Process algebras in Quality of Information research

1. Process algebras in Quality of Information research Toward an event detection calculus

2. Quality of information • In a given military scenario information is imperfect and the ground truth is represented by a probability distribution over the system states. • The information that can derived from the sensor network is also represented by a distribution over the same space, but taking into account sensor and network characteristics. • Quality of Information must embody the difference between these two distributions.

3. Abstraction and Stochastics • Practical modelling requires some simplification • Abstraction using stochastic descriptions allows controlled removal of detail. • e.g. A network communication protocol can be represented by a single exchange at a stochastic rate rather than the complete packet level description • Stochastic process algebras provide the basis for formal reasoning about, and quantitative evaluation of, such models.

4. Process algebras • Formally represent activities and interactions • Provide inputs to tools which calculate measures of probability, duration and feasibility • PEPA has a strong armoury of specifically designed solution tools, and translators to other modelling formalisms • This is an excellent time to be approaching this work: • Momentum in the SPA community is expanding from academic contemplation of expressiveness into solving concrete problems

5. Plug and play modelling

6. Small Example • Zone A • Stationed ally, at ease or alert • Sensor, which detects target • Network leaf, which receives packets from the wider network • Zone X • Sensor and network node • Each has a dynamic acoustic environment which may mask the target, or cause false detection • Mobile target, moves between A and X and may be detected acoustically while active • Sensors, network, environments and clients are designed to be “plug-and-play”, • e.g. acoustic (passive) or radar (active) sensor

7. PEPA fragments (1/2) Acoustic sensor: Acoustic_sensor_asleep = (wake, acoustic_sensor_wake_rate).Acoustic_sensor_awake; Acoustic_sensor_awake = (hear, infty).Acoustic_sensor_sending + (acoustic_sensor_sleep,acoustic_sensor_sleep_rate).Acoustic_sensor_asleep; Acoustic_sensor_sending = (data,acoustic_data_rate).Acoustic_sensor_awake;

8. PEPA fragments (2/2) Zone X: ZoneX = ( IdZoneX[_] <> (Target[Target_inactive] <reflect> Passives_pad[_]) <hear, reflect> ( ( Acoustic_sensor_asleep <data> Network_node ) <dataXAprep,dataXBprep> (PacketXA[PacketXA]) %<>PacketXB[PacketXB]) ) );

9. Events as State Transitions • An event corresponds to a state transition in our models. • Detecting the event requires recognition of entry into an appropriate destination state • Exposed: • A target is present and active • When did it arrive? • In Danger: • Target is present and active, but ally believes it to be elsewhere • How do we construct that belief to satisfy safety and efficiency? • Wasteful: • Sensor is consuming power, but the target is not in range • Should we change policy?

10. Parameter exploration The next stage of development is to extend the model to analyse outcome distributions

11. Stochastic model investigation