1. Philip Levis Stanford University 17.i.2007 T2: What the Second Generation Holds

2. 17.i.2007EE3802 Moore’s Law

3. 17.i.2007EE3803 Bell’s Law 1950196019701980199020002010 log(users/device)

4. 17.i.2007EE3804 Law enforcement and military: pinpointing snipers in cities. Applications 33m: 111 32m: 110 30m: 109,108,107 20m: 106,105,104 10m: 103, 102, 101 Biology: redwood micro- climates and trends Sustainable architecture: monitoring and conserving water/energy use. Medicine: monitoring patients outside the office.

5. 17.i.2007EE3805 Many Tiny Low-Cost Devices Weighing the costs –Cost of device –Cost of deployment –Cost of maintenance Unseen and in uncontrolled environments –A tree, a body, a faucet, a river, a vineyard Wireless is inherent to embedded sensor networks –Reduces cost of deployment and maintenance –Wires not feasible in many environments

6. 17.i.2007EE3806 Sensornets Today Patch (tiny nodes) Transit Gateway (PC, cellphone, stargate) Backend (PC)

7. 17.i.2007EE3807 The Hardware Two platform classes: gateway and embedded wireless. Linux: MB of RAM Active power: W Sleep power: mW TinyOS: KB of RAM Active power: mW Sleep power: µW 3 orders of magnitude AA Battery for a year: ~2.7 Ah / (365 days * 24 hours) = 300µA avg. draw - Energy is defining metric: lifetime, form factor, resources

8. 17.i.2007EE3808 Moore’s Law

9. 17.i.2007EE3809 Moore’s Law with Energy

10. 17.i.2007EE38010 Microcontrollers

11. 17.i.2007EE38011 A Brand New World Cost, scale, lifetime and environment require wireless –Wireless makes energy the limiting factor –Moore’s Law has not followed an energy curve –Need for long-lived deployments means that ultra low-power nodes must still spend 99% of their time asleep. These extreme energy limitations, coupled with long lifetimes, large numbers, and embedment, completely change hardware design, software design, OS structure, network protocols, and application semantics.

12. 17.i.2007EE38012 Outline A Brave New World Platforms and hardware considerations Operating systems and software Networking and network protocols An open alliance

13. 17.i.2007EE38013 Sample Platforms 4-10kB RAM40-250 kbps

14. 17.i.2007EE38014 Why So Little? Power

15. 17.i.2007EE38015 Where the mica2 Energy Goes Active20-25mA Idle13-18mA Idle, radio off3mA Power-down110µA 2002

16. 17.i.2007EE38016 Where the Telos Energy Goes Active18-21mA Idle17-20mA Idle, radio off50µA Power-down10µA 2004

17. 17.i.2007EE38017 Lifetime 2 AA batteries is ~2700mAh To last a year, average draw must be 2-300µA Radio is principal cost PlatformDrawLifetime Mica2 active20 mA5.5 days Mica2 idle13 mA8 days Mica2 power-down110 µA~3 years Telos active18 mA6 days Telos idle17 mA6 days Telos power-down10 µA~30 years

18. 17.i.2007EE38018 CPU Utilization (mica2) Application uses 0.01% - 0.4% of the CPU From “Simulating the Power Consumption of Large-Scale Sensor Network Applications,” Shnayder et al., SenSys 2004.

19. 17.i.2007EE38019 Platform and Hardware Considerations Three axes for optimization: sleep power, wakeup times, and active power Radio increasingly dominates power profile –Low-power reception is critical to long-term deployments Need fine-grained control of component power states –MCU power state depends on external components Lowest power states depend on timers Platforms are evolving quickly, and there is much variety –BTnode3, tinynode, etc.

20. 17.i.2007EE38020 Outline A Brave New World Platforms and hardware considerations Operating systems and software Networking and network protocols An open alliance

21. 17.i.2007EE38021 In the Beginning (1999) Wireless sensor networks are on the horizon… … but what are they going to do? –What problems will be important? –What will communication look like? –What will hardware platforms look like? An operating system would provide a common execution environment for building and researching systems… … but how do you design one with these uncertainties?

