1. International Technology Alliance In Network & Information Sciences International Technology Alliance In Network & Information Sciences 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] Passives_pad[_]) ( Acoustic_sensor_asleep Network_node ) (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

12. Distributions Conservative, dangerous, wrong, missing one or more factors Begin with a simple example: –Where is an object of interest? –Probability of presence in two zones –Sensor in each zone with simple network –Why are we interested? Asset in one of the zones may be threatened Predict level of threat i.t.o. probability of presence of aggressor This slide needs re-thinking

13. Meta-data semantics We would like a measure of the difference between –True outcome distributions –Distributions indicated by available information Meta-data from sensors is a natural idea So, synthesize sensor metadata semantics