Design Lessons from an Unattended Ground Sensor System*

1. Design Lessons from an Unattended Ground Sensor System* Lewis Girod† CS 294-1 23 Sept 2003 †Center for Embedded Networked Sensing, girod@cs.ucla.edu *Work done at Sensoria Corp, supported by DARPA/ATO contract DAAE30-00-C-1055. Sensoria team: R. Costanza, J. Elson, L. Girod, W. Kaiser, D. McIntire, W. Merrill, F. Newberg, G. Rava, B. Schiffer, K. Sohrabi

2. Introduction SPEC (J. Hill): 4MHz/8bit, 3K/0K • Networked embedded systems come in a variety of sizes • RAM is a primary tradeoff • Costs power, cost to shutdown • Enables greater complexity • Enables development while postponing optimization Mica2 (Berkeley/Xbow): 8MHz/8bit, 4K/128K MK2 (UCLA/NESL): 40MHz/16bit, 136K/1M SHM (Sensoria): 300MIPS+FP/32bit, 64M/32M

3. Motivation for EmStar • EmStar is a run time environment designed for Linux-based distributed embedded systems • Useful facilities (process health/respawn, logging, emulation) • Common APIs (neighbor discovery, link interface, etc) • Designed for larger memory footprint (avoids hard limits) • Many of the ideas and motivations for EmStar derived from our experience with SHM • Modularity, robustness to module failure • System transparency at low cost to developers • Some parts of EmStar are used in SHM & elsewhere • Time Synch service • Audio Server

4. System Objectives and Design

5. Objectives • Unattended Ground Sensor (UGS) System • Fully autonomous operation • Ad-hoc deployment • Scaling unit: 50 nodes • All operations and protocols local to 50 node region • No global operations or context required * *Fancy graphics taken from the official SHM website

6. Adaptive and Self-Configuring • Self-localizing without GPS*: acoustic ranging • Build map of relative locations • Adaptive/Resilient to environmental conditions • e.g. wind, sunny days, background noise • Self-assembled data network • TDMA MAC layer • Typically 10-hop diameter with 100 nodes • Adaptive/Resilient to RF environment *In this application, GPS is avoided for security reasons. In other applications, obstructions and foliage can be an issue

7. Maintain coverage via Actuation • Vigilant units detect failed unit(s) • Remaining units autonomously move in to maintain coverage

8. Demonstration Requirements • 200x50m outdoor field • 100 nodes, 10m spacing • Sunny afternoon • 85°F, 20 MPH wind • No preconfigured state • GPS-free relative geolocation to 0.25m • Detect downed nodes, move to maintain coverage within 1 min

9. SHM Project Design Choices: Optimize for rapid development • Concurrent HW/SW development • Compressed schedule • Aggressive scaling milestones • Logistical problems with debugging system of 100 nodes in 200x50m field • Complex software required • 150K lines C code • 30 processes • 100 IPC channels • Power is not the driving constraint • Continuous vigilance, rapid response are project requirements • System lifetime target: < 1 day

10. System Configuration • 300 MIPS RISC processor with FPU • 64M RAM / 32M Flash • 2 50kbps 2.4GHz data radios, TDMA, frequency-hopping, star-topology MAC, 63 hopping patterns • 4 channels full-duplex audio • 3-axis magnetometer / accelerometer • 2 mobility units, with integrated thrusters • Linux 2.4 kernel • Optional wired ethernet (for Devel/Debug only)

11. Results: Acoustic Ranging • Ground truth was hand-surveyed, +/- 0.5m • Ranges not temperature compensated in demo • Ranges with angles are more accurate • Angle from TDOA of two or more ranges, must be consistent • Bug discovered after the fact, caused large errors

12. Results: Radio Utilization • Graph shows traffic at three bases over a complete run • Initial spikes: • Tree formation • Lots of ranging • Quiescent rates • Heartbeats to detect down nodes • Maintenance of trees and location • Reaction to dynamics

13. Challenges in Implementation • Dealing with a dynamic environment • Adapt to wind, weather, RF connectivity • Dealing with noise • Rejecting outliers from timesync, ranges, angles • Filtering neighbor connectivity, insignificant changes to range/angle • Dealing with failure • Node failure • Infrequent crashes (e.g. FP exceptions from transient bad data) • Fault tolerance at process boundaries, avoid ripple effect • Dealing with complexity • Cross layer integration vs. modularity.. Or both? • What are the right set of primitives for coordination?

