autonomic control for wireless sensor network surveillance applications

Autonomic Control for Wireless Sensor Network Surveillance Applications

Presented by: Darminder Singh Ghataoura

This work is supported by:

Contents

• Introduction• Enabling an Autonomic Capability • Autonomic Transmission Control• Autonomic Transmission Control Strategies• Simulation Results• Summary• Questions?

Introduction

– Unattended Ground Sensor (UGS) Networks for surveillance are classified as distributed systems.

– Why?: Increases Tactical reach for mission planners, primarily because of its scalability property

– They are deployed within a security-sensitive region to support surveillance capabilities such as:

– Threat Presence Detection (e.g. Mitigating on False Alarms, Providing Detection Information)

– Threat Geo-location (e.g. Current Threat Location (x, y) coordinates)

– Threat Classification and Tracking

– The distributed nature of operation however presents challenges for application support protocol development since:

– Surveillance operations are dynamic, threat situation can be constantly changing

– UGS devices have a limited life-span dictated by their battery energy reserves

Introduction

– Our overall objective is on managing network resource consumption, primarily communication energy and bandwidth.

– Why? : Promoting the operational longevity goal of the deployed UGS network field.

– Potentially we can achieve this goal through a self-managed (autonomic) control implementation, incorporating awareness towards a current threat situation.

– How?: Incorporating a Situation Awareness (SA) methodology. An integrated three level approach for enabling distributed UGS surveillance management.

• Additional Info: D.S.Ghataoura, J.Mitchell, G.E.Matich, “Networking and Application Interface Technology for Wireless Sensor Network Surveillance and Monitoring”, IEEE Communications Magazine, vol.49, no.10, pp.90-97, October 2011.

Enabling an Autonomic Capability

State of the Environment

Decision Making

Performance of Actions including Sensor Queuing

Level 1

PerceptionLevel 2

Comprehension

Level 3

Projection

SITUATION AWARENESS (SA)

Projection of future states of the

operational/sensing environment

Understanding the significance associated with raw sensor data (joining the dots !)

Situation AssessmentPresence of threat as

well as combined characteristics

(Accuracy, Certainty, Timeliness)

Autonomic Transmission Control

– Conserving on network resource consumption requires autonomic transmission control and can be facilitated through SA level 3 operation.

– Method ? : Firstly, a framework for transmission control management is needed, to project future states of the operational/sensing environment

– Method ? : Secondly, strategies to adjust when transmission control decisions should be made within the level 3 framework, according to the monitored threat environment

– Transmission control therefore becomes an application-orientated approach through applying feedback on temporal environmental dynamics.

– Advantage?: Yes, this can help to maintain relevant surveillance information utility and prevent UGSs continually sending their information, during periods of low surveillance activity.


– Partially Observable Markov Decision Process (POMDP)

– Applying autonomic transmission control requires an ability for UGSs to comprehend their surveillance surroundings (“Context Awareness”)

Surveillance Environment (STATE)

Belief State Estimator (BSE)

“Context” Evaluation

Current Observation Current Belief State

EstimateCurrent Action

Sensor “Context Aware” Dynamic Transmission Management Controller

Transmission Control Selection,

Scheduler and Prioritisation


– Projecting the POMDP for future states (Mission Objective “Context”)

– At each decision epoch (discrete point in time) signifies an evaluation of the state “context”, given past experiences, initiating a transmission control response.

STATEK STATEK+1 STATEK+2

Transmission Control

BSEK BSEK+1

Current Observation zK Current Observation zK+1

Action aK Action aK+1

BSEK-1

Action aK-1


Current Observation zK+2

TK-1 (Decision Epoch) TK (Decision Epoch) TK+1 (Decision Epoch)

Mission Objective Partial Observable Belief State

(Threat Presence/Location“Context”)


Autonomic Transmission Control Strategies

– Autonomic transmission control decisions using the POMDP are undertaken at specific decision epoch intervals at a pre-determined observation frequency.

– Disadvantage?: Yes, the observation frequency ignores the dynamic characteristics of the monitored threat

– Disadvantage?: Yes, Non-Adaption to threat characteristics encourages unnecessary transmission control decisions to occur

• Why is decision epoch interval adaption needed?

– For example, a threat moving at a constant velocity or not changing direction frequently (low threat dynamics), would imply setting a larger decision epoch interval, in order to save on network resource consumption.

– Providing control strategies for when decision epochs should occur is primarily aimed at making further savings to network resource consumption.


POMDP Decision Epoch Control Strategy Formulation (For Single Sensor Types)

• To formulate the decision epoch control process, we employ a time frame window, in which characteristics concerning the monitored threat are observed:

– A total of l threat characteristic observations are made within a designated time frame window (Tj) in seconds.

– Observations at each time interval (T^j)

n, equal to 1 / l seconds, where n = 0, at the start of an observation time frame, with condition, n < l.

