International Technology Alliance
In Network & Information Sciences
Process algebras in Quality of
Information research
Toward an event detection calculus
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.
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.
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
Plug and play modelling
OBJECTS
EN
T
M
IR
ON
EN
V
CLIENT
SENSOR
NETWORK
MOBILITY
INTERFACE
DATA
AND
INFORMATION
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
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;
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])
)
);
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?
Parameter exploration
Arbitrary parameter sweep example
0.5
waste
exposure
danger
Instantaneous probability
0.45
0.4
0.35
0.3
0.25
0.2
0
5
10
15
20
25
30
35
Sensor sleep rate
The next stage of development is to extend the model to
analyse outcome distributions
Stochastic model investigation
Quality of information
speaks to the
relationship between
fact and information
PARAMETERS
SENSORS
OUTCOMES
(FACT)
SCENARIO
INFORMATION
INFERERENCE MODEL
e.g.
FACT
INFO
optimistic
pessimistic
wrong
under-specified