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Flock Inspired Area Coverage Using Wireless Boid-like Sensor Agents Colin Chibaya Rhodes University Computer Science Department Grahamstown,

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Presentation on theme: "Flock Inspired Area Coverage Using Wireless Boid-like Sensor Agents Colin Chibaya Rhodes University Computer Science Department Grahamstown,"— Presentation transcript:

1 Flock Inspired Area Coverage Using Wireless Boid-like Sensor Agents Colin Chibaya c.chibaya@ru.ac.za Rhodes University Computer Science Department Grahamstown, South Africa Shaun Bangay s.bangay@ru.ac.za Rhodes University Computer Science Department Grahamstown, South Africa

2 Outline 2 of 18

3 Background Wireless Sensor NetworksBeacon/landmark based Require memory - Movement history -positional info (beacon) Geometry Require complex agents with powerful computational capabilities 3 of 18 BackgroundBackground The goal Movie Routine Experiment Results Conclusion contributions

4 Background BackgroundBackground The goal Movie Routine Experiment Results Conclusion contributions 4 of 18 Reynolds’ simulated flocks Separation Move to avoid overcrowding Cohesion Attempt to stay close to neighbours Alignment Move towards the average direction of flock mates

5 The goal Flock inspired area coverage model Perching Wireless sensor networks Reynolds’ flocking rules 5 of 18 BackgroundBackground The goalThe goal Movie control routine Experiment Results Conclusion contributions alignment Beacons, geometry, landmarks

6 Exemplary scenario Free neighbouring locations Free locations in sensing range The mean free path 6 of 18 BackgroundBackground The goalThe goal MovieMovie Control routineControl routine Experiment Results Conclusion contributions

7 Control routine-1 Mode separation FOR_EVERY agent X at location L i IF (cardinality of L i > 1) THEN IF (exist free neighbouring spaces (L * )) THEN X move to L * ELSE IF (free spaces, L ** are in sensing range) THEN X move to L * that is closest to L ** ELSE X uses the mean free path ELSE Mode cohesion 7 of 18 BackgroundBackground The goalThe goal MovieMovie Control routineControl routine Experiment Results Conclusion contributions

8 Control routine-2 Mode cohesion FOR_EVERY agent X at location L i IF (exists a neighbouring location L * whose cardinality > 0) THEN mode perch ELSE IF (exist some covered locations in sensing range) THEN X moves to L* closest to the covered locations ELSE X uses the mean free path 8 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine Experiment Results Conclusion contributions

9 Experiment 1: Separation speed How we measure separation speed – -number of agents that are successfully perched within a specific time in simulation Centrally placed values – 10 simulations are averaged Results – Compared against a random guess. 9 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment Results Conclusion contributions

10 Result 1: Separation speed Fig 1: comparison of separation speedFindings – 50.57% of agents were perched in 31 iterations, compared to only 19.14% using a random guess – Our model achieved complete coverage in 135 iterations when a random guessing model was at 65.66% 10 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment ResultsResults Conclusion contributions Perched agents Time in simulation

11 Experiment 2: Cohesion speed How we determine cohesion speed – Count iterations from isolation until cohesion Procedure – Allow coverage to occur in some continuous space – Deploy an isolated agent – Record the iterations – Repeat for 1000 times Results – Compare with random guessing 11 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment Results Conclusion contributions

12 Result 2: Cohesion speed Table 1 ModelGuess Mean steps1639 Standard deviation314 Entropy levels0.5%41.2% Findings – Agents achieved cohesion in 16 ±3 steps, compared to 39 ±14 steps using random guessing – Chances that agents fail to perch are 0.5% in our model and 42.2% in random guess model 12 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment ResultsResults Conclusion contributions

13 Experiment 3: Coverage quality Metrics of importance – Fraction of area covered at any time slot – Time it takes to achieve complete coverage Measured in iterations Results – Compared with results achieved 13 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment Results Conclusion contributions

14 Result 3: Coverage quality Figure 2 : Coverage qualityFindings Our model achieved complete coverage in 135 iterations Random guess achieved a maximum of 65.66% 14 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment ResultsResults Conclusion contributions

15 Experiment 4: Fault tolerance Purpose of experiment – Model performance where agents may fail How we conducted the experiment – Allow coverage to occur – Kill 40 agents Results – Compare with a random guess 15 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment Results Conclusion contributions

16 Result 4: Fault tolerance Our model self-repaired to 94.35% coverage quality in 34 iterations Agents that used the random guessing model could not re- organize. 16 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment ResultsResults Conclusion contributions

17 Conclusions We proposed an area coverage model inspired by Reynolds’ flocking algorithms. The model exhibits good separation speed and cohesion properties of the flocking algorithm The model is fault tolerant and adaptive to agents’ failures The model is fast, achieving high quality coverage in a relatively short period of time 17 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment ResultsResults ConclusionConclusion contributions

18 Contributions We presented a novel sensor agents control model using simulated flocking rules We devised and evaluated a plausible strategy for determining coverage quality as well as fault tolerance This work provides a new way of measuring the performance of agent based coverage models 18 of 18 LiteratureLiterature The goalThe goal MovieMovie Control routineControl routine ExperimentExperiment ResultsResults ConclusionConclusion contributionscontributions

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