Link Estimation and Tree Routing

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1 Network Embedded Systems Sensor Networks Link Estimation and Tree Routing 1 Marcus Chang, Slides: Andreas Terzis

2 Outline Link quality estimation Examples of link metrics Four-Bit Wireless Link Estimation Tree Routing Low-Power 2

3 Routing in Wired Networks Static/Default routes Dynamic routing Share neighbor information Calculate shortest path Problems in sensor networks Ad-hoc deployment No geo-location Small packets, e.g., 127 bytes on Telosb RAM and CPU limits neighborhood table and shortest path alg. 3

4 Radio Range Unit Disc (simplification) MAC Broadcast Unicast C A D Link B Asymmetric 4

5 Connectivity Graph 5

6 Multi-hop Routing Routing: A B Problems: Optimal route? Metric? Route discovery? Network changes? Asym. links? B A? 6 C?

7 Tree Routing (Shortest Path Example) Sink advertises (beacons) No-hop nodes listen N-hop nodes beacons 1 hop 1 hop 2 hop Sink 2 hop 1 hop 1 hop 2 hop 3 hop 2 hop 7 3 hop 2 hop

8 Tree Routing (Shortest Path Example) Routing: A B Multiple routes A B: 3 hop C B: 2 hop 1 hop 1 hop 2 hop Sink 2 hop 1 hop 3 hop 1 hop 2 hop A? 4 hop 2 hop 3 hop 2 hop 8 3 hop C?

9 Routing Problems No geolocation Low *, etc. Which metric? N-to-N? Sink? A??...? 9 C

10 Routing Distributed protocol used to determine the optimal multi-hop paths nodes use to reach destination Distance Vector, Link State Optimality is defined as maximizing or minimizing certain aspect of the path characteristics Delay, bandwidth, probability of loss Link metric is an abstract version of link characteristics Possible to construct path metric from link metrics Desired metric qualities Maximize percentage of packets delivered at the sink Minimize number of transmissions necessary to deliver the packets to the sink 10

11 Hop Count is a bad metric Reception rate deteriorates quickly as transmission range grows Hop count tends to pick long links lossy links Instead, one should select high quality links 11

12 Estimating Link Quality Ideal Link Metric Predict probability of successful packet delivery Impossible to implement, have to use heuristics General idea: Measure link characteristics and hope that past is a good indication of future How to measure link characteristics: Passive vs. Active Passive: snoop on packets the neighbors transmit Active: Nodes transmit beacons/probes to measure link characteristics 12

13 Estimating Link Quality What link characteristics can we measure? Hardware (radio dependent): RSSI: Received Signal Strength Indication Energy (dbm) SNR: Signal-to-Noise Ratio LQI: Link Quality Indicator Packet correctness confidence Software PRR: Packet Reception Ratio 13

14 Passive estimation of link PRR Node overhears packets from neighbors Packet sequence number for inferring packet loss Issue: cannot infer loss until hearing the next packet Infer losses based on time Assume minimum data rate is known 14

15 Challenge #1: PRR changes over time How do we keep track? WMEWMA Computes an average success rate over time, T, and smoothes with an exponentially weighted moving average (EWMA) Average calculation Packet Received over T divided by Max of Number of packets expected over T Number of packets sent over T suggested by sequence number T=30, α=0.6 Tuning parameters: T and history size of EWMA Performance Yields agile and stable estimations Uses constant memory, and is very simple 15

16 Challenge #2: Too many neighbors! Nodes in dense networks may have more neighbors than they can track in the neighborhood table If a neighbor is not on the table then cannot collect link statistics and eventually use it to route traffic! Three components: insertion, eviction, reinforcement Goal: Keep a sufficient number of good neighbors regardless of node density FREQ: Least frequently used LRH: Least-Recently-Heard CLOCK: Least-Recently-Updated Table size= 40 16

17 Metric idea #1: Ignore bad links What happens if we only use links with PRR estimate > threshold? Taming the Underlying Challenges of Reliable Multihop Routing in Sensor Networks by Woo et al. (SenSys 2003) 17