Tj ( l threat characteristic observations)

(T^j)

n

Epoch Interval ( (∆DEj)Previous + ∆DEj) ∆DEj


POMDP Epoch Control Strategy 1 : Threat Position

• Location Metadata (LM) of the last l observations taken within Tj, concerning the monitored threat is modeled, as shown:

→

• dj denotes the net distance travelled by the monitored threat, during the last l observations, as shown :

• The total distance travelled by the threat during the last l observations, dtj, forming the threat position observation history within Tj, is:


POMDP Epoch Control Strategy 2 : Mission Objective “Context”

• Probabilistic confidence measures for presence (P1j) and geo-location (P2j) of threat, derivations (SA-level 2) , allow specific “context” ratios (CR), as shown:

• Using min and max functions in this instance, determines the degree of “contextual” variation, present within Tj.

• ∆DEj, is assumed to be a linear function of CR, and can be calculated, for each specific mission objective, M1 (Threat Presence), M2 (Threat Geo-location) as shown:

Autonomic Transmission Control StrategiesPOMDP Epoch Control Strategy 3 : Similarity in “Context”

• We define Sim (Tj) = cos(α), reflecting the similarity “context” measurement between B and X.

• ∆DEj is assumed to be a linear function of Sim (Tj), ∆DEj = f (Sim (Tj)), as shown:

P1j n=0

B (Threat Presence)

X (Threat Geo-location)

P1j n=1

P2j n=0

P2j n=1

α (T^

j) n=0

(T^j)

n=1

Similarity “Context”

Simulation Setup

• Simulations are conducted for random deployments using a total of 10 sensors• Deployed in a 1km by 1km surveillance region.• Sensing Range -1000m and Transmission Range - 500m• Simulations are run for duration of 1000 threat observations, for each v(max).

Random Waypoint Model, v (max) Intruder

Acoustic Sensor

Seismic Sensor

Command Centre

3

3

1

2

1

2

Simulation ResultsCommunication Energy Consumption Performance

10 15 20 25 30 35 40 45 50 550

50

100

150

200

250

300

350

Strategy 1 Strategy 2 Strategy 3 POMDP - Non-Adaption IDSQ

Threat Velocity -vMAX (m/s)

Ave

rage

Net

wor

k E

nerg

y

C

onsu

mpt

ion

(Jo

ules

)

• Adapting decision epoch interval selection improves communication energy consumption• Being “context aware” conserves on energy when compared with non-”context aware” (IDSQ)

Simulation ResultsNetwork Latency Performance

10 15 20 25 30 35 40 45 50 550.600000000000001

0.700000000000001

0.800000000000001

0.900000000000001

1

1.1

1.2

Strategy 1 Strategy 2 Strategy 3 POMDP- Non-Adaption


Lat

ency

(m

sec)

• Adapting decision epoch interval selection improves Latency (Bandwidth Efficiency)• Being “context aware” improves Latency when compared with non-”context aware” (IDSQ)

Simulation ResultsThreat Presence Detection Performance

10 15 20 25 30 35 40 45 50 5560

65

70

75

80

85

90

95

Strategy 1 Strategy 2 Strategy 3 POMDP- Non-Adaption IDSQ


QoS

I (%

)

• Adapting decision epoch interval selection incurs a minimal loss in QoSI performance• Being “context aware” improves the QoSI % when compared with non-”context aware” (IDSQ)

Simulation ResultsThreat Geo-Location Performance

10 15 20 25 30 35 40 45 50 552

2.5

3

3.5

4

4.5

5

Strategy 1 Strategy 2 Strategy 3 Non-Adaption IDSQThreat Velocity -vMAX (m/s)

CE

P (

met

res)

• Adapting decision epoch interval selection improves CEP performance (Strategy1)

Summary

– Presentation has proposed an autonomic control capability:

– This is provided using a situation awareness (SA) methodology– SA level 3 can provide us a capability for UGSs to better manage their network resources efficiently

– Presentation then focused on autonomic transmission control:

– This can be achieved using a POMDP framework– Transmission control is invoked through being “context-aware”- UGSs taking an evaluation of their

common surveillance environment– Transmission control decisions however are typically taken at a defined non-adaptive frequency

interval

– Presentation then followed by looking at autonomic transmission control strategies:

– Adapting the POMDP decision epoch interval according to threat dynamic characteristics– Strategy 1: Threat Position– Strategy 2: Mission Objective (Task) “Context”– Strategy 3: Similarity in Mission Objective “Context”

Summary

– Adapting the POMDP decision epoch interval encourages:

– Better management of network resource consumption (Communication Energy and Bandwidth)

– A minimal loss in threat detection performance

– Fully distributed operation, no need for centralized control, through UGSs being “context-aware”

– Which strategy to adopt?

– Results suggest strategy 3 improves on communication energy and threat detection performance over strategy 1 and 2.

– Strategy 1 improves on threat geo-location performance over strategy 2 and 3.

– Hybrid strategy approach? Strategy 3 for Threat Detection and Strategy 1 for Threat Geo-location?

– Further results needed to gauge system performance.

QUESTIONS?

autonomic control for wireless sensor network surveillance applications

Technology

transmission control

autonomic capability

current threat situation

decision epochstatek

threat presence detection

monitored threat disadvantage

low threat dynamics

network consumption