1. Networks of Tiny Devices Embedded in the Physical World David Culler Computer Science Division U.C. Berkeley www.cs.berkeley.edu/~culler Intel Research Berkeley

2. 5/13/2002TinyOS IPAM2 Technology Push Complete network embedded systems going microscopic Processing Storage Sensing Actuation Communication LNA mixer PLL baseband filters I  Q  Power

3. 5/13/2002TinyOS IPAM3 Application Pull Complete NW embedded systems going microscopic Huge space of new applications Circulatory Net Habitat Monitoring Condition-based maintenance Disaster Management Ubiquitous computing Monitoring & Managing Spaces

4. 5/13/2002TinyOS IPAM4 Bridging the Technology-Application Gap Power-aware, communication-centric node architecture Tiny Operating System for Range of Highly- Constrained Application-specific environments Network Architecture for vast, self-organized collections Programming Environments for aggregate applications in a noisy world Distributed Middleware Services (time, trigger, routing, allocation) Techniques for Fine-grain distributed control Demonstration Applications

5. 5/13/2002TinyOS IPAM5 Critical issues Highly constrained devices –power, storage, bandwidth, energy, visibility –primitive I/O hierarchy Observation and action inherently distributed –many small nodes coordinate and cooperate on overall task The structure of the SYSTEM changes Devices ARE the infrastructure –ad hoc, self-organized network of sensors Highly dynamic –passive vigilance most of the time –concurrency-intensive bursts –highly correlated behavior –variation in connectivity over time –failure is common

6. 5/13/2002TinyOS IPAM6 A de facto platform for EmNets Developed a series of wireless sensor devices TinyOS concurrency framework Messaging Model Networking stacks (RF and Serial) Multihop routing Several Key components –sensing, logging, data filters, broadcast Simulation tools DARPA NEST OEP USC robomote

7. 5/13/2002TinyOS IPAM7 Many Research Groups using it UCB –NEST –SensorWeb –Blackout –Glaser structures –CBE –BFD –BRWC UCLA USC, ISI Rutgers winlab Intel Bosch Crossbow U Wash Rutgers UIUC NCSA U Virginia Ohio State UCSD Dartmouth MIT UT Austin, ASU, Iowa Accenture and many more

8. 5/13/2002TinyOS IPAM8 The MICA architecture Atmel ATMEGA103 –4 Mhz 8-bit CPU –128KB Instruction Memory –4KB RAM 4 Mbit flash ( AT45DB041B) –SPI interface –1-4 uj/bit r/w RFM TR1000 radio –50 kb/s – ASK –Focused hardware acceleration Network programming Rich Expansion connector –i2c, SPI, GIO, 1-wire –Analog compare + interrupts TinyOS tool chain sub microsecond RF synchronization primitive 2xAA form factor Atmega103 Microcontroller TR 1000 Radio Transceiver 4Mbit External Flash 51-Pin I/O Expansion Connector Power Regulation MAX1678 (3V) DS2401 Unique ID 8 Analog I/O 8 Programming Lines SPI Bus Coprocessor Transmission Power Control Hardware Accelerators Digital I/O

9. 5/13/2002TinyOS IPAM9 Rich Sensor board PHOTO TEMP MAGNETOMETERACCELEROMETER MICROPHONE SOUNDER Mica PINS ADC Signals (ADC1-ADC6) I 2 C Bus On/Off Control Interrupt X Axis Y Axis Gain Adjustment Mic Signal Tone Intr 2.25 in 1.25 in Microphone Accelerometer Light Sensor Temperature Sensor SounderMagnetometer

10. 5/13/2002TinyOS IPAM10 More Sensors and Actuators Motor-Servo board interfaces any combination of two motors, servos, and solenoids to a toy car platform whisker board for obstacle detection digital accelerometer (ADXL202) board for crude odometry GPS Board Weatherboard light, temp, humidity, barometric pressure, occupancy (thermopile)

11. 5/13/2002TinyOS IPAM11 Acadia National Park Mt. Desert Island, ME Great Duck Island Nature Conservancy Getting Ready for Outdoors

12. 5/13/2002TinyOS IPAM12 A Operating System for Tiny Devices? Traditional approaches –command processing loop (wait request, act, respond) –monolithic event processing –bring full thread/socket posix regime to platform Alternative –provide framework for concurrency and modularity –never poll, never block –interleaving flows, events, energy management => allow appropriate abstractions to emerge

