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Autonomic Control for Wireless Sensor Network Surveillance Applications
- 1. Autonomic Control for Wireless Sensor Network Surveillance Applications Presented by: Darminder Singh Ghataoura This work is supported by:
- 2. Contents • • • • • • • Introduction Enabling an Autonomic Capability Autonomic Transmission Control Autonomic Transmission Control Strategies Simulation Results Summary Questions?
- 3. 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
- 4. Introduction – Our overall objective is on managing network consumption, primarily communication energy and bandwidth. resource – 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.
- 5. Enabling an Autonomic Capability State of the Environment SITUATION AWARENESS (SA) Level 1 Perception Level 2 Level 3 Comprehension Situation Assessment Presence of threat as well as combined characteristics (Accuracy, Certainty, Timeliness) Projection Understanding the significance associated with raw sensor data (joining the dots !) Decision Making Performance of Actions including Sensor Queuing Projection of future states of the operational/sensing environment
- 6. 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.
- 7. Autonomic Transmission Control – Partially Observable Markov Decision Process (POMDP) Surveillance Environment (STATE) Current Observation Belief State Estimator (BSE) “Context” Evaluation Current Belief State Estimate Transmission Control Selection, Scheduler and Prioritisation Current Action Sensor “Context Aware” Dynamic Transmission Management Controller – Applying autonomic transmission control requires an ability for UGSs to comprehend their surveillance surroundings (“Context Awareness”)
- 8. Autonomic Transmission Control – Projecting the POMDP for future states (Mission Objective “Context”) Mission Objective Partial Observable Belief State (Threat Presence/Location “Context”) Current Observation zK Current Observation zK+1 STATEK BSEK-1 Current Observation zK+2 STATEK+1 BSEK Action aK-1 Transmission Control TK-1 (Decision Epoch) STATEK+2 BSEK+1 Action aK Transmission Control TK (Decision Epoch) Action aK+1 Transmission Control TK+1 (Decision Epoch) – At each decision epoch (discrete point in time) signifies an evaluation of the state “context”, given past experiences, initiating a transmission control response.
- 9. 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.
- 10. Autonomic Transmission Control Strategies 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) ∆DEj (T^j) n Epoch Interval ( (∆DEj)Previous + ∆DEj)
- 11. Autonomic Transmission Control Strategies
- 12. Autonomic Transmission Control Strategies
- 13. Autonomic Transmission Control Strategies (T^j) n=1 B (Threat Presence) P1j n=1 Similarity “Context” X (Threat Geo-location) P2j n=1 α P1j n=0 P2j n=0 (T^j) n=0
- 14. Simulation Setup Random Waypoint Model, v (max) Command Centre Intruder 2 Seismic Sensor 2 3 1 Acoustic Sensor • • • • 3 1 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).
- 15. Simulation Results Communication Energy Consumption Performance • • Adapting decision epoch interval selection improves communication energy consumption Being “context aware” conserves on energy when compared with non-”context aware” (IDSQ) Average Network Energy Consumption (Joules) 350 300 250 200 150 100 50 0 10 15 20 25 30 35 40 45 50 55 Threat Velocity -vMAX (m/s) Strategy 1 Strategy 2 Strategy 3 POMDP - Non-Adaption IDSQ
- 16. Simulation Results Network Latency Performance • • Adapting decision epoch interval selection improves Latency (Bandwidth Efficiency) Being “context aware” improves Latency when compared with non-”context aware” (IDSQ) Latency (msec) 1.2 1.1 1 0.9 0.8 0.7 0.6 10 15 20 25 30 35 40 45 50 55 Threat Velocity -vMAX (m/s) Strategy 1 Strategy 2 Strategy 3 POMDP- Non-Adaption IDSQ
- 17. Simulation Results Threat Presence Detection Performance • • 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) 95 90 QoSI (%) 85 80 75 70 65 60 10 15 20 25 30 35 40 45 50 55 Threat Velocity -vMAX (m/s) Strategy 1 Strategy 2 Strategy 3 POMDP- Non-Adaption IDSQ
- 18. Simulation Results Threat Geo-Location Performance • Adapting decision epoch interval selection improves CEP performance (Strategy1) 5 CEP (metres) 4.5 4 3.5 3 2.5 2 10 Strategy 1 15 20 Strategy 2 25 30 35 40 Threat Velocity -vMAX (m/s) Strategy 3 45 Non-Adaption 50 55 IDSQ
- 19. 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”
- 20. 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 “contextaware” – 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 Geolocation? – Further results needed to gauge system performance.
- 21. QUESTIONS?
Editor's Notes
- Direct the general picture to theaudience
- The general outline of our Presentation.Direct Audience to our IEEE COMMS article explaining the nature of distributed UGS surveillance, operational requirements and constraints, our methodology.
