nodes that are geographically closer *Ganesan et. al. 02; Woo et. al. 03; Zhao et. al. 03; Cerpa et. al. 03; Zhou et. al. 04

6. Parameters • Transmission gain control:  most WSN low power radios have some form TX gain control • Antenna height: relative distance of antenna wrt reference ground • Radio frequency and modulation type • Packet size:  # bits per packet can affect likelihood of receiving the packet with no errors • Data rate: # packets transmitted per second • Environment type:  e.g., indoors or outdoors, with or w/o LOS, different levels of physical interference (furniture, walls, trees, etc.), and different materials (sand, grass, concrete, etc.)

7. Non-isotropic connectivity* *Zhou et. al. 04

8. Explanation of Transitional Region received power (dBm) • Observations • σ ↑ → TR ↑ • η ↑ → TR ↓ distance (m) *Krishnamachari et. al.

9. Reception vs RSS

10. Links from A Given Source (1)

11. Links from A Given Source (2) • Good link receives a packet from source (whp)  all other links will as well • Good link does not receive packet (whp)  all other links will not as well • Medium/bad links receive a packet from source (whp)  good links will receive packet whp • Medium/bad links do not receive a packet from source  good links may still receive packet whp • little incentive to exploit multiple paths concurrently * Cerpa et al Mobihoc05

12. Spatial Characteristics • Great variability over distance (50 to 80% of radio range) • Reception rate not normally distributed around the mean and std. dev. • Real communication channel notisotropic • Low degree of correlation between distance and reception probability; lack of monotonicity and isotropy • Region of highly variable reception rates can be 50% or more of the radio range, and not confined to limit of radio range • From a given source, reception on good links is correlated to reception on other links

13. Main cause of asymmetric links? • When swapping asymmetric links node pairs, the asymmetric links are inverted (91.1% ± 8.32) • Claim: Link asymmetries are primarily caused by differences in hardware calibration

14. Bidirectional Link Correlation Conclusion: • Send ack immediately after receiving • When sending acks immediately, sum of link RNP in both directions is highly correlated with actual link cost, i.e., almost always a good indicator of link quality * Cerpa et al Mobihoc05 Large Distance/RNP ratio Time before sending ack after receiving a packet

15. symmetric asymmetric unidirectional Empirical study of link asymmetry symmetric asymmetric unidirectional • Many links are asymmetric • Traditional techniques tend to ignore asymmetric links • Lower transmission power --> more asymmetric links • Symmetric links: short asymmetric links: long • Exploiting asymmetric links can lead to more efficient routing

16. Reliability of synchronous ACKs • Significant improvement of using sync ACK over async messages, especially in the presence of interference • Improvement occurs on both short and long links • => Norm of estimating link quality in both directions via async beacons underestimates the link reliability of asymmetric links

17. Asymmetric Links • Found 5 to 30% of asymmetric links • Claim: No simple correlation between asymmetric links and distance or TX output power • They tend to appear at multiple distances from the radio range, not at the limit

18. Temporal Variation *Cerpa et. al. 03

19. Temporal Consistency of Links L1 norm indicates that good links and links with high distance/RNP ratio are temporally stable; so are bad links * Cerpa et al Mobihoc05

20. Temporal Characteristics Summary • Time variability is correlated with mean reception rate • Time variability is not correlated with distance from the transmitter (especially for “useful” links)

21. Summary • Great variability over distance (50 to 80% of radio range) • Reception rate is not normally distributed around the mean and std. dev. • Real communication channel is notisotropic • Found 5 to 30% of asymmetric links • Not correlated withdistance or transmission power • Primary cause: differences in hardware calibration (rx sensitivity, energy levels) • Time variability is correlated with mean reception rate and not correlated with distance from the transmitter • Possible to optimize performance by adjusting the coding schemes and packet sizes to operating conditions

22. Link Quality Estimation • Estimate rate of successful reception from neighboring nodes • RSSI may not work well • Neighbors exchange estimations to derive bi-directional link quality • 2 Techniques: Passive vs. Active • Key decision factor: broadcast medium • Passive: snoop on neighbor packets • Active: broadcast beacons

23. Passive Estimation • Link sequence number snooping • Estimate inbound reception quality • Key issue • Cannot infer losses until next packet reception • E.g. dead node or mobility • Solution • With a minimum data rate, infer losses based on time • Likely to be true in periodic data collection • Asymmetric links • Require outbound transmission quality estimation • Exchange reception quality over local broadcast