22. 17.i.2007EE38022 TinyOS Goals (2000) Allow fine-grained concurrency Require very few resources Adapt to hardware evolution Support a wide range of applications Be robust Support a diverse set of platforms

23. 17.i.2007EE38023 TinyOS Basics (2000) A program is a set of components –Components can be easily developed and reused –Components can be easily replaced –Components can be hardware or software Allow hardware/software boundary to easily change Hardware has internal concurrency –Software must have it as well Hardware is non-blocking –Software must be so as well

24. 17.i.2007EE38024 TinyOS Basics, Continued (2002) Data Link Protocol Data Link Protocol Hardware Crypto Software Crypto Component Interface Task

25. 17.i.2007EE38025 TinyOS Composition Packet TimersLogging Application Tree Routing Single-hop packet Timer Logging Storage Routing Collection

26. 17.i.2007EE38026 TinyOS Composition Packet TimersLogging Application Tree Routing Single-hop packet Timer Logging Storage Routing Collection

27. 17.i.2007EE38027 TinyOS Goals, Revisited Allow fine-grained concurrency: tasks Require very few resources: no threads, components Adapt to hardware evolution: components Support a wide range of applications: flexible boundaries Be robust: component encapsulation Support a diverse set of platforms: replacing components

28. 17.i.2007EE38028 TinyOS Timeline 1999: First platform (30 nodes) 2000: rene platform, 4-5 groups 2002: mica platform, 35+ groups, TinyOS 1.0 released 2003: mica2 platform, 100+ groups, TinyOS 1.1 released 2004: Telos/micaZ, 200 downloads/day, 100K+ nodes 2006: 500K+ nodes, TinyOS 2.0

29. 17.i.2007EE38029 TinyOS 2.x (2005-6) Evolution of TinyOS to address recent developments –Need for better standardization –Growing community interest and contribution –Increasing platform diversity –Transition from research to commercially viable platform Four basic developments –Scheduler: improve robustness and flexibility –Network types: platform interoperability –Platform definition: simplify porting –Power management: OS support for long-term deployments

30. 17.i.2007EE38030 The Power of Counting A TinyOS program is a complete component graph Counting across a program is a very powerful primitive –How many packet senders are there? –How many timers are used? –How many tasks are there? Only components used by an application are included Assigning each element a unique counter –3 senders: sender 0, sender 1, sender 2 –6 timers: timer 0, timer 1, … timer 5 –8 tasks: task 0, task 1, … task 7

31. 17.i.2007EE38031 Tasks and the Scheduler Tasks represent internal software concurrency A component posts a task, which the OS runs later Counting provides compile-time guarantees, leads to simpler code, and can enforce fairness policies 80 cycles (10µs) to post and run a task

32. 17.i.2007EE38032 Network Types Depending on the processor, there are different data alignment and layout restrictions –ARM vs. x86 vs. AVR vs. MSP430 Network protocols often use “network ordering” –Big endian, byte aligned, OSes have conversion functions TinyOS supports network types at the language level –Automatically pack/unpack as needed struct data_packet_t { nx_am_addr_t source; nx_am_addr_t dest; nx_uint8_t; opt; nx_uint16_t sNo; nx_uint8_t index; }; opt source index dest sNo opt source index dest sNo opt source index dest sNo MSP430 x86 network type

33. 17.i.2007EE38033 MCU Power States State External Interrupts External Clock Main Clock Timer0EEPROMADC, I/O Idle Ext. Standby Standby Power-save Power-down ATMega128 While reading/writing packets to the radio, the MCU cannot drop below the idle state. While waiting for packet reception or transmission to complete, the MCU can drop into power-save.

