Macroprogramming

The following program in the ScaFi aggregate programming language [7] defines the loop control logic needed to compute a channel (a Boolean field where the devices yielding true are those connecting, through a hop-by-hop path, a source device to a target device) across a large set of situated devices interacting with neighbours.

class SelfContainedChannel extends AggregateProgram with SensorDefinitions {
  def isObstacle = sense[Boolean]("obstacle")
  def isSource = sense[Boolean]("source")
  def isDestination = sense[Boolean]("target")
  
  override def main(): Boolean = 
  branch(isObstacle){ false }{ channel(isSource, isDestination, 5) }

  def channel(src: Boolean, dest: Boolean, width: Double): Boolean =
    dilate(distanceTo(src) + distanceTo(dest) <= distanceBetween(src,dest), width)

  type OB[T] = Builtins.Bounded[T]
  def G[V:OB](src: Boolean, field: V, acc: V=>V, metric: =>Double): V =
    rep( (Double.MaxValue, field) ){ dv =>
      mux(src) { (0.0, field) } {
        minHoodPlus {
          val (d, v) = nbr { (dv._1, dv._2) }
          (d + metric, acc(v))
        } } }._2

  def distanceTo(source: Boolean): Double =
    G[Double](source, 0, _ + nbrRange(), nbrRange())

  def broadcast[V:OB](source: Boolean, field: V): V =
    G[V](source, field, x=>x, nbrRange())

  def distanceBetween(source: Boolean, target: Boolean): Double =
    broadcast(source, gradient(target))

  def dilate(region: Boolean, width: Double): Boolean =
    gradient(region) < width
}

What is interesting to note is that the channel function, as well as the functions that are used to implement it, namely distanceTo, distanceBetween, dilate, broadcast etc. can be interpreted not just in terms of the individual behaviour of a device, but rather by a macroscopic perspective. In fact, for instance, distanceTo(s) is used to compute the field of minimum distances from the closest device for which expression s yields true: this is effectively a distributed data structure that is sustained through processing and communication with neighbours, in a self-organising way. Semantically, such functions define a macro-level (or collective) behaviour that yields a macro-level (or collective) data structure. Such macro-level functions/behaviours can be composed together to obtain another more complex macro-level function/behaviours.

The following program in the Regiment language [3] can be used to compute the mean temperature perceived by the whole system:

% function definition
doSum :: float (float, int) -> (float, int);
doSum(temperature, (sum, count)) { (sum+temperature, count+1) }

% functional reactive program logic
temperatureRegion = rmap(fun(node){ sense("temperature", node) }, world);
sumSignal = rfold(doSum, (0.0, 0), temperatureRegion)
avgSignal = smap(fun((sum,count)){ sum / count }, sumSignal)

BASE <- avgSignal % move such information to the base station

The following program in PyoT [8] can be used to turn on a fan if the mean temperature computed by several sensors exceeds a certain threshold.

temperatures = Resource.objects.filter(title="temp")
results = [temp.GET() for temp in temperatures]
avg = sum(results) / len(results)
TEMP_THRESHOLD = 24
if avg > TEMP_THRESHOLD:
    Resource.objects.get(title="fan").PUT("on")

In TinyDB,[9] a data-oriented macroprogramming approach is used where the programmer writes a query which turns into single-node operations and routing in a wireless sensor network.

SELECT nodeId , temperature WHERE temperature > k FROM sensors SAMPLE PERIOD 5 minutes