1. Programming Abstractions in Wireless Sensor Networks Professor Jack Stankovic Department of Computer Science University of Virginia

2. Importance Raise the level of abstraction Move out of the domain of HW and embedded systems programmer Amplify the applications addressed – programmed by domain experts More reliable One of the most important areas of research to make WSN widely used

3. Context System Development Environment Analysis/Tools Use of System SQL/Queries Actual Programming

4. Programming Models Environmental (domain specific, tracking, monitoring, …) –EnviroSuite Data/Database Centric - Queries –SQL, TinyBD, Cougar –Statistical and range queries Middleware APIs and Services –Group Management, Token machine language (Regiment), Milan (QoS based), Agilla –Support for other models Components Database Event Agent

5. Programming Models Components –nesC Virtual Machines –Mate Functional Macro-programming –Regiment (regions, streams) (Mobile) Scripts –SensorWare Extend TCL interpreter with support for commands such as query, send, wait, replicate, … Provides execution environment for Scripts Events –Publish/subscribe –DSWare, SNEDL, Impala

6. Programming Models Model Driven Swarm computing Java Linux

7. Program What? Low level protocols (MAC, routing, …) Middleware services Application Use of system –Queries Blurs

8. Sensor/Actuator/Communication Heterogeneous Homogeneous Resource management, team formation, networking, aggregation, … Severe constraints power, memory, bandwidth, cpu, cost,...

9. Reliability Make an unreliable platform reliable!! HW (motes) are/will be very reliable Environment/wireless communication will not be reliable

10. Abstractions Program as a whole – not individual nodes Hide details (which ones, expose what) –Single node decisions –Asynchrony –Race conditions –False alarms –Retries –Physical details (e.g., location, sensor characteristics, …) –Faults –Scale –Storage locations

11. Other Questions How to disseminate programs –Flooding, viral programs, gossip protocols Efficient on small, embedded, real-time devices Long-lived systems –Disseminate again –Disseminate pieces of executable

12. Environmental Paradigm Current Paradigms –Single node abstractions –Explicit interactions between nodes –Read sensor data; fuse data; move data –Set actuators Environmental Paradigm –Reflect the physical world directly –Virtual world of entities (fire, people, vehicles, pesticide, velocity, location, areas, …)

13. EnviroSuite Way to think –Events – measurable environmental activities –Events mapped to programming objects –Computation is attached to these objects Report location, compute velocity, … Library provides protocols for sensor data processing, object maintenance, inter-object coordination, aggregation mechanisms, etc.

14. EnviroSuite

15. event-object VEHICLE { object_creation_condition = ferrous object && background sound object_resolution = 6; // minimal distance between // two events object_function = report.Location; object_function = report.Speed; } ferrous object && background sound This statement is supported by drivers in a library:

16. Level of Knowledge Required Ferrous object –Which signal processing module? –What parameter specifications need to be made? –We are programming in an application that interacts with the environment – how divorced from that fact can the programmer be?

17. Routing Protocol - SPEED USES VELOCITY Expose Velocity

18. Radio Irregularity DOI = Degree of Irregularity DOI = 0.05 DOI = 0.2 Do Not Expose

19. Data/Database Centric Query Processing Architectures (SQL-based) –Process the queries –Route the queries –Retrieve information efficiently (in time and with low energy costs) Storage Architectures –How/where to store collected data from sensors –In-network storage At local node Data centric – name the data and hash to a location Multi-resolution (hierarchies)

20. Example of Query Retrieve, every 30 seconds, the rainfall level if it is greater than 50 mm SELECT R.Sensor.getRainfallLevel() FROM RFSensors R WHERE R.Sensor.getRainfallLevel() > 50 AND $every(30); Many other extensions to query capability including statistical and range based queries

21. Content Dissemination - Caching Information source (Aperiodic updates) Data replicas (Placement?) Receivers (Refresh Rate, Accuracy) Goal: Find the optimal communication path to send sensory data from a monitored source to multiple (mobile) sinks such that energy usage is minimized.

22. Middleware APIs Group Management –Create –Terminate –Merge –Join/Leave –Assign function Track target Classify target Map temperature region –Consensus

23. Group Management IR Camera

24. Group Management IR Camera

25. Examples: Tracking and Map Regions Base Station

26. Group Management - API –Create_Group(name,function,criterion,atleast,acc uracy) - implicit and explicit –Destroy_Group(name) –Join() –Leave() –Merge() –Move_COG() –Expand() -- to gain sensing confidence –Shrink() -- to save power –Commit(grp_ID) - to synchronize group re- configurations

27. Other Services Naming Directory Location Monitor Configure …

28. Question How general/specific should a middleware service be? Function of severity of resource constraints Severe Careful interoperability Exactly what is needed and no more! Excess Capacity More general services Useable by many

29. Functional Macro- Programming (Regiment) Apply function to data from a stream Aggregate samples from a stream Filter stream Define/Disseminate Events from a area (region) Area Base Station Abstract Regions Streams

30. Regiment (cont.) Set frequency Fidelity Contour finding Combine regions Anchors (leaders for a region) Functional Language

