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

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

4 Radio Propagation Basics Path Loss (large-scale) Other sources of distortion:  Multi-path effects: Reflection, diffraction, scattering, absorption o Bounced waves, new waves, multiple waves  Doppler effect o Shift in frequency (loss of center) o Due to mobility or unstable oscillators  Thermal noise (white noise), crosstalk  Hardware callibration effects  Multipath effects Fading refers to variation as a function of time  May be slow (eg shadowing) or fast (eg multipath) Packet reception reliability depends on modulation, encoding, and length

5 Path Loss Model n depends on environments: 2 or more outdoors, below 2 in open indoors with line of sight, 3..6 in offices and concrete buildings) Friis free space equation: PL(d)=PL(d 0 ).(d 0 /d) 2, d 0 is far field dist. When not in free space, replace 2 with n for path loss exponent Log normal model for shadowing, taking obstacles into account: X σ is zero-mean Gaussian dist. rv with σ std.dev

6 Noise Variability Across Nodes

7 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

8 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.)

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

10 *Zhou et. al. 04

11 *Cerpa et. al. 03

12 *Cerpa et. al. 03

13 *Cerpa et. al. 03

14 *Cerpa et. al. 03

15 Antenna height *CS 213 – Boelter Hall court yard measurements - 04

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

17 Reception vs RSS

18 Links from A Given Source (1)

19 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

20 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 not isotropic 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

21 *Cerpa et. al. 03

22 *Cerpa et. al. 03

23 *Cerpa et. al. 03

24 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

25 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

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

27 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

28 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

29 *Cerpa et. al. 03 Temporal Variation

30 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

31 Consecutive Link Correlation Conclusion: Sum of RNPs along path are greater than actual cost; using conditional RNP on successive links yields more accurate cost measure Implication: estimation via implicit acks is desirable * Cerpa et al Mobihoc05 Sum of RNP’s of consecutive links

32 *Cerpa et. al. 03

33 *Cerpa et. al. 03

34 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)

35 *Zhao et. al. 03 4B6BSECDED Manchester SECDED coding has lower packet loss (as it is resilient to packet losses, and has error correction capability) Other two schemes statistically indistinguishable Manchester coding is more tolerant to bit corruption

36 *Zhao et. al. 03

37 Effect of Modulation Scheme

38 *Cerpa et. al. 03

39 *Cerpa et. al. 03

40 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 not isotropic Found 5 to 30% of asymmetric links  Not correlated with distance 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

41 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

42 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

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

44 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

45 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 o Compute success rate over non-overlapping window (T) o Average over an EWMA()  Key:  10% |error| requires at least 100 packets to settle  Limits rate of adaptation

46 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

47 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

48 Link Estimation Metric - ETX Forward delivery ratio: d f  Probability that a data packet delivered at recipient Reverse delivery ratio: d r  Probability that ACK packet is delivered Expected probability that a transmission is delivered and acknowledged is d f X d r ETX = 1 / (d f X d r )

49 ETX Example

50 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

51 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

52 Example: Physical Layer Information alone Insufficient Unacked PRR LQI

53 Four Bit Interface Physical Layer  Sets white bit to denote that each symbol in received packet has a very low probability of decoding error Link Layer  Sets ack bit 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

54 Four Bit Interface Details WHITE Packets on this channel experience few errors ACK A packet transmission on this link was acknowledged PIN Keep this link in the table COMPARE Is this a useful link?

55 On the impact of link estimation via Broadcast versus Unicast messages An b study Zhang et al Infocom 06

56 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

57 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

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

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

60 Mac-latency is larger “ with-interefer ”

61 Almost isotropic, especially in inner-band “ granularity ” of DR matters

62 - isotropy interferer-free vs. with- interferers

63 Isotropy pattern not changed significantly “ with-interferer ”

64 SP with Threshold High threshold (e.g. 70%) fails to form a tree  Works fine in simulation!  link quality degrades when there is traffic  High threshold leads to network partition  Echo the observation made in Zhao’s work Lower threshold (e.g. 40%) is also problematic  Tree prunes and rebuilds over time when traffic is high

65 MT No predefine threshold is necessary Captures both reliability and energy cost Routing cost builds upon individual estimations along the path  Cost = hops + number of expected link retransmissions if link quality = 100%, MT reduces to normal SP routing

66 Findings (I) Hop distribution and success rate  longer majority hop-count yields higher success Hop distribution and distance  Evidence of long links, potentially reliable Retransmissions are not too effective  MT yields ~80% success rate  packets delivered only experience 1 retransmission along the path  A maximum of 2 retransmission per hop can  Needs a maximum of 3 per hop to achieve over 90% end-to-end success rate

67 Findings (II) Link failure detection with consecutive packet loss leads to instability Stability and Congestion  link quality fluctuates at congestion period  creates global instability BS can hear half the number of neighbors in the network even with a low power setting MT metrics build upon link estimations are stable No cycles are detected

68 Discussions (I) Passive snooping  What are the assumptions for this to work?  Estimation takes too long  Can we infer from BER before FEC? (tricky)?  But missing start symbol is the major cause! Neighborhood management argument  Do you buy it? Stability  Do we care? Congestion  How to avoid it?  Scheduled communication?

69 Discussions (II) Can we define a hop?  One hop neighbor?  What is the averaged hop distance? Deployment  What’s the expected hop-count?  What distance or transmit power should we use? Overhead  Anecdotal setting of route update rate  Can it be adaptive?

70 Discussion (III) Power  No address on power management  How does it work with scheduled communication which avoids overhearing?  Potentially run over low-power listening  What’s used in Great Duck Island DSDV (Yarvis et al. ICPP Workshop 2002)  Different kinds of link estimation and routing cost  Do we need to prevent cycle like DSDV in a relatively static network? N-to-N Routing?

71 Effect of Radio Ultrawideband

72 Cross-interference

73 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

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

75 Models