Scholarlink

<p>With the advancement of 5G, the Software-Defined-Network (SDN) framework improvises the flexibility and practical feasibility of resource allocation to connected user objects in Internet-of-Vehicles (IoV). This is facilitating the users’ dynamic access to requirements for quality of service (QoS) in regard to threshold parameters such as network traffic, link capacity, and sensor coverage. But scalable and optimized allocation of resources are still a concern in an environment of rapidly expanding heterogeneous vehicular traffic. So, in order to meet the multiple objectives of resource optimization for SDN managed and 5G enabled IoV networks, an Entropy Weighted Non-dominated Sorting Genetic Algorithm (EW-NSGA) is suggested. The various needs of connected objects are projected using five cost centric functions and the entropy weight method is used to determine the weight priorities for the corresponding objectives. The results of the simulation and analogy demonstrate that EW-NSGA algorithm is capable of efficiently optimizing the connections, load distribution, end-to-end delays and energy consumption for rapidly expanding dense IoV networks, as compared to the existing algorithms. Thus, this proposed model enables the IoV service providers to launch a reliable, scalable, user-service-based network infrastructure.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An entropy weight-non dominated sorting genetic algorithm for QoS resource optimization in 5G driven IoV networks

  • 期刊论文

  • 发表日期：

    2025年7月1日

查看全文
  • Satyabrat Sahoo,
  • Satya Prakash Sahoo,
  • Ram Chandra Barik,
  • Manas Ranjan Kabat

摘要

With the advancement of 5G, the Software-Defined-Network (SDN) framework improvises the flexibility and practical feasibility of resource allocation to connected user objects in Internet-of-Vehicles (IoV). This is facilitating the users’ dynamic access to requirements for quality of service (QoS) in regard to threshold parameters such as network traffic, link capacity, and sensor coverage. But scalable and optimized allocation of resources are still a concern in an environment of rapidly expanding heterogeneous vehicular traffic. So, in order to meet the multiple objectives of resource optimization for SDN managed and 5G enabled IoV networks, an Entropy Weighted Non-dominated Sorting Genetic Algorithm (EW-NSGA) is suggested. The various needs of connected objects are projected using five cost centric functions and the entropy weight method is used to determine the weight priorities for the corresponding objectives. The results of the simulation and analogy demonstrate that EW-NSGA algorithm is capable of efficiently optimizing the connections, load distribution, end-to-end delays and energy consumption for rapidly expanding dense IoV networks, as compared to the existing algorithms. Thus, this proposed model enables the IoV service providers to launch a reliable, scalable, user-service-based network infrastructure.