13. 5/13/2002TinyOS IPAM13 Tiny OS Concepts Scheduler + Graph of Components –constrained two-level scheduling model: threads + events Component: –Commands, –Event Handlers –Frame (storage) –Tasks (concurrency) Constrained Storage Model –frame per component, shared stack, no heap Very lean multithreading Efficient Layering Messaging Component init Power(mode) TX_packet(buf) TX_pack et_done (success ) RX_pack et_done (buffer) Internal State init power(mode) send_msg (addr, type, data) msg_rec(type, data) msg_sen d_done) internal thread Commands Events

14. 5/13/2002TinyOS IPAM14 Application = Graph of Components RFM Radio byte Radio Packet UART Serial Packet ADC Tempphoto Active Messages clocks bit byte packet Route map routersensor appln application HW SW Example: ad hoc, multi-hop routing of photo sensor readings 3450 B code 226 B data Graph of cooperating state machines on shared stack

15. 5/13/2002TinyOS IPAM15 TOS Execution Model commands request action –ack/nack at every boundary –call cmd or post task events notify occurrence –HW intrpt at lowest level –may signal events –call cmds –post tasks Tasks provide logical concurrency –preempted by events Migration of HW/SW boundary RFM Radio byte Radio Packet bit byte packet event-driven bit-pump event-driven byte-pump event-driven packet-pump message-event driven active message application comp encode/decode crc data processing

16. 5/13/2002TinyOS IPAM16 Dynamics of Events and Threads bit event filtered at byte layer bit event => end of byte => end of packet => end of msg send thread posted to start send next message radio takes clock events to detect recv

17. 5/13/2002TinyOS IPAM17 Maintaining Scheduling Agility Need logical concurrency at many levels of the graph While meeting hard timing constraints –sample the radio in every bit window  Retain event-driven structure throughout application  Tasks extend processing outside event window  All operations are non-blocking  lock-free scheduling queue

18. 5/13/2002TinyOS IPAM18 Demonstration applications 29 Palms Cory Hall network –½ million packets over 3 weeks Surge network and environment display 800 node ad hoc network CBE Glaser Shakes Granlibakken retreat watcher Robomote Group response => continued application focus more real and long lived more dynamics extract architecture and create framework

19. 5/13/2002TinyOS IPAM19 Example TinyOS study UAV drops 10 nodes along road, –hot-water pipe insulation for package Nodes self-configure into linear network Synchronize (to 1/32 s) Calibrate magnetometers Each detects passing vehicle Share filtered sensor data with 5 neighbors Each calculates estimated direction & velocity Share results As plane passes by, –joins network –upload as much of missing dataset as possible from each node when in range 7.5 KB of code! While servicing the radio in SW every 50 us!

20. 5/13/2002TinyOS IPAM20 Structural performance due to multi-directional ground motions (Glaser & CalTech). Wiring for traditional structural instrumentation + truckload of equipment Mote infrastructure 1515 1313 1414 6 5` 5` 1515 1 8 Mote Layout 1212 9 Comparison of Results

21. 5/13/2002TinyOS IPAM21 Energy Monitoring/Mgmt System 50 nodes on 4 th floor 5 level ad hoc net 30 sec sampling 250K samples to database over 6 weeks

22. 5/13/2002TinyOS IPAM22 Energy Monitoring Network Arch sensor net GW 802-11 control net GW 20-ton chiller PC scada term modbus UCB power monitor net PC telegraph MYSQL Browser

23. 5/13/2002TinyOS IPAM23 Meeting Social Network

24. 5/13/2002TinyOS IPAM24 Wealth of Research Challenges Large numbers of highly constrained (energy & capability), connected devices –able to be casually deployed in infrastructure (existing or in design) –imperfect operation and reliability –operating in aggregate New family of issues across all the layers application service network system architecture technology mgmt / diag / debug algorithm / theory prog / data model

25. 5/13/2002TinyOS IPAM25 Node Communication Architecture Application Controller RF Transceiver Classic Protocol Processor Direct Device Control Hybrid Accelerator

26. 5/13/2002TinyOS IPAM26 Novel Protocol Examples Low-power Listening Really Tight Application-level Time Synchronization Localization Wake-up MACs Self-organization

