Mobile edge computing(MEC)provides services to devices and reduces latency in cellular internet of things(IoT)networks.However,the challenging problem is how to deploy MEC servers economically and efficiently.This pap...Mobile edge computing(MEC)provides services to devices and reduces latency in cellular internet of things(IoT)networks.However,the challenging problem is how to deploy MEC servers economically and efficiently.This paper investigates the deployment problem of MEC servers of the real-world road network by employing an improved genetic algorithm(GA)scheme.We first use the threshold-based K-means algorithm to form vehicle clusters according to their locations.We then select base stations(BSs)based on clustering center coordinates as the deployment locations set for potential MEC servers.We further select BSs using a combined simulated annealing(SA)algorithm and GA to minimize the deployment cost.The simulation results show that the improved GA deploys MEC servers effectively.In addition,the proposed algorithm outperforms GA and SA algorithms in terms of convergence speed and solution quality.展开更多
基金supported in part by National Key Research and Development Project (2020YFB1807204)in part by the National Natural Science Foundation of China (U2001213 and 61971191)+1 种基金in part by the Beijing Natural Science Foundation under Grant L201011in part by Jiangxi Key Laboratory of Artificial Intelligence Transportation Information Transmission and Processing (20202BCD42010)
文摘Mobile edge computing(MEC)provides services to devices and reduces latency in cellular internet of things(IoT)networks.However,the challenging problem is how to deploy MEC servers economically and efficiently.This paper investigates the deployment problem of MEC servers of the real-world road network by employing an improved genetic algorithm(GA)scheme.We first use the threshold-based K-means algorithm to form vehicle clusters according to their locations.We then select base stations(BSs)based on clustering center coordinates as the deployment locations set for potential MEC servers.We further select BSs using a combined simulated annealing(SA)algorithm and GA to minimize the deployment cost.The simulation results show that the improved GA deploys MEC servers effectively.In addition,the proposed algorithm outperforms GA and SA algorithms in terms of convergence speed and solution quality.
