Core Concepts
The author proposes multi-objective optimization algorithms to address challenges in deploying roadside units in urban vehicular networks, focusing on communication efficiency and cost reduction.
Abstract
The significance of transportation efficiency and safety in urban vehicular networks is increasing. Challenges such as conflicting objectives, obstacles, and large-scale optimization spaces are addressed through proposed multi-objective optimization algorithms. These algorithms aim to optimize RSU deployment by balancing exploration and exploitation strategies, calibrating RSU density, and implementing a data offloading mechanism. Experimental results show the effectiveness of the proposed algorithms in improving communication services and network efficiency.
Key points:
Importance of RSU deployment in urban environments for communication services.
Challenges include conflicting objectives, obstacles, and large-scale optimization spaces.
Proposed multi-objective optimization algorithms address these challenges.
Strategies include balancing exploration and exploitation, calibrating RSU density, and data offloading mechanisms.
Experimental results demonstrate the effectiveness of the proposed algorithms.
Stats
The population size (N) is set as 360.
The number of sub-populations (m) is set as 3.
Initial crossover rate is set at 50%, mutation rate at 0.05.
Maximum number of iterations (G) limited to 50.
Quotes
"The deployment of RSUs encounters significant difficulties due to multiple objectives and obstacles."
"Our strategies perform better in both high-density and low-density urban scenarios."