Routing in Sensor Networks

1. Routing in Sensor Networks Prabal Dutta CS 294-11, Oct 25, 2005

2. Some Communication Abstractions • Collection (MintRoute) • Dissemination (Trickle) • Point-to-Point (BVR) • Aggregation (TAG, Synopsis Diffusion) • Neighborhoods (Hood) • Data-centric Storage (GEM, PathDCS) • Attribute-based Routing (Directed Diffusion)

3. Slides borrowed from:A Holistic Approach to Multihop Routing for Sensor Networks Alec Woo Dissertation Talk Computer Science Division, UC Berkeley with David Culler and Terence Tong

4. Key Takeaways • Physical connectivity is not unit disk • What does connectivity look like? • How to estimate connectivity? • Often, more neighbors than slots in NBR TBL • When to insert? Evict? • How to avoid thrashing? • Routing algorithms use cost metrics • What are the right metrics? Hops? Distance? METX? • Collection routing is a very common pattern

5. 2 2 2 2 1 1 2 Boolean Connectivity Assumption 0 A

6. Physical Connectivity • Measure • Average link quality among many pairs of nodes at different distances • Communication Range? • 3 regions, with a large transitional region Effective Region Transitional Region Clear Region

7. Implications Transitional Region • Deployment: (X-axis) (In-situ analysis) • Communication range = effective region • Individual nodes (Y-axis) • Discover connectivity = link estimation • Hear many nodes in transitional region • How to define a “neighbor”? • Zhao et al., SCALE

8. Neighborhood: A Fuzzy Concept • Many potential neighbors • Short effective region • Short sensing range • Few good ones (blue) • Large gray region • Neighbors > Table-size • If not in table, • can’t estimate • Don’t rely on density control • Adapts to all cell density Get in Get out Neighbor Table • General solution: • down-sample to suppress gray nodes • maintain frequent nodes

9. Average Hop-Count Contour Plot

10. Derive Connectivity Graph through Passive Link Estimation • Link sequence number snooping • Estimate inbound reception quality • Key issue • Cannot infer losses until next packet reception • Solution • Rely on a network-wide minimum data rate • infer losses based on it • Bi-directional estimation • Require outbound transmission quality estimation • Exchange reception quality over local broadcast • E.g piggyback on route updates

11. A Good Estimator • Accurate • +/- 10% error, with a high confidence • Agile yet stable • Relative to message opportunities rather than time • Small memory footprint • Many neighbors to estimate! • Simple • This is a low-level operation

12. On-Line Table Management Process • Insertion Policy • Adaptive down-sampling hysteresis • Throw a coin, only insert if success • Eviction and Replacement Policy • Classical Cache Replacement Policy • FIFO, LRU (LRH), Clock • Borrow Database Techniques • Estimate most frequent tokens of a data stream • FREQUENCY (Manku et al.)

13. Freq always keeps 50% or more of the table entries in maintaining the good neighbors Key Results • Fixed-size table as cell density increases # Good neighbors > Table size 2nd 3rd 1st 40 Number of Potential Neighbors

14. Link estimator provides Cost Functions • SP on physical connectivity graph • SP with threshold on logical connectivity graph • Path Reliability (Yarvis et al.) • Product of link quality along the entire path • Exponential drop: (link success rate)# of hops • Assumes no link retransmissions • Minimum Transmission (MT) • Cost is based on link quality • Cost = E[total number of trans.] • ETX (De Couto et al.) • Implicit retransmission assumption 70% 70% 50%

15. Tree-Building Approach • Variant of a distributed distance-vector protocol • Goal: stable and reliable tree (nodes are relatively immobile) • Different from discovering paths quickly in mobile computing • Operate over a dynamically changing physical connectivity graph • Environmental changes • Node failures • Low-rate periodic route messages (low bandwidth) • Carry “cost” to tree root • Piggyback link estimations • Hear neighbor’s “cost” and store in table • Select minimum cost neighbor for routing • Route damping (stability) • Periodic vs. asynchronous • Switching threshold for noisy cost

17. Self-Organizing Networks • Using onlysimple local rules for highly resource-constrained nodes to self-organize into a globally consistent and robust network • Protocol design consideration • Bandwidth/energy • Amount of states/complexity • Memory footprint • One instance: Multihop routing

18. Overview • Problem decomposition into 3 local processes • Connectivity defines relative to link quality estimation • Neighbor table management to build weighted logical connectivity graph • Cost functions to exploit such graph • Observe global properties • End-to-end success rate • Hop distribution • Topology Stability • Extensive simulations and empirical experiments • MintRoute, released in TinyOS 1.1

19. Roadmap • Physical Connectivity in Reality • Connectivity Graph Derivation with Link Estimations • Neighborhood Management • Tree-Based Routing Study

20. Central Limit Theorem Prediction • For a 10% error with a 95% interval • worst case for agility is at least 100 packets

21. Estimator Study • Study 7 different estimators • EWMA, Flip-Flop EWMA, MA, Time-weighted MA, Packet Loss/Success Interval, WMEWMA • Compared by tuning each to the same objectives • Verify with empirical traces • See details in thesis • Results • WMEWMA(T, ) Estimator • Stable, simple, constant memory footprint • Compute success rate over non-overlapping window (T) • Average over an EWMA() • Key Implication • 10% |error| requires at least 100 packets to settle • Limits rate of adaptation

22. Roadmap • Physical Connectivity in Reality • Connectivity Graph Derivation with Link Estimations • Neighborhood Management • Tree-Based Routing Study