34. 17.i.2007EE38034 Computing a Power State SchedulerMcuSleep CC2420 SPI Bus Application Hardware State

35. 17.i.2007EE38035 Computing a Power State SchedulerMcuSleep CC2420 SPI Bus Application Hardware State Application turns on radio

36. 17.i.2007EE38036 Computing a Power State SchedulerMcuSleep CC2420 SPI Bus Application Hardware State Application turns on radio –Radio sets sleep state dirty

37. 17.i.2007EE38037 Computing a Power State SchedulerMcuSleep CC2420 SPI Bus Application Hardware State Application turns on radio –Radio sets sleep state dirty Scheduler completes –Sees sleep state is dirty, recalculates sleep state

38. 17.i.2007EE38038 Computing a Power State SchedulerMcuSleep CC2420 SPI Bus Application Hardware State Application turns on radio –Radio sets sleep state dirty Scheduler completes –Sees sleep state is dirty, recalculates sleep state –Goes to power-down

39. 17.i.2007EE38039 Computing a Power State SchedulerMcuSleep CC2420 SPI Bus Application Hardware State Application turns on radio –Radio sets sleep state dirty Scheduler completes –Sees sleep state is dirty, recalculates sleep state –Goes to power-down Packet wakes up TinyOS

40. 17.i.2007EE38040 Computing a Power State SchedulerMcuSleep CC2420 SPI Bus Application Hardware State Application turns on radio –Radio sets sleep state dirty Scheduler completes –Sees sleep state is dirty, recalculates sleep state –Goes to power-down Packet wakes up TinyOS –Stack starts reading in packet from bus –Bus sets sleep state dirty

41. 17.i.2007EE38041 Computing a Power State SchedulerMcuSleep CC2420 SPI Bus Application Hardware State Application turns on radio –Radio sets sleep state dirty Scheduler completes –Sees sleep state is dirty, recalculates sleep state –Goes to power-down Packet wakes up TinyOS –Stack starts reading in packet from bus –Bus sets sleep state dirty Scheduler completes –Sees sleep state is dirty, recalculates sleep state

42. 17.i.2007EE38042 Computing a Power State SchedulerMcuSleep CC2420 SPI Bus Application Hardware State Application turns on radio –Radio sets sleep state dirty Scheduler completes –Sees sleep state is dirty, recalculates sleep state –Goes to power-down Packet wakes up TinyOS –Stack starts reading in packet from bus –Bus sets sleep state dirty Scheduler completes –Sees sleep state is dirty, recalculates sleep state –Goes to idle

43. 17.i.2007EE38043 Putting It Together Components are lightweight state machines –Encapsulated state –Respond to external events TinyOS remains reactive with low-overhead tasks –80 cycles to post and run –Allows components to interleave execution cooperatively Language techniques to optimize call paths and provide some compile-time promises of system behavior Fine-grained component control enables fine-grained power management

44. 17.i.2007EE38044 The Big Picture Clean-slate OS design –Not an RTOS, significant departures from prior embedded Make as much of a program static as possible –Compile-time, not run-time promises –Component isolation through careful design Language/OS co-design –Brand-new domain enables breaking out of the law of C Hide complexity when possible, expose it when needed –As we better understand sensornets and their requirements, versions of TinyOS can provide more policy

45. 17.i.2007EE38045 Outline A Brave New World Platforms and hardware considerations Operating systems and software Network protocols and a network architecture An open alliance

46. 17.i.2007EE38046 Networking and Network Protocols United States National Research Council thesis: “embedded sensor networks are different.” –Embedment, energy limitations, data-centric operation –They’re not just a new set of IP devices If not IP, what are they? –What are the critical services and mechanisms? –What does a sensornet protocol stack look like? –Maybe it is just IP…

47. 17.i.2007EE38047 Testing the Hypothesis We don’t know what these networks will look like, so we’ll build a framework so everyone can figure it out TinyOS: component-based OS –Can easily switch components –Designed for and supports major requirements: low power, hardware diversity, robustness, etc. A lot of people start using TinyOS, and 6 years later…

