Computation Offloading Strategy Based on Improved Polar Lights Optimization Algorithm and Blockchain in Internet of Vehicles
Abstract
1. Introduction
- A blockchain-based multi-vehicle multi-task computational offloading model for mobile scenarios is established that can accurately map the task computational demand, offloading the logic and blockchain consensus process generated by vehicles in the process of dynamic mobility;
- A new polar lights optimization algorithm is adopted to optimize the computational offloading strategy, which accelerates the convergence speed while reducing the risk of falling into the local optimal solution and is significantly better than the traditional optimization methods;
- An authorized Byzantine fault-tolerant consensus mechanism is adopted to dynamically select consensus nodes through stake vote election, which ensures transactions are tamperable;
- The impact of task load, number of vehicles, and data volume on system performance under different offloading strategies is demonstrated in simulation experiments, which verifies the stability of the proposed strategies in dynamic IoV environments and confirms the effectiveness of the strategies proposed in this paper.
2. System Model
2.1. Network Model
2.2. Mission Model
2.3. Communications Model
2.4. Computational Model
- (1)
- Local Computing
- (2)
- Edge Server Computing
2.5. Vehicle Mobility Model
2.6. Blockchain Model
- (1)
- Preparation request:
- (2)
- Preparation reply:
- (3)
- Submission:
- (4)
- Broadcast:
2.7. Joint Optimization Problems
3. Computation Offloading Strategy Based on Improved Polar Lights Optimization Algorithm
3.1. Improved Polar Lights Optimization
3.2. Algorithm Design
- (1)
- Initializing the Population
- (2)
- Updating Population Locations
- (3)
- Population Variation
- (4)
- Bounce Boundary Handling Mechanism
3.3. The Overall Flow of the Algorithm
Algorithm 1. Computation offloading strategy based on IPLO algorithm | |
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. | Input: X Initialize high-energy particle populations X[i](i = 1, 2, 3, ...); Initialize the number of iterations FEs; Initialize the maximum number of iterations MaxFEs; Initialization N, X_new, BestS, BestP, BestTotal; Calculation of adaptation values ; While FEs <= MaxFEs do Use Equation (27) to calculate ; Use Equation (28) to calculate ; For i = 0 to N do Use Equation (29) to calculate ; Use Equation (30) to calculate ; Use Equation (31) to update ; If and then Use Equation (32) to update ; Use Equation (33) to update ; End if Calculate the fitness value ; FEs = FEs + 1; End for If then ; End if Use Equation (22), Equation (23) to calculate BestP; Calculate BestTotal from the sum of BestS and BestP; End While Output: BestTotal. |
4. Simulation Experiments and Data Analysis
4.1. Simulation Parameters
4.2. Analysis of Experimental Results
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameters | Numerical |
---|---|
Data size of the task CPU cycles required for the task Transmitting power of the vehicle [28] Computing power of the vehicles [29] Computing power of the MEC server [30] Radius of communication coverage of RSUs [31] Gaussian channel white noise [32] Subchannel bandwidth [33] Weighting of latency Weighting of energy CPU cycles required for signature [34] CPU cycles required for MAC [34] Average deal size [35] Average computing power of RSUs [36] Average transmission power of RSUs [37] Number of blockchain nodes Number of blockchain consensus nodes Constant speed | [0.5–1.5] MB [1000–2000] Megacycles 46 dBm 2.0 GHz 8.0 GHz 500 m −147 dBm 2 MHz 0.8 0.2 1 Megacycles 10 Megacycles 200 B 3 GHz 1000 mW 6 4 40 Km/s |
Arithmetic | Fixed Data Volume and Number of Tasks | Fixed CPU Cycles and Number of Tasks | Fixed CPU Cycles and Data Volume |
---|---|---|---|
FOX | 0.022325712 | 0.121529847 | 0.084908155 |
PLO | 0.006160379 | 0.120456452 | 0.086015252 |
IPLO | 0.001792003 | 0.115658619 | 0.071276243 |
Collision Probability | Fixed Data Volume and Number of Tasks | Fixed CPU Cycles and Number of Tasks | Fixed CPU Cycles and Data Volume |
---|---|---|---|
0.01 | 0.450215907 | 0.40370882 | 0.615417646 |
0.03 | 0.451200618 | 0.397350783 | 0.619286688 |
0.05 | 0.451540167 | 0.400150143 | 0.616115548 |
0.07 | 0.450900238 | 0.399467543 | 0.618433646 |
0.1 | 0.45002346 | 0.403075415 | 0.616594892 |
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Liu, Y.; Yan, B.; Wang, B.; Sun, Q.; Dai, Y. Computation Offloading Strategy Based on Improved Polar Lights Optimization Algorithm and Blockchain in Internet of Vehicles. Appl. Sci. 2025, 15, 7341. https://doi.org/10.3390/app15137341
Liu Y, Yan B, Wang B, Sun Q, Dai Y. Computation Offloading Strategy Based on Improved Polar Lights Optimization Algorithm and Blockchain in Internet of Vehicles. Applied Sciences. 2025; 15(13):7341. https://doi.org/10.3390/app15137341
Chicago/Turabian StyleLiu, Yubao, Bocheng Yan, Benrui Wang, Quanchao Sun, and Yinfei Dai. 2025. "Computation Offloading Strategy Based on Improved Polar Lights Optimization Algorithm and Blockchain in Internet of Vehicles" Applied Sciences 15, no. 13: 7341. https://doi.org/10.3390/app15137341
APA StyleLiu, Y., Yan, B., Wang, B., Sun, Q., & Dai, Y. (2025). Computation Offloading Strategy Based on Improved Polar Lights Optimization Algorithm and Blockchain in Internet of Vehicles. Applied Sciences, 15(13), 7341. https://doi.org/10.3390/app15137341