23. Details • Insert • Set prob. such that insertion rate < reinforcement rate • Down-sample prob.  min(1,Table Size / # Neighbors Est.) • Estimate # neighbors based on periodic route beacons • Reinforce if in table • Cache hit (FIFO, LRH, Clock) • Node’s Counter++ (Freq) • bypass down-sampling for reinforcement • Evict • Cache policies • evict for each insertion • Freq: Counter--, • Counter == 0 becomes replaceable • If all Counters > 0, drop insertion

24. Implications • Non-threshold based neighborhood selection • No estimation required • One-hop neighbor • Based on competitiveness relative to the goodness metric • Other goodness metric that augment neighborhood selection • Control in/out degree on the logical connectivity graph • Higher-level changes on cell density will not affect system functionality • Connectivity graph adapts with its best using limited resources • New neighborhood interface and abstraction

25. A Select Good Routes Based on ? Neighbor management using FREQUENCY A A Link Estimation using WMEWMA Holistic Approach to Routing • Now, the connectivity graph is built

26. Many-to-One Data Collection • A common routing service for data collection • Simple form of directed-diffusion • Tree rooted at the sink node where data is collected

27. Evaluation Roadmap • Key observations: • Hop distribution, end-to-end success, stability • Graph analysis • 80x80 grid • SP, SP(%), MT • Rule out SP because of poor reliability • Packet-level simulation • 10x10 grid, (max 2 retrans./hop) • Broadcast and DSDV (periodic route selection) • Neighbor table management • Freq + Routing Goodness -> MTTM • Empirical (Mica/Mica2 Motes) • 5x10 grid and 30-node random placement, smote • SP(%), MT with large enough table • max 2 retrans./hop, deliberate congestion High Level Large Low Level Small

28. Graph Analysis Key Results • Hop-Distribution and Reliability to BS

29. Simulation Key Results Hop-Count Distribution End-to-end Success vs. Distance Stability

30. Empirical Study • Restudy connectivity vs. distance • Put nodes at end of effective region (~ worst case) • 8 feet • Study SP(70%), SP(40%), MT • Key observations: • SP(70%) fails • SP(40%) fails • Hard threshold fails under congestion Link quality drops under traffic

31. End-to-end Success vs. Distance Hop-Count Distribution Empirical Key Results Different from simulations! Effective Region is 8 feet

32. Congestion and Stability 30-node network Topology Stability # Route Changes Per 5 Route Messages Link Estimation % Time (s) Possible Congestion/Rate Control: Woo et al. (Mobicom ’01)

33. Mitigate Instability • Subtle overflow bug in link estimation • Confidence-interval filtering on link estimation • Link estimation to tree root can affect stability on the entire tree • Switching threshold helps stability, but sacrifices end-to-end success rate

34. Cross-layer Interactions Ave. # of Parent Changes Per Route Update 3.02 2.49 0.52 0.14 0.10

35. Induced Interference Ave. # of Parent Changes Per Route Update 0.30 0.10

36. Node Failure

37. Used by GDI ’03, TinyDB, TASK (Intel) TinyOS 1.1 Release Surge as a Network Analysis Tool Crossbow: www.xbow.com Incorporated with low-power listening ~97% success rate on mica2 Current Status Source: Crossbow

38. Connectivity Study Choi et al., Zhao et al., Cerpa et al., Ganesan et al. Link estimation IGRP, EIGRP, De Couto (Mobicom ’03), Kim et al. (Mobicom ’99) Neighborhood Management Limiting Logical Neighborhood Size (Miller et al., Simulation of computer networks ’ 87) Random Selection (Shacham et al., ICC ’88) Routing Metrics De Couto (Mobicom ’03) Draves et al. (Microsoft Research TR-2004-18 March ’04) LIR, least gain routing opt. for spatial reuse (SRNTN ’88) LRR, link cost = physical-level interference, (Tactical Communication Conference ’90) Sensor Network Routing Real experiment running DSDV + Path Reliability Metric (Yarvis et al. IWAHN ’02) Related Work Summary

39. Future Work • Reverse Tree Routing Support • any-to-any routing • Co-design of query processing and networking • Query-informed routing • See June Communication of the ACM ‘04

40. Thank you!

41. Backup Slides

42. 144-node, 12x12 grid network with Rene Motes A Connectivity Cell • Joint work with Ganesan et al. • 2-feet spacing • Low transmit power • Open tennis court

43. RSSI & Link Quality • Can we use RSSI to predict link quality? • Low packet loss => good RSSI • But not vice versa • Interference from traffic • Similar findings • Zhao et al. (RFM sensor networks) • De Couto et al. (802.11 networks)

44. Approximate Connectivity Variations • Approximate time variations

45. Time-Varying Connectivity • Link quality varies over time over a 5-hour period over an 8-hour period

46. Routing Architecture Timer Send originated data message Application Send route update message Run parent selection and send route message periodically Parent Selection • Cycle detected • choose other parent Originating Queue Forward Queue Cycle Detection Estimator Neighbor Table Table Management Forwarding message Data message • All message • sniff and • estimate • Route message • save information Filter All Messages • discard non data packet • discard duplicate packet

47. 40-70% Topology over Time Est. Link Quality 49 70-100% 42 0- 40% 35 Tree Depth Feet 28 1 21 2 14 3 7 0 7 14 21 28 35 42 49 56 63 Feet

48. Channel Utilization Contour

49. Routing Cost: Actual vs. Est.

50. “Design and Implementation of a Sensor Network System for Vehicle Tracking and Autonomous Interception”, Submitted to OSDI 2004 Pursuer and Evader Application The Berkeley NEST team