48. 17.i.2007EE38048 Sensor Network Protocols Today Phy Link/MAC Topology Network Transport Application RadioMetrix RFM CC1000 Bluetooth 802.15.4 eyes nordic WooMacSMAC TMAC WiseMAC FPS MintRoute ReORg PAMAS CGSR DBF MMRP TBRPF BMAC DSDV ARADSR TORA GSR GPSR GRAD Ascent SPIN SPAN Arrive AODV GAF Resynch Yao Diffusion Deluge Trickle Drip Regions Hood EnviroTrack TinyDB PC TTDD Pico FTSP STRAW ZMAC TOSSIM Drain

49. 17.i.2007EE38049 Defining an Architecture Protocol research, applications, and real deployments show sensornets to have a diverse set of requirements –Traditional layer boundaries do not fit well Commonalities emerge from these diverse efforts From these commonalities we can begin to understand and define a sensor network architecture –Provide a structure for protocols and applications, separating concerns and promoting interoperability

50. 17.i.2007EE38050 Why a New Architecture? Short answer: we haven’t seen IP take over Long answer: the Internet assumes a usage model –Independent end-to-end flows –Host-centric communication –Edge networks with a shared infrastructure Sensor networks do not follow this model –Collaborative protocols –Data-centric communication –Sensing removes distinction between edge and core

51. 17.i.2007EE38051 The Two Major Protocols Most simple sensornets start with two protocols Protocol 0: Dissemination –Reliably deliver data to every node in a network –Reconfiguration, programs, queries –Basic mechanism for network control Protocol 1: Collection –Deliver data from every node to one or more sinks –Basic mechanism for gathering data

52. 17.i.2007EE38052 Dissemination Use local broadcasts and packet suppression –Scale to a wide range of densities –Control transmissions over space 100% eventual reliability –Disconnection, repopulation, etc. –Continuous process Maintenance: exchange metadata (e.g., version numbers, hashes) at a low rate to ensure network is up to date Propagation: when a node detects an inconsistency, the network quickly broadcasts the new data

53. 17.i.2007EE38053 Trickle Polite gossip: “Every once in a while, broadcast what data you have, unless you’ve heard some other nodes broadcast the same thing recently.” Energy efficient, fast, and scalable –Maintenance: a few sends per hour –Propagation: across large multihop networks in seconds –Scalability: thousand-fold changes in density

54. 17.i.2007EE38054 Trickle Algorithm Time interval of length  Redundancy constant k (e.g., 1, 2) Maintain a counter c Pick a time t from [0,  ] At time t, transmit metadata if c < k Increment c when you hear identical metadata to your own Transmit updates when you hear older metadata At end of , pick a new t

55. 17.i.2007EE38055 Example Trickle Execution time  B C transmissionsuppressed transmissionreception A k=1 c 0 0 0

56. 17.i.2007EE38056 Example Trickle Execution time  B C transmissionsuppressed transmissionreception A k=1 c 0 1 0 t A1

57. 17.i.2007EE38057 Example Trickle Execution time  B C transmissionsuppressed transmissionreception A k=1 c 0 2 0 t A1 t C1

58. 17.i.2007EE38058 Example Trickle Execution time  B C transmissionsuppressed transmissionreception A k=1 c 0 2 0 t A1 t B1 t C1

59. 17.i.2007EE38059 Example Trickle Execution time  B C transmissionsuppressed transmissionreception A k=1 c 0 0 0 t A1 t B1 t C1

60. 17.i.2007EE38060 Example Trickle Execution time  B C transmissionsuppressed transmissionreception A k=1 c 1 0 1 t A1 t B1 t C1 t B2

61. 17.i.2007EE38061 Example Trickle Execution time  B C transmissionsuppressed transmissionreception A k=1 c 1 0 1 t A1 t B1 t C1 t B2 t C2

