Region Streams Functional Macroprogramming for Sensor Networks Ryan Newton MIT CSAIL Matt Welsh Harvard University Tracking a vehicle Tracking many vehicles 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 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 CompileDown Regiment Data Model Regiments Purpose Provide a rich set of (fully composable) operators to work with data distributed over time and space. Forming Region Mapping an operation Folding a Region 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 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) 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 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)) 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)) 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)) 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)) 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)) 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)) 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)) 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 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 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 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 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! End Ryan Newton MIT CSAIL Matt Welsh Harvard University Region Streams: Functional Macroprogramming for Sensor Networks 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 Lazy (call-by-need) semantics Regiment is a lazy functional language; values are computed when theyre 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 Dont think of region operations as strict, imperative commands. Instead, they declare relationships. 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 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 Compilers place analysis Regions are Streams of Spaces 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 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) Anchor Free Localization 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 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] Example: Contour finding Example: Threats & Safe path