27. 5/13/2002TinyOS IPAM27 Low-Power Listening Costs about as much to listen as to xmit, even when nothing is received Must turn radio off when there is nothing to hear. Time-slotting and rendezvous cost BW and Latency Can turn radio on/of in <1 bit  Small sub-msg recv sampling  Trade small, infrequent tx cost for freq. Rx savings

28. 5/13/2002TinyOS IPAM28 Exposing Time Synchronization Up Many applications require correlated data sampling Distributed time sync accuracy bounded by ½ the variance in RTT. Successful radio transmission requires sub-bit synchronization  Provide accurate timestamping with msg delivery  Jitter < 0.1us (propagation) + 0.25 us (edge capture accuracy) + 0.625 us (clock synch)

29. 5/13/2002TinyOS IPAM29 Localization Many applications need to derive physical placement from observations –Spatial sampling, proximity, context-awareness Radio is another sensor Sample baseband to estimate distance –Need a lot of statistical data –Calibration and multiple- observations are key Acoustic time-of-flight alternative –Requires good time synchronization Noise Error Noise Error

30. 5/13/2002TinyOS IPAM30 Statistical Approach Distance Probability X Y Node A Node B

31. 5/13/2002TinyOS IPAM31 Integrated Architecture Chip Area ~5 mm 2 –AVR core with protocol Accelerators.5 mm 2 –16 Kbytes on-chip ram 4 mm 2 –ADC –800-1GHz FSK transceiver, -90dBm receive sens’y.5 mm 2 Expected sleep current = 1 uW –lifetime on a single AA = 400+ years Expected active (processing current) –Processor @ 4 Mhz < 1 mW –Radio: 1mW power consumption, 100Kbps –ADC: 20 pJ/sample 10 Ksamps/second =.2 uW.

32. 5/13/2002TinyOS IPAM32 Networking Hands-on Experience with Large Networks of Tiny Network sensors  intense constraints, freedom of abstraction Re-explore entire range of networking issues –encoding, framing, error handling –media access control, transmission rate control –discovery, multihop routing –broadcast, multicast, aggregation –active network capsule (reprogramming) –localization, time synchronization –security, network-wide protection –density independent wake-up and proximity est. Fundamentally new aspects in each

33. 5/13/2002TinyOS IPAM33 The Nodes are the Infrastucture Simple Epidemic Algorithm Schema if (new mcast) then take local action retransmit modified request Examples: Network wakeup, command propagation –Build spanning tree »record parent Naturally adapts to available connectivity Minimal state and protocol overhead => surprising complexity in this simple mechanism

34. 5/13/2002TinyOS IPAM34 Network Discovery: Radio Cells

35. 5/13/2002TinyOS IPAM35 Network Discovery

36. 5/13/2002TinyOS IPAM36 Controlled Empirical Study Experimental Setup – 13x13 grid of nodes – separation 2ft – flat open surface – Identical length antennas, pointing vertically upwards. – Fresh batteries on all nodes – Identical orientation of all nodes – The region was clean of external noise sources. Range of signal strength settings Log many runs

37. 5/13/2002TinyOS IPAM37 Example “epidemic” tree formation

38. 5/13/2002TinyOS IPAM38 Final Tree

39. 5/13/2002TinyOS IPAM39 Power Laws ? Most nodes have very small degree (ave =.92) Some have degree = 15% of the population Few large clusters account for most of the edges

40. 5/13/2002TinyOS IPAM40 Open Territory => Many Children Example: Level 1

41. 5/13/2002TinyOS IPAM41 Open Territory => Many Children Example: Level 2 – variation in distance

42. 5/13/2002TinyOS IPAM42 Open Territory => Many Children Example: Level 3 – long links

43. 5/13/2002TinyOS IPAM43 Understanding Connectivity 16 transmit power settings For each transmit power setting, each node transmits 20 packets. Receivers log successfully received packets. Nodes transmit one after the other in a token-ring fashion No collisions. Define “range”: radius where 75% of enclosed nodes receive 75% of packets Often good nodes at a distance probability of reception from center node vs xmit strength

44. 5/13/2002TinyOS IPAM44 Importance of Asymmetric Links Asymmetric Link: –>65% successful reception in one direction –<25% successful reception in the other direction 10%-25% of links are asymmetric Many long links are asymmetric –in large field it is likely that someone far away can hear you –what does this mean for protocol design?