24. A Good Link Estimator • Accurate • Agile yet stable • Agility and stability are at odds with each other • Small memory footprint • Simple * Woo et al

25. WMEWMA Estimator • Compute an average success rate over time, T, and smoothen with an exponentially weighted moving average (EWMA) • Average calculation • Packets Received over T divided by • Max of • Number of packets expected over T • Number of packets sent over T suggested by sequence number • Tuning parameters: • T and history size of EWMA • Performance • Yields agile and stable estimations • Uses constant memory, and is simple

26. WMEWMA better than other Link Estimators • Woo et al studied 7 estimators • by tuning to yield the same error bound • Results • WMEWMA(T, ) Estimator • Stable, simple, constant memory footprint • Compute success rate over non-overlapping window (T) • Average over an EWMA() • Key: • 10% |error| requires at least 100 packets to settle • Limits rate of adaptation

27. Agility and Error Bound • Simulation worst case: 10% error ~ 100 packet time • Assuming IID Binomial model, by the central limit theorem • Worst case (p = 0.5) requires • 10% error with 90% confidence requires ~100 packets to learn • For example: at 30sec/packet • 50 minutes for 100 packets • forwarding traffic helps to reduce this time but potentially a long delay • Major disadvantage

28. Link Estimation Metric - ETX • ETX of a link: • Predicted number of data transmissions required to send a packet over a link, including retransmissions • Calculated using forward and reverse delivery ratios of a link • How to measure: Broadcast probe packets and derive link quality information from each direction • ETX of a route: • Sum of ETX for each link in the route

29. Link Estimation Metric - ETX • Forward delivery ratio: df • Probability that a data packet delivered at recipient • Reverse delivery ratio: dr • Probability that ACK packet is delivered • Expected probability that a transmission is delivered and acknowledged is df X dr • ETX = 1 / (df X dr)

30. ETX Example

31. ETX Example Each node’s ETX value is the sum of the link ETX value along the lowest-ETX path to the destination node E

32. Cross Layer Link Estimation Better estimator with information from different layers? • Physical Layer • Packet decoding quality • Link Layer • Packet Acknowledgements • Slow to adapt • Network Layer • Relative importance of links • Keep useful links in table

33. Example: Physical Layer Information alone Insufficient PRR LQI Unacked

34. Four Bit Interface • Physical Layer • Sets white bitto denote that each symbol in received packet has a very low probability of decoding error • Link Layer • Sets ackbit on a transmit buffer when it receives a layer 2 ack for that buffer • Network Layer • Sets pin bit on a link table entry so link estimator cannot remove it from the table until the bit is cleared • Sets compare bit to indicate whether route provided by sender of packet is better than route provided by one or more of the entries in the link table

35. Four Bit Interface Details PINKeep this link in the table COMPAREIs this a useful link? ACKA packet transmission on this link was acknowledged WHITEPackets on this channel experience few errors

36. On the impact of link estimation via Broadcast versus Unicast messagesAn 802.11b study Zhang et al Infocom 06

37. Difference in Broadcast vs Unicast Reliabilty Broadcast has longer comm range - lower transmission rate for broadcast - no RTS-CTS handshake for broadcast Mean delivery rate of unicast is higher, variance is lower - retransmissions - RTS-CTS

38. Impact of Interference on Difference between Broadcast and Unicast • Estimation in the presence of unicast data traffic is dependent on whether we use broadcast or unicast messages

39. When calculating packet delivery rate, “granularity” matters • Delivery rate cut-off threshold is high: different from shorter inter-node separation and more hops

40. interferer-free vs. with-interferers • More variance “with-interferer” • Delivery rate smaller “with-interferer”

41. Mac-latency is larger “with-interefer”

42. Almost isotropic, especially in inner-band • “granularity” of DR matters

43. - isotropyinterferer-free vs. with-interferers

44. Isotropy pattern not changed significantly “with-interferer”

45. Cross-interference

46. Interference studies: Main findings • Single Interferer effects • Capture effect is significant • SINR threshold varies due to hardware • SINR threshold does not vary with location • SINR threshold varies with measured RSS • Groups of radios show ~6 dB gray region • New SINR threshold (simulation) model • Multiple interferer effects • Measured interference is not additive • Measured interference shows high variance • SINR threshold increases with more interferers

47. Capture effect White Gray Black Gray White • Finding: Capture effect is significant & SINRθ is not constant • Concurrent packet transmission does not always means packet collision (capture effect: recently studiedby Whitehouse et al.) • Systematically study capture effects and quantify the SINRθ value