- This slide presents the relevant parts of situation awareness in terms of level 1 , 2 and 3Mention Level 1, for mitigating against false alarms, threat presence characterisationMention level 2, can be achieved using Bayesian Belief Network (BBN) TechniquesMention in how level 3 can promote the management of a particular sensor task (e.g. Transmission decision making)
- Since our main objective is to conserve on network resources, we need transmission control ability.This can be implemented using SA level 3 (i.e. Projecting the future states of the Surveillance Environment)The ability to project/evaluate the future states of the sensing environment allows us take feedback on temporal environmental dynamics.Subsequently we can manage our transmission activity according to the dynamics associated with the monitored threat.The level 3 framework can be formalised using a POMDP (NEXT SLIDE)
- A partially observable view, indicates an incomplete perspective regarding the surveillance environment (STATE)A partial view requires us to take feedback control of previous Transmission actions and observations.The Belief State Estimator (BSE) enables this.The BSE represents the most probable view of the surveillance environment given past experiences.Through “context” awareness the transmission control decisions evolve randomly over time in response to the Belief State Estimation (BSE)Partial Observability is a conservative approach to transmission control however has advantages for being entirely distributed , since current observations are taken locally (at UGS level) and do not rely on any further control/observation updates from the network.
- Mission Objective implies the surveillance operation, which the deployed UGS network has been tasked to do.Evaluating state “context” ensures the dynamics of the monitored threat are considered, at specific points in time and upon which a appropriate transmission control decision is taken.
- This slide highlights using a POMDP at a fixed observation frequency, ignores the dynamic characteristics of the threat.This can lead to unneccessary transmission control decisions to occur.Explain ExampleExplain Why decision epoch interval selection/adaption is required.
- Transmission Control Strategy Formulation(∆DEj)Previousdenotes the decision epoch interval calculated in the previous designated time frame window (Tj).A current decision epoch interval (∆DEj) is evaluated and scheduled at the end of each (Tj).
- Location Metadata (LM) dictated by dtj, is equal to unity if the threat moves in a completely uniform manner and is < 1, if threat dynamics change in a non-uniform manner. Therefore, LM is bounded by the interval (0 < LM <= 1), within (Tj). For strategy 1, ∆DEj, is assumed to be a linear function of LM, ∆DEj = f (LM (Tj))
- min and max are functions calculating the final minimum and maximum confidence measures obtained, within Tj.If there is low “Context” variation present, this would imply a higher CR being set. CR, is therefore bounded by the interval (0 < CR <= 1), within Tj.
- Similarly we can use the probabilistic confidence measures (FROM PREVIOUS SLIDE) to determine the degree of similarity between M1 (Threat Presence Mission Objective) and M2 (Threat Geo-Location Mission Objective)The degree in similarity through vector analysis is described, through angle αValue of α, Cos(α), is in fact an accuracy description of correlations (similarity) between two types of “context”, in view of the current mission objective surveillance environment. Similarity “context” varies according to cos (α). This being 0 when B and X are orthogonal (LOW SIMILARITY - Large “context” magnitude) and 1 when Band X are identical or proportional (HIGH SIMILARITY - small “context” magnitude).
- Sensing ranges set at 1000m, Transmission ranges 500mIEEE 802.11 basic access mode is selected as the MAC protocol, under lossless channel conditions.Threat Mobility is set using the Random Waypoint Model, where v(max) signifying the random velocity set.Simulations are run for duration of 1000 threat observations, for each v(max).Information-driven sensor querying (IDSQ), selects a sensor at each time step based on maximizing the measured information utility and minimizing the communication cost. IDSQ also has limitations in using uniform fixed time intervals for decision making, since their inability to account for a changing environment or threat maneuvering, can either introduce tracking errors or increase network resource consumption.
- Being “context aware” conserves on energy when compared with “non-context” aware (i.e. IDSQ).Explain through providing adaption to decision epoch interval selection, we conserve on communication energy consumption. (AVOID unnecessary transmission control decision making points)In terms of increase in speed (at the High end), communication activity picks up for POMDP and strategy 2.Strategy 1 and 2 provide a more consistent performance across threat velocity.
- Latency is a measure in the delay in receiving a message. (THEREFORE MEASURING BANDWIDTH EFFICIENCY)Being “context aware” (strategy 1, 2, 3 and POMDP) through SA conserves on LATENCY when compared with “non-context” aware (i.e. IDSQ).As we can see, Providing decision epoch interval adaption according to threat dynamic characteristics improves on bandwidth efficiency.Adaption Limits the number of messages being sent, channel becomes less busy, therefore making it easier for accessing the channel medium. (Lower channel access delay) All strategies provide approximately the same level in LATENCY performance.
- Threat Presence Detection Utility is measured in terms of QoSI.QoSI is measured in terms of an aggregated detection certainty, accuracy and timeliness score.While Adaption provides network resource consumption benefits, we also want to ensure minimal loss in mission objective surveillance utility.Being “context-aware” encourages better QoSI performance over “non-context” aware (IDSQ)
- Threat Geo-location utility is measured in terms of CIRCULAR ERROR PROBABLE (CEP) Defined as a the radius of the circle that has its centre at the true threat position and measuring at a % accuracy the position uncertainties associated with the threat location estimate.A lower CEP implies better geo-location performance.Geo-location performance improves using strategies 1,2,3 at higher threat velocities