چكيده لاتين
Mobile devices often rely on alternative servers to handle tasks due to limited storage and processing capabilities. While cloud computing servers offer ample resources, the high latency in task transfer makes them less favorable for task offloading. Mobile edge computing as a new communication paradigm reduces latency by positioning edge servers in close proximity to end users. Despite having lower capacity and processing power than cloud servers, edge servers play a crucial role in improving QoS and QoE. Deploying additional servers not only enhances the robustness of the edge server network but also allows for load balancing among servers. However, service providers usually tend to minimize costs by deploying the least number of edge servers necessary to cater to the maximum number of users. The utilization of a reduced number of servers and their improper placement may result in increased latency and inadequate coverage for users. Therefore, it is essential to implement a policy for determining the quantity and positioning of servers, ultimately achieving a trade-off between latency, coverage, and cost. This study focuses on strategically siting edge servers to minimize expenses and enhance key user criteria. The concept of overlapping areas of servers is employed, aiming to deploy fewer servers to cover base stations concurrently and consequently reduce costs. In this dissertation, two three-objective models are introduced to optimize delay, coverage, and costs simultaneously in WMAN. To simulate the environment, the Shanghai Telecommunications Dataset was utilized. To solve the first model, BMOPSO-O, BMOPSO-T, and NSGA-II algorithms were used, based on the results of HV indicator, the NSGA-II algorithm showed the best performance among the mentioned algorithms, and in the worst case, its value was equal to 0.1883. For the second model, alongside the BMOPSO-T, NSGA-II, BMOGWO, and BMOWOA algorithms, a novel algorithm called BHNM was introduced. According to the results of the HV indicator, the proposed algorithm had a better performance and the value of the HV indicator was equal to 0.0354 in the worst case. When using the HV indicator to compare algorithms, the higher the HV indicator of an algorithm indicates the successful performance of that algorithm in achieving a more diverse set of solutions. The variety of solutions available on the Pareto front enables urban planners and service providers to select the most suitable solution for attaining their objectives.