18 Evaluation (simulation) Network Graph analysis 400 nodes, 20x20 grid Sink placed at corner Results SP produces more shallow trees Increased threshold, increased path reliability 18

19 Evaluation (testbed) Empirical Evaluation 50-node network, 5x10 grid, 8 foot spacing, indoors SP(70%) failed to create a tree! 19

20 Metric Idea #2: Avoiding static thresholds Using a static PRR threshold for selecting links can lead to disconnections, suboptimal performance Ideally we want to dynamically select the links with the highest PRRs ETX metric: Expected number of Transmissions required to successfully send a packet over link/path Proposed in A High-Throughput Path Metric for Multi-Hop Wireless Routing by DeCouto et al. (MobiCom 2003) 20

21 ETX metric definition ETX (link) 1 / Prob(TX success ) 1/(Prob(Pkt success ) * Prob(ACK success )) ETX (path) = ETX(link) Estimating link ETX: Prob(Pkt success ) measured fwd delivery ratio r fwd Prob(ACK success) measured rev delivery ratio r rev Link ETX 1 / (r fwd r rev ) 21

22 Measuring Delivery Ratios Each node broadcasts small link probes periodically Nodes remember # probes received over past T seconds Reverse delivery ratios estimated as r rev pkts received / pkts sent Forward delivery ratios obtained from neighbors (piggybacked on probes) 22

23 ETX Evaluation Destination-Sequenced Distance-Vector Routing (DSDV) DSDV hop-count better DSDV ETX Best static route found experimentally 23

24 Metric idea #3: Use PHY-layer information Some radios provide an estimate of the quality of received packets Example for CC240 LQI: 110-Sum of Hamming distances over first eight symbols Translate LQI to per-link PRR For example, 1/(LQI^3) Path quality is the sum of link qualities 24

25 Information from individual sources has limitations Physical Layer Provides quality of incoming packet (e.g., LQI) Information is available only for received packets Can overestimate quality of bursty links Link Layer Learning link quality from periodic beacons can be slow to adapt Network Layer Estimating link qualities at the network layer is inefficient and slow to adapt 25

26 Metric idea #4: : Use information from multiple layers 4-bit Link Estimator Physical layer If white bit is set, medium quality is high during reception Used to quickly decide whether a link should be even considered Link layer Ack bit is set when an acknowledgement is received Increases frequency of ETX estimation Network layer Set pin bit for entries that are important Compare bit indicates whether new neighbor is better than one of the current entries 26

27 Evaluation 27

28 Delivery rates 28

29 Low-Power Tree Routing Problem Statement Environmental monitoring applications require long network lifetimes Radio is highest power consumer How to reduce the time nodes spend idle listening and overhearing? Real Life deployments 29

30 Life Under Your Feet 30

31 Koala Size: 100s All nodes sleep Low-Power-Probing Gateway wakes up neighbor nodes Nodes awake rebroadcasts probes Probes interfere constructively 31

32 Koala Nodes gather neighborhood information from probes Gateway Queries neighborhood information from 1 st hop Uses first hop information to query 2 nd hop, etc. Construct connectivity graph Choose links based on RSSI threshold Randomly choose links for diversity Pull data one node at a time Put network back to sleep 32

33 DCGenome Data Center Monitoring 33

34 Wireless Reliable Acquisition Protocol Nodes: 100s Link estimation: RSSI threshold to remove bad links LQI PRR approximation Calculate path ETX 34

35 MEDiSN 35

36 MEDiSN Nodes: 10s Uses CTP 36

37 Summary Link estimation Static thresholds can lead to segmented networks Cross layer link estimation can improve performance Tree routing Next week: IP Networking 37

38 Schedule Week 1: Introduction and Hardware Week 2: Embedded Programming Week 3: Medium Access Control Week 4: Link Estimation and Tree Routing Week 5: IP Networking Week 6: Near Field Communication Week 7: (seminar, no lecture) Week 8: Energy Management Week 9: Review and Midterm Week 10: Time Synchronization Week 11: Localization Week 12: Energy Harvesting Week 13: (seminar, no lecture) Week 14: TBD 38

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