14. Case Study: Acoustic Ranging

15. Degree of correlation as a function of time offset Amount of Correlation T Basic TOF Ranging • Basic idea: • Sender emits a characteristic acoustic signal • Receiver correlates received time series with time-offsets of reference signal to find “peak” offset

16. Basic AOA Estimation • 16 possible paths • First pick best speaker • Then estimate angle from TDOA of one or more consistent ranges

17. Field Table orientation Merging Orientation Flooding mlat orientation Cluster Mlat (CH only) mlat AR Acoustic Signal Neighbor Discovery Neighbor Discovery Radio0 Radio1 Acoustic Ranging, Version 1 • First cut implemented explicit cluster coordination protocol • Lots of error cases to handle, hard to handle all efficiently • Very timing sensitive (sync) • Did not scale past 20 nodes • Can’t range across clusters • Best acoustic neighbors may be in other clusters • MLat merging algorithm is error prone • Overuse of flooding • Soft state reflood of cluster MLats and orientation data

18. Flooding w/ Hop by Hop Time Conv Reliable State Sync AR Audio Server Timesync Service V2: Decomposing AR AR

19. Audio Sample Server • Continuously samples audio, integrates to Timesync • Eliminates error-prone “Synchronized start” • Enables acquisition of overlapped sample sets • Buffers past N seconds, exposes buffered interface • Data access can be triggered after the fact: relaxes timing constraints on trigger message • Can process overlapping chirps by requesting overlapping retrievals, rather than having to pick one and ignore other • Enables access so audio device from multiple apps • Ranging can coexist with acoustic comm subsystem* *Acoustic comm was developed as a backup channel to be used in event of RF jamming

20. Senders* Sync to green nodes Sync to purple nodes Sync to P&G nodes Inter-node Timesync: RBS • Key idea: • Receiver latency more deterministic than sender • Thus, common receivers of a sender can be synched by correlating the reception times of sender’s broadcasts • It’s your only option if you don’t control the MAC * For sender sync, senders must be in some other sender’s broadcast domain

21. TimeSync Service • Inter-node Sync: Implementation of “RBS” • Computes conversion params among all nodes in each cluster • CH does computation, reports parameters to CM’s • Intra-node Sync: Codec sample clocks • Clock pairs reported by audio server • Map time of DMA interrupt to sample number • Outlier rejection and linear fit to find offset and skew estimate • Yields more consistent result than “synch start” • Multihop Time Conversion • Graph of sync relations through system • Conversion from one element to another requires path through graph. Gaussian error at each step ~sqrt(hops)

22. Timebase: A Time: 144 Timebase: C Time: 732 Timebase: E Time: 234 A B C D E Hop-by-hop Time Conversion • Problem: • Nodes have ability to convert within cluster but not outside • Could continually broadcast conversion parameters… BUT • They are continuously varying • Large amount of data to transmit across network • Solution: Integrate time conversion with routing • Routing layer knows about packets that contain timestamps* • Convert timestamps en route • At cluster boundaries • At destination node • Integrated with flooding • Can fail if sync graph  route *Unclear what the right API is here: we simply added code to flooding.

23. Reliable State Synchronization • Problem: • Need to reliably broadcast the latest range data to N-hop away nodes, so they can build a consistent coordinate system • Should have reasonable latency and low overhead • V1 addressed this problem with periodic refresh • Cluster heads retransmit Mlat tables every 15 seconds • Problems: Traffic load from redundant sends, latency on msg loss • Traffic load forced new protocol: • Send a hash when there was no change since last refresh • If the hash has not been seen, request full version • But, still has 15 second latency on lost data • V2 introduced a “Reliable State Sync” protocol (RSS)

24. RSS Design • Semantics: • Reliably converges on latest published state • Does not guarantee client sees every transition • Robust and Efficient, structurally similar to SRM/wb: • Based on reliable transfer of a sequenced log of “diffs”. • Pruning of the log is done with awareness of log semantics (replaced or deleted keys are pruned) • Per-source forwarding trees (MST of connectivity graph) • Local repair, up to complete history, from upstream neighbor • New or restarted nodes will download all active flows from upstream neighbor

25. RSS API • Node X publishes current state as Key-Value Pairs • Diffs are reliably broadcast N hops away from X • Each node within N hops of X eventually sees the data X published • API presents each node’s KVPs in its own namespace • Caveat: transmission latency, loss, edge of hopcount can cause transient inconsistencies State Sync “Bus” 1 2 3 4 1: A=1 1: B=2 2: A=3 2: C=4 1: A=1 1: B=2 2: A=3 2: C=4 1: A=1 1: B=2 2: A=3 2: C=4 2: A=3 2: C=4 Note: 2-hop publish from 1 doesn’t reach 4

26. Reliable State Sync “bus” types/orient types/ranges types/orient types/orient types/ranges mlatd ar/mlat orient_bcast Audio Data ar/request_chirp ar_recv orient/pub_seq Acoustic Signal orient/status ar_send audiod orientd audio/[02]/sync_bin Chirp Notification syncd Conversion Software Module Output Device Message/Device File Distributed Module Chirp Notification Conversion Flooding with hop by hop time conversion Putting it back together: AR V2