31. Regiment Abstractions –Streams, Spaces, Events, Anchors (leader for a region), When (time) –Regions – group of nodes with some relationship to each other Like the function for a group in previous middleware slides Ex. – all nodes within k radio hops of node N Ex. – all nodes whose proximity sensor records a hit

32. Token Machine Language (TML) Kind of middleware to support Regiment PL Regiment –Compiler transforms a macro-program written in functional style into an efficient nodal program based on a token machine –Token machine is an intermediate language (target) for Regiment

33. TML NODE Token Handlers – Atomic Tasks Token from Network Name of Token Handler Data Generate Tokens

34. Components - nesC Underlying execution model (TinyOS) –Tasks and Events –Split phase operation Modules –Component Specification – Interfaces –Component Implementation Configurations –Wiring Diagrams

35. Tasks and Events Tasks –Deferred computation –Non-preemptable by other tasks –Scheduled FCFS from task queue Events –Execution of an interrupt handler –Runs to completion –Can preempt tasks and can be preempted by other events

36. Application Example RFM Radio byte i2c Temp photo Messaging Layer clocks bit byte packet Radio Packet Routing Layer sensing application application HW SW ADC messaging routing UART Packet UART byte

37. Components - nesC The nesC model: –Interfaces: uses provides –Components: modules configurations Application:= graph of components Component A Component B Component D Component C Application configuration Component E Component F EnviroTrack on top of nesC

38. Interfaces Examples of interfaces: interface StdControl { command result_t init (); command result_t start (); command result_t stop (); } interface Timer { command result_t start (char type, uint32_t interval); command result_t stop (); event result_t fired (); } interface SendMsg { command result_t send (uint16_t addr, uint8_t len, TOS_MsgPtr p); event result_t sendDone (); } interface ReceiveMsg { event TOS_MsgPtr receive (TOS_MsgPtr m); } StdControl.ncTimer.nc ReceiveMsg.ncSendMsg.nc

39. Modules Implements a component’s specification with C code: module MyCompM { provides interface X; provides interface Y; uses interface Z; } implementation { …// C code } MyCompM.nc

40. Modules Posting Tasks: module BlinkM {… } implementation {… task void processing () { if(state) call Leds.redOn(); else call Leds.redOff(); } event result_t Timer.fired () { state = !state; post processing(); return SUCCESS; }… } BlinkM.nc

41. Configuration Example Blink application Wiring Example configuration BlinkC { } implementation { components Main, BlinkM, ClockC, LedsC; Main.StdControl->BlinkM.StdControl; BlinkM.Clock->ClockC; BlinkM.Leds->LedsC; } ClockCLedsC Main BlinkC BlinkM BlinkC.nc

42. Future? Different component models? nesC components are “low level” with a fair amount of syntax Envirosuite, Mate, etc. do build on top of nesC

43. Mate – Virtual Machine TinyOS Mate VM (interprets) 24 Instruction Programs Each instruction executes as task (slow) Code capsules

44. Code Capsules One capsule = 24 instructions Fits into single TOS packet –Energy efficient Atomic reception Code Capsule –Type and version information –Types: send, receive, timer, subroutine Larger programs – use several capsules

45. Value Re-programmability –Re-task a network Parameter adjustment Self-replication –One of the special instructions on the VM is forward (which supports a viral-like infection of the entire network) Incremental addition of new nodes

46. Propagation Example

47. Node Enters the Network

48. Instruction Set One byte per instruction Three classes: basic, s-type, x-type basic: arithmatic, halting, LED operation s-type: messaging system x-type: pushc, blez  8 instructions reserved for users to define Instruction polymorphism Example: add(data, message, sensing)

49. User Defined Instructions TinyOS Mate VM (interprets) 24 Instruction Programs Code capsules Instructions Understood by Mate User defined 1234 1 3 3 1 - Sound alarm - Write to flash

50. Mate Architecture Stack based architecture Operand stack Return address stack Three events/execution contexts: Clock timer Message reception Message send

51. Code Example(1) Display Counter to LED

52. Code Example(2) Sense and Send

53. TinyOS Sense and Send event result_t Timer.fired() { if (state == IDLE && call Photo.sense()) {state = SENSE;} return SUCCESS; } event result_t Photo.dataReady(uint16_t data) { if (state == SENSE) { packet->reading = data; if (call SendMsg.send(packet, sizeof(DataBuf)) { state = SENDING; } else {state = IDLE;} } return SUCCESS; } event result_t SendMsg.sendDone(TOS_MsgPtr msg) { if (state == SENDING) {state = IDLE;} return SUCCESS; }

54. Component Breakdown 7286 bytes code, 603 bytes RAM

55. Questions What should a programmer see? –New language –New middleware –Services –Components –Abstracts models –All the above – None of the above

56. Questions What would have made your life easier in doing the programming assignments? What makes it easier to get the system to work? –Debugging tools Initially (in programming environment) On real platform

57. Ideas Move code to node – executes on middleware, VM, interpreter Domain specific (good or bad?) Regions/Streams Expose/Hide? –Performance –Physical systems Disseminate

58. Summary Very active area –It must be Solutions can have large impact –Applications programmers rather than system experts Standard? –Analogy to real-time computing and programming languages? WSN-Java?

59. Final Question Will there be GP WSNs (and how might that affect the programming abstractions)?