Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet
Abstract
:1. Introduction
1.1. Related Work
1.2. Our Contribution
2. System Architecture
2.1. System Model
- building a regional coverage model;
- determining the least number of drones needed for coverage in a given area;
- finding optimal drones’ 3D-placement locations to save communication energy;
- proposing a strategy to prolong the entire drone fleet’s lifetime.
2.2. Energy Model
2.2.1. Flight Mode
2.2.2. Calculation Mode
2.2.3. Communication Mode
3. Research Methods
3.1. Terrain Simplification
- transfer height map H into a 3D binary volume array ;
- extract the isosurface of the volumetric array V and store the faces and vertices information as ;
- apply a mesh decimation algorithm to simplify the surface and obtain faces and vertices as .
Algorithm 1: Terrain Simplification: Calculate |
Require: |
Ensure: |
1: |
2: for to a do |
3: for to a do |
4: for to |
5: |
6: end for |
7: end for |
8: end for |
9: |
10: |
3.2. Area Division
Algorithm 2: Area Division: Calculate |
Require: |
Ensure: |
1: |
2: |
3: while do |
4: |
5: T |
6: |
7: repeat |
8: |
9: |
10: for do |
11: if and then |
12: |
13: end if |
14: end for |
15: for do |
16: for to do |
17: |
18: |
19: for do |
20: if then |
21: |
22: end if |
23: end for |
24: |
25: if and and then |
26: |
27: end if |
28: end for |
29: if then |
30: |
31: end if |
32: end for |
33: |
34: until |
35: |
36: end while |
3.3. Placement
- initialize each sub-area’s drone location with a random element of ;
- for each sub-area , find the best location within that minimizes the total distance from drone to where ;
- compare the new drone coordinate set with previous ones;
- if the differences are greater than a predefined threshold, repeat steps 3 and 4. Otherwise, return current drone fleet coordinates.
Algorithm 3: Placement: Calculate |
Require: |
Ensure: |
1: for to n do |
2: for do |
3: for to do |
4: |
5: |
6: for do |
7: if then |
8: |
9: end if |
10: end for |
11: |
12: if and and then |
13: if |
14: end if |
15: end for |
16: end for |
17: end for |
18: for to n do |
19: |
20: end for |
21: |
22: |
23: while do |
24: for n do |
25: |
26: for do |
27: |
28: if then |
29: |
30: |
31: end if |
32: end for |
33: end for |
34: |
35: |
36: end while |
3.4. Dynamic Adjustment
Algorithm 4: Routing Table: Calculate |
Require: |
Ensure: |
1: for n do |
2: for do n do |
3: |
4: |
5: end for |
6: end for |
7: for to n do |
8: |
9: |
10: |
11: while do |
12: for do |
13: if then |
14: |
15: |
16: end if |
17: end for |
18: j |
19: |
20: |
21: end while |
22: end for |
Algorithm 5: Positions Dynamic Switch: Calculate |
Require: |
Ensure: |
1: while do |
2: update() |
3: if then |
4: |
5: |
6: lowest battery drone |
7: if then |
8: Calculate SwitchMoveCost(ID,j) |
9: Initiate switch protocol |
10: if request accepted then |
11: Inform next highest battery drone |
12: Broadcast has been switched |
13: end if |
14: end if |
15: end if |
16: end while |
17: Current time |
4. Results Evaluation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Values | Parameters | Values |
---|---|---|---|
Field side length a | 3000 m | Drone maximum moving power | 4 W |
Horizontal FOV | 120 | Drone maximum speed | 25 m s |
Resolution requirements | 1 pixel m | Drone processing power | 1 W |
Drone’s mass m | 800 g | Transceiver circuitry energy consumption | 50 nJ bit |
Number of propellers n | 4 | Transmitter amplifier energy consumption | 100 pJ bit m |
Propeller’s radius r | 12 cm | Drone initial energy storage | 34,632 J |
Gravitational acceleration g | 9.8 m s | Communication rate within drones | 49.8 Mbit s |
Air density | 1.225 kg m | Drone switching overheads power | 1 W |
Drone Location | Mountain | Valley | Complex |
---|---|---|---|
(698, 448, 1245) | (778, 425, 1127) | (751, 516, 1074) | |
(2174, 515, 1134) | (2210, 409, 1200) | (2144, 500, 1132) | |
(809, 1410, 1031) | (691, 1180, 1125) | (410, 1465, 1085) | |
(1401, 1330, 1014) | (2166, 1339, 1110) | (1748, 1251, 941) | |
(2183, 1246, 1016) | (721, 1905, 1018) | (2496, 1308, 964) | |
(1571, 1697.5, 987) | (1797, 2110, 1045) | (1621, 1926, 1083) | |
(890, 1969, 1096) | (2553, 2161, 1046) | (2548, 2176, 1052) | |
(2322, 2153, 1020) | (757, 2703, 1345) | (911, 2405, 1070) | |
(2168, 2579, 1120) | (2130, 2561, 1229) | (2160, 2505, 1176) | |
(909, 2494, 1048) | N/A | (885, 2903, 1136) |
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Luo, Y.; Chen, Y. Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet. Sensors 2021, 21, 2622. https://doi.org/10.3390/s21082622
Luo Y, Chen Y. Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet. Sensors. 2021; 21(8):2622. https://doi.org/10.3390/s21082622
Chicago/Turabian StyleLuo, Yawen, and Yuhua Chen. 2021. "Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet" Sensors 21, no. 8: 2622. https://doi.org/10.3390/s21082622
APA StyleLuo, Y., & Chen, Y. (2021). Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet. Sensors, 21(8), 2622. https://doi.org/10.3390/s21082622