Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network
Abstract
:1. Introduction
2. Combining Upwind Sprint and GA for CPSL
2.1. Original GA
Algorithm 1. Original GA [20]. |
2.2. Adapting GA for CPSL
Algorithm 2 uGA. |
2.2.1. Behavior Selection Scheme
- (1)
- gld < thl: At least one SN is currently keeping in contact with the plume. In this circumstance, as a cooperation mechanism, the newly detected plume information can be exploited by the SNs.
- (2)
- thl < gld < tha: All n SNs have lost contact with the plume. However, the previously detected information is still time-efficient and can be exploited to recontact the plume.
- (3)
- tha ≤ gld < thm: All n SNs have lost contact with the plume for a long time. It is necessary to explore the uncovered search space for recontacting the plume.
- (4)
- gld equals thm: The CPSL process is considered as failed.
2.2.2. Elementary Behaviors
- D-behavior: The SN moves away from the centroid of the MSN with the perspective of exploring the uncovered space. In the D-behavior, the goal position of the i-th SN with coordinates {xgi, ygi} is
- U-behavior: The SN moves in the opposite direction to that of the detected fluid-flow to search in the upwind area. The upwind movement direction angle can be denoted as θi = π + βi, where βi is the flow direction angle detected by the i-th SN.
- C-behavior: The SN moves toward the new candidate chromosome generated by the GA to evaluate it. Once the C-behavior is performed, a new chromosome is generated by conducting crossover operation on the GA population.
- Initialization (line 8 in Algorithm 2).
- 2.
- Population update (line 10 in Algorithm 2).
- 3.
- Candidate chromosome generation (line 15 in Algorithm 2).
- 4.
- Candidate chromosome evaluation (line 16 in Algorithm 2).
- 5.
- Termination criterion (line 2 in Algorithm 2).
3. Simulation Setup
- The same flow field was reproduced by periodically reading a file in which time-varying flow velocities were previously saved. This setup can help to avoid the influence of different flow fluctuations on the localization processes and results.
- The same plume-finding scheme was used, in which the MSN starts from the same rendezvous (the top-right corner) and departs from it along prefixed angles. In other words, the initial plume-finding stage, which is strongly influenced by the selection of the starting rendezvous, was not studied in these comparison simulations. Because of this setup, the parameter SpiralGap1 in the plume-finding stage of the original SS is omitted. Only the value of SpiralGap2 should be set in the SS simulations. For testing the FPSP, the value of SpiralGap2 was empirically set to 5 m.
- The same termination criterion was used to terminate the methods. The LCP convergence condition in uGA (i.e., the distance between successive LCPs did not exceed 1 meter for 10 LCPs) was also used to terminate SS.
4. Results and Discussion
4.1. Parameter Selection
4.2. Results Comparison
- (1)
- The average spent time cycles of uGA and SS are 1054 and 2041, respectively.
- (2)
- The spent time cycles of uGA are generally smaller than those of SS.
- (3)
- The distribution of the uGA time cycles is close to the normal distribution, whereas those of SS fall into two separate sectors that are far from each other.
- (1)
- The uGA outputs a set of straight-line paths, whereas the SS outputs revolving-shaped paths.
- (2)
- The interactions within the MSN in uGA were more effective than those in SS.
- (3)
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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GA | uGA | |
---|---|---|
Initialization | Random initialization | Initialized around the first plume contact |
Population update | Fitness-value comparison | Moving and detecting |
Selection | Select m × pc chromosomes | Select two chromosomes |
Crossover | Probabilistic crossover | Upwind-customized probabilistic crossover |
Termination | Maximal iteration times | LCP convergence |
Application | Function optimization | Chemical plume source localization |
SS | uGA | |
---|---|---|
Contacting the plume | Upwind sprint | Upwind sprint |
To reacquire the plume | Spiraling | Move to candidate chromosomes |
Cooperation | Simple communications | Customized chromosome crossover |
Termination | Source distance | LCPs convergence |
No. | Experimental Factors | Spent Cycles a | Localization Errors b (m) | |||||
---|---|---|---|---|---|---|---|---|
m | tha | cTy | pm | Average | std. | Average | std. | |
1 | 10 | 20 | 1 | 0.3 | 1166 | 153.96 | 0.948 | 0.0457 |
2 | 10 | 40 | 2 | 0.5 | 1322 | 221.89 | 0.920 | 0.0606 |
3 | 10 | 60 | 3 | 0.8 | 1341 | 385.91 | 0.959 | 0.0455 |
4 | 20 | 20 | 2 | 0.8 | 1027 | 115.76 | 0.932 | 0.0529 |
5 | 20 | 40 | 3 | 0.3 | 1355 | 303.79 | 0.935 | 0.0437 |
6 | 20 | 60 | 1 | 0.5 | 1532 | 127.90 | 0.954 | 0.0587 |
7 | 30 | 20 | 3 | 0.5 | 1119 | 322.25 | 0.941 | 0.0476 |
8 | 30 | 40 | 1 | 0.8 | 1418 | 327.57 | 0.949 | 0.0281 |
9 | 30 | 60 | 2 | 0.3 | 1166 | 153.96 | 0.948 | 0.0457 |
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Cao, M.; Bie, H.; Hu, X. Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network. Appl. Sci. 2022, 12, 5767. https://doi.org/10.3390/app12125767
Cao M, Bie H, Hu X. Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network. Applied Sciences. 2022; 12(12):5767. https://doi.org/10.3390/app12125767
Chicago/Turabian StyleCao, Mengli, Haofan Bie, and Xiong Hu. 2022. "Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network" Applied Sciences 12, no. 12: 5767. https://doi.org/10.3390/app12125767