Macroprogramming

In computer science, macroprogramming is a programming paradigm where programs for the individual components of a distributed system are compiled or interpreted from a macro-program expressed by a system-level perspective or in terms of the intended global goal. The aim of macroprogramming approaches is to support expressing the macroscopic interactive behaviour of a whole distributed system of computing devices or agents in a single program. It has not to be confused with macros, the mechanism often found in programming languages (like C or Scala) to express substitution rules for program pieces.

Macroprogramming originated in the context of wireless sensor network programming and found renewed interest in the context of the Internet of Things and swarm robotics .

Macroprogramming shares similar goals (related to programming a system by a global perspective) with multitier programming, choreographic programming, and aggregate computing.

Examples

ScaFi

The following program in the ScaFi aggregate programming language [1] 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(gradient(src) + gradient(dest) <= distBetween(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 gradient(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 distBetween(source: Boolean, target: Boolean): Double =
    broadcast(source, gradient(target))

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

Regiment

The following program in the Regiment language 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

PyoT

The following program in PyoT 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')

TinyDB

In TinyDB , 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
2 SAMPLE PERIOD 5 minutes

See also

• Multitier programming
• Choreographic programming
• Aggregate computing
• Distributed computing