45. 5/13/2002TinyOS IPAM45 Collisions are primary factor Nodes out of range may have overlapping cells –hidden terminal effect Collisions => these nodes hear neither ‘parent’ –become stragglers As the tree propagates –folds back on itself –rebounds from the edge –picking up these stragglers. Seen in many experiments Mathematically complex because behavior is not independent beyond singe cell

46. 5/13/2002TinyOS IPAM46 Stragglers significant fraction of links point ‘backwards’

47. 5/13/2002TinyOS IPAM47 Minimal lessons learned Don’t think about wireless networks as bunch of circles of radius r –connectivity is a probability distribution –falls off with distance, but not as simple fading law –shape varies with time and context With large, dense arrays the low-probability events are common Must strike a balance in exploiting structure and adapting to observed behavior Want simple local rules that have predictable, robust global behavior

48. 5/13/2002TinyOS IPAM48 More typical routing for sensor nets Current applications dominated by data acquisition –route from many nodes to nearest gateway –aggregate from many nodes –routing determined by simple local rules Nodes listen to data transfers from neighbors –carries hop-count info –monitors link goodness of potential ‘parents’ –dynamically selects best node is lesser hop count –includes hysterisis and continuous rediscovery –gateways emit null data with 0-hop Much to understand about how such algorithms manage major change

49. 5/13/2002TinyOS IPAM49 Self-propagating Programs? TinyOS components support class of applns. Tiny virtual machine adds layer of interpretation for specific coordination Primitives for sensing and communication Small capsules (24 bytes) Propagate themselves through network

50. 5/13/2002TinyOS IPAM50 Multihop Bandwidth Management Should self-organize into fair, dynamic multihop net Hidden nodes between each pair of “levels” –CSMA is not enough P[msg-to-base] drops with each hop –Investment in packet increases with distance –need to optimize for low-power fairness! RTS/CTS costly (power & BW)  Local rate control to approx. fairness  Priority to forwarding, adjust own data rate  Additive increase, multiplicative decrease  Listen for retransmission as ack

51. 5/13/2002TinyOS IPAM51 Example: Multihop Adaptive Transmission Control B 1414 1515 1818 1717 1616 Max rate: 4 samples/sec - rate = 4p Channel BW ~20 p/s - cannot expect more than 1/3 thru parent Monitor number of children (n)  (n) ~ 1/n  = ½ p’ = p +  (n) on success (echo) p’ = p *  without rate control, success drops ~½ per hop

52. 5/13/2002TinyOS IPAM52 Key Experience Really good at building tinyOS subsystems –non-blocking, split-phase event structures Internalized the “state of constant change” paradigm –ex: maintain routing tree by constantly rebuilding it –soft state that is always suspect –simple one-way protocols Operating in the aggregate Simple mechanisms to accomplish large goals –MAC, ATC Out of the box on networking abstractions –Low-power listen, wake-up, statistical sampling, weighted aggregation Understanding of large scale dynamics

53. 5/13/2002TinyOS IPAM53 Feeding experience back into simulation

54. 5/13/2002TinyOS IPAM54 Rich set of additional challenges Efficient and robust security primitives Density independent wake-up, aggregation –sensor => can use radio in ‘analog’ mode Resilient aggregators Programming support for systems of generalized state machines Programming the unstructured aggregate –SPMD, Data Parallel, Query Processing, Tuples Understanding how an extreme system is behaving and what is its envelope –adversarial simulation Self-configuring, self-correcting systems

55. 5/13/2002TinyOS IPAM55 The “Law of Miniaturization” Each major generation is increasingly smaller, more deeply interactive, arrives when previous is at its strength Vast majority of computing will be small, embedded, devices connected to the physical world –actually the case today, but... –not connected to us, the web, or each other – this will change Time Integration Log R Mainframe 99 Innovation Minicomputer Personal Computer Workstation Server

56. 5/13/2002TinyOS IPAM56 Where to go for more? http://www.tinyos.net/tos/ Jason Hill, Robert Szewczyk, Alec Woo, Seth Hollar, David Culler, Kristofer Pister. System architecture directions for network sensors. ASPLOS 2000. David E. Culler, Jason Hill, Philip Buonadonna, Robert Szewczyk, and Alec Woo. A Network-Centric Approach to Embedded Software for Tiny Devices. EMSOFT 2001.