1. Region Streams Functional Macroprogramming for Sensor Networks Ryan Newton MIT CSAIL newton@mit.edu Matt Welsh Harvard University mdw@eecs.harvard.edu

2. Tracking a vehicle

3. Tracking many vehicles

4. Programming Sensornets is HARD  Message passing is painful  Programming global behavior at the local level  Life at the node level is messy  Low level hardware and communication details  Limited node resources (cpu, memory)  Lost messages and failed nodes

5. Macroprogramming Vision  The programmer thinks in terms of the whole network, manually translates into node programs.  Can some of this translation be automated by a tool or compiler?  An example: TinyDB  Declarative, global-level queries  No message passing or node details  Limited to certain kinds of data gathering

6. CompileDown

7. Regiment Data Model

8. Regiment’s Purpose Provide a rich set of (fully composable) operators to work with data distributed over time and space.

9. Forming Region

10. Mapping an operation

11. Folding a Region

12. Regiment Programming Model  Data Model: Sensor network data represented as streams of tuples, one stream per node.  Regions group streams  Map allows data parallel computation across a region  Fold aggregates the streams in a region

13. Simple and Derived Regions  Simple Regions, created from geometric or radio relationships:  K-hop-neighborhood  K-nearest nodes  All nodes within circle (square, etc)  All nodes in network (“world”)  Derived Regions, built from other regions  Union, Intersection  Map, Filter  (Many other library procedures: border, sparsify, cluster, etc)

14. Relationship to SQL queries SELECT nodeid,light,temp FROM sensors WHERE light > 400 SAMPLE PERIOD 1024 let r1 = filter (\n -> read LIGHT n > 400) world r2 = map (\n -> (read ID n, read LIGHT n, read TEMP n)) r1 in set_freq 1024 r2

15. Tracking a vehicle in Regiment let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) in centroid (filter aboveThresh (map select world))

16. Tracking a vehicle in Regiment let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) in centroid (filter aboveThresh (map select world))

17. Tracking a vehicle in Regiment let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) in centroid (filter aboveThresh (map select world))

18. Tracking a vehicle in Regiment let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) in centroid (filter aboveThresh (map select world))

19. Tracking a vehicle in Regiment let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) in centroid (filter aboveThresh (map select world))

20. Tracking a vehicle in Regiment let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) in centroid (filter aboveThresh (map select world))

21. Tracking a vehicle in Regiment let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) in centroid (filter aboveThresh (map select world))

22. let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) selected = filter aboveThresh (map select world) in centroid selected Multi-target Tracking

23. let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) selected = filter aboveThresh (map select world) globs = cluster selected targets = map centroid globs in targets

24. Tracking with sentries let abovethresh (p,x) = p > threshold select node = (read PROXIMITY node, get_location node) selected = filter aboveThresh (map select world) globs = cluster selected targets = map centroid globs sentries = map read (border world) event = whenAny abovethresh sentries in until event nullArea targets

25. Current Compiler Status  Compiles to Token Machines  Node level state machine exposing “tokens” for region membership  Whole program analysis on token machines enables optimization  Token machines currently run in simulation  Soon will compile to NesC/TinyOS

26. Future work, Research challenges  Finish prototype, evaluate on real motes  Provide effective controls over stream rates and energy usage  Can adaptive runtimes reduce energy usage  Attack specific problem domains to uncover useful domain primitives  Understand general global-to-local compilation and optimization strategies  Preliminary work; would love your feedback!

27. End Ryan Newton MIT CSAIL newton@mit.edu Matt Welsh Harvard University mdw@eecs.harvard.edu Region Streams: Functional Macroprogramming for Sensor Networks

28. With what glue?: The language  General purpose purely functional macroprogramming language  Uses monads, Functional Reactive Programming to encapsulate Regions/Streams safely  Has Conditionals  Has user defined functions  BUT: No arbitrary depth recursion  User defined procedures inline at compile time Control flow is known, programs terminate Thus Regiment has no stack/heap at runtime But, procedures stick around in argument position

29. Lazy (call-by-need) semantics  Regiment is a lazy functional language; values are computed when they’re needed.  If you only use only part of a data structure, compute only that part.  We might not want to compute a region at all places and times. Consider the intersection of contiguous regions  Don’t think of region operations as strict, imperative commands.  Instead, they declare relationships.

30. Compilation Strategy  Safe ADT for Regions and Streams  Only interface through map/fold/filter  Can reason about time/space properties of code with type system  Source level rewrite optimizations based on algebraic properties  Program analysis to recognize efficient communication patterns  analyze region contiguity, area, diameter

31. Token Machines  Token handlers are like a functions whose last call is cached  Can be broadcast to neighbors as well as called locally  Each region has some place(s) from which its formed  Formation and membership tokens Compiler’s place analysis

32. Regions are Streams of Spaces

33. Full Tracking Program let abovethresh (p,x) = p > threshold read node = (read_sensor PROXIMITY node, get_location node) in centroid (afilter aboveThresh (amap read world)) centroid area = divide (afold accum (0,0) area) accum (wsum, xsum) (w,x) = (w + wsum, x*w + xsum) divide stream = smap (\(w,x) -> x/w) stream

34. Core types in Regiment type Stream 'a ≈≈ (Time -> 'a) type Space 'a ≈≈ (Location -> Multiset 'a) type Event 'a ≈≈ (Time, 'a) type Area 'a = Stream (Space 'a) type Region = Area NodeSnapshot type Anchor = Signal NodeSnapshot smap :: ('a -> 'b) -> Signal 'a -> Signal 'b amap :: ('a -> 'b) -> Area 'a -> Area 'b afold :: ('a -> 'b -> 'a) -> 'a -> Area ‘b -> Signal ('a)

35. Anchor Free Localization

37. anchorOptimizing ls = let compare x y = let loop comps = case comps of { [] -> True; -- better break ties somehow h:t | h x y -> true; _:t | loop t } in loop ls in anchorWhere (\_ -> True) compare

38. Anchor Free Localization between a b = (\ x y -> abs (dist_from a x - dist_from b x) > abs (dist_from a y - dist_from b y)) awayfrom a = (\ x y -> dist_from a x > dist_from a y) awayfrom2 a b = (\ x y -> abs (dist_from a x + dist_from b x) > abs (dist_from a y + dist_from b y)) n0 = anchorOptimizing [ ] n1 = anchorOptimizing [awayfrom n0] n2 = anchorOptimizing [awayfrom n1] n3 = anchorOptimizing [between n1 n2, awayfrom2 n1 n2] n4 = anchorOptimizing [between n1 n2, awayfrom n3] n5 = anchorOptimizing [between n1 n2, between n3 n4]

39. Example: Contour finding

40. Example: Threats & Safe path