62. 17.i.2007EE38062 Example Trickle Execution time  B C transmissionsuppressed transmissionreception A k=1 c 1 0 1 t A1 t B1 t C1 t A2 t B2 t C2

63. 17.i.2007EE38063 Example Trickle Execution time  B C transmissionsuppressed transmissionreception A k=1 c 0 0 0 t A1 t B1 t C1 t A2 t B2 t C2

64. 17.i.2007EE38064 Propagation Simulated 400 node 16 hop network Time to reception in seconds Set  l = 1 sec,  h = 1 min 20s for 16 hops Wave of activity Real 71 node 8 hop network Time to reception in seconds Set  l = 1 sec,  h = 1 min Time to “catch,” long tail

65. 17.i.2007EE38065 Trickle Overview Trickle scales logarithmically with density Can obtain rapid propagation with low maintenance –In example deployment, maintenance of a few sends/hour, propagation of 30 seconds Controls a transmission rate over space –Coupling between network and the physical world Trickle is a nameless protocol –Uses wireless connectivity as an implicit naming scheme –No name management, neighbor lists… –Stateless operation (well, eleven bytes)

66. 17.i.2007EE38066 The Internet Narrow Waist The Internet narrow waist is at the network layer: IP Separate many transport and application protocols from underlying data-link technologies But sensornets have many different network protocols (collection, dissemination, etc.) Local coordination and communication pushes the narrow waist downwards…

67. 17.i.2007EE38067 Sensor Network Narrow Waist Hypothesis: in sensor networks, the narrow waist of is at layer 2 (single hop) But there are many L2 packet formats and protocols –International spectrum allocation –Media access Work at the network layer and above can provide guidance on what the narrow waist needs to provide

68. 17.i.2007EE38068 Narrow Waist Proposal: FWP X SINK X ABC Fair Waiting Protocol –A multihop protocol receives a fair share of local bandwidth –Also provides protocol isolation Basic mechanism: grant-to-send –Every transmission has an optimal time value t –Only the recipient may transmit during this time t Current area of active work

69. 17.i.2007EE38069 Sensor Network Architecture Edge devices within the larger Internet cloud –Not transit networks Data-centric within –Collaborative operation –Snooping, address-free –Complex single-hop requirements –Traditional layers do not apply Addressable from without –Management, configuration, etc.

70. 17.i.2007EE38070 IETF 6lowpan WG Question: how do you connect a low-power embedded wireless network to the larger Internet with IPv6? Wide range of issues: –IP adaptation/Packet Formats and interoperability –Addressing schemes and address management –Network management –Routing in dynamically adaptive topologies –Security, including set-up and maintenance –Application programming interface –Discovery (of devices, of services, etc) –Implementation considerations Definition without evaluation is dangerous… –Per-hop vs. end-to-end fragmentation and assembly –Cost: prr -(f+h) vs. fh x prr -1

71. 17.i.2007EE38071 Outline A Brave New World Platforms and hardware considerations Operating systems and software Network protocols and a network architecture An open alliance

72. 17.i.2007EE38072 Changing the World 33m: 111 32m: 110 30m: 109,108,107 20m: 106,105,104 10m: 103, 102, 101

73. 17.i.2007EE38073 TinyOS Alliance Low-power wireless embedded networks need a close collaboration between academia and industry –Many unsolved problems –Revisiting old assumptions –Remaining grounded in practical concerns The TinyOS Alliance mission –“Provide a forum to facilitate… the development and maintenance of a stable,technically-sound base of TinyOS technology and surrounding tools through the creation of standard interfaces and protocols, vetted extensions, open reference implementations, technical documents,testing and verification suites, and educational materials…”

74. 17.i.2007EE38074 TinyOS Alliance Structure (tentative) Grassroots: it’s about the contributors and their work –Follow an IETF model Members, corporate members, contributing members Working groups Steering committee Steering Committee WG Members

75. 17.i.2007EE38075 Learn, Participate, and Use http://www.tinyos.net/

76. 17.i.2007EE38076 Questions