27. But, there are many error cases: • REQ lost? • ACK lost? • Bcast lost to some receivers? • Bcast delayed in queue? • Bcast lost to sender? • CMs join two clusters; may be busy ranging in other cluster. • Inaccurate codec sync start? • Interference in acoustic channel? • Reply from sender lost? • Reply from receiver(s) lost? • How long to wait for stragglers? • CH failure loses all ranges for cluster AR V1 Event Diagram Cluster Head (Coordinator) Cluster Member (Sender) Cluster Member (Receiver) mlatd (CH) AR (CH) AR (CM) AR (CM) Reliability challenges: The sender is the linchpin: an error in sender sync affects all ranges to receivers, and replies from receivers can’t be interpreted without the sender reply. If connectivity to sender is bad and the broadcast is lost, all receivers waste CPU on a useless correlation. Implementing reliable reporting is made more complex because retx’d receiver replies must be matched to a past sender. If not enough data for cluster mlat, request ranging to specific missing cluster members Send Range REQ to first CM in round robin order, check busy ACK with preferred start time Bcast Range Start, specify code Timestamp msg arrival. Sender delays before starting to ensure rough sync, and reports exact time offset from bcast to codec start Acoustic Signal Run correlation, report time offset from bcast to detection in data • Big complexity increase to…: • Range across clusters • Coordinate adjacent clusters • Do regional mlats • Average multiple sync bcasts CH waits for stragglers Report new ranges and notify mlat when round robin thru CMs completes

28. What can go wrong here? • Collisions in acoustic channel. • Flooded message delayed beyond audio buffering (16 seconds). • Flooded message dropped for lack of sync relations along route. • Node restart causes ranges to/from that node to be dropped. AR V2 Event Diagram Waiting for enough data to compute mlat Waiting for chirp request Continuous Sampling Continuous Sync Maintenance Waiting for chirp notification mlatd ar_send audiod syncd ar_recv If not enough data for mlat, request chirp and wait for a while • Key design points: • Encapsulate timing critical parts, no timing constraints on reliability. • If a receiver can’t sync to sender it won’t attempt correlation. Chirp audio (audiod on remote node records it) Acoustic Signal Flood Chirp notification message, with hop-by-hop conversion at flood layer Retrieve samples from buffer and correlate in separate thread Publish new range to N hop away neighbors Try mlat again with new data in separate thread

29. Key Observations • No coordination required • Simplifying transport abstractions • Continuous operation and service model

30. Key Observations • No coordination required • If mlatd doesn’t have enough data it triggers chirping to start generating more data • Exponential backoff on chirping with reset when data is lost. • Simplicity of system lets designer focus on these details • ar_send & ar_recv are slaves to request and notify messages. • Transparently, ar_recv can receive overlapping triggers and buffer the data for correlation • Priority scheme decides the best order to process queued correlations, based on past success/failure and RF hopcount • Simplifying transport abstractions • Continuous operation and service model

31. Key Observations • No coordination required • Simplifying transport abstractions • Flooding takes care of delivering a local time • State Sync provides consistency for data input to mlatd • Efficiently supports a potentially large number of keys (~1000), enabling full regional mlat at each node (no “merging”) • Mlat takes 10-15min, sync is consistent on that timescale • Failure of one node only loses range data for that node • Continuous operation and service model

32. Key Observations • No coordination required • Simplifying transport abstractions • Continuous operation and service model • Eliminates many inconsistencies and corner cases • Reduces the number of states or modes • Simplifies interfaces to services • Recovery from faults without coordination – just wait for stuff to start working again • Service model supports multiple apps concurrently

33. The Catch • Of course, the catch is power consumption • Continuous operation can be wasteful • Modularity can be less efficient than cross-layer integration • Interesting questions: • How much is gained by fine-grained shutdown, plus the added coordination overhead, relative to more coarse grained shutdown and periods of continuous operation? • For instance, the AR system could shut down after generating an initial map, and only wake up when something moves.

34. The End! For more information on EmStar, see http://cvs.cens.ucla.edu/emstar/

35. Design Evolution • Initial design strategy: shortest path first • Modular decomposition according to best guess at time • Making a full-blown, generalized service is much more work than a one-off feature – so tradeoff considered case by case • Problem: As more is learned these tradeoffs fit more poorly • Unmanageable complexity to address problems • Redesign: • Factor out common components • Plan for known scaling problems • Remaining modules are of manageable complexity, yet usually achieve a more complete and correct implementation • More sophisticated inter-module dependencies

36. Radios • Each node has two radios • TDMA, frequency hopping radios • 63 hopping patterns • Each radio can lock to one pattern • Patterns are independent “channels” • Bases on same pattern tend to be desynchronized • Base/Remote (star) topology • Base synchronizes TDMA cycle, remotes join

37. TDMA Slot Scheme • Each frame contains 1 transmit slot for the base and 1 transmit slot for each remote • Slot size implies MTU • Frame size is a constant • Base slot size is fixed: 70 byte MTU • Number of remotes inv. prop. to remote MTU • Practical MTU (40 bytes)  8 node clusters 14ms Base S Base Slot Remote 1 Remote 2 Remote 3

38. Packet Transfer • Broadcast capability • Base can use its slot to send a broadcast to all remotes, or a unicast to a single remote • Remotes can send only unicasts to base • Link layer retransmission • MAC implements link layer ACKs for unicast messages, and configurable retransmission

39. Breach Healing In this application, “healing” is intended only to address breaches created by dying nodes, not preexisting breaches. Other algorithms might also be useful, e.g. density maintenance, but were not implemented here.