[论文解读] Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique for Nodes Starvation Problem in Wireless Mesh Network

Fuad A. Ghaleb, Bander Ali Saleh Al‐rimy+3 more|arXiv (Cornell University)|Jun 17, 2020

Cooperative Communication and Network Coding参考文献 45被引用 4

一句话总结

本文提出 FA-SCGA-CAA，一种面向公平性的半混沌遗传算法，用于多无线电多信道无线网状网络中的信道分配，以缓解由不公平带宽分配引起的节点饥饿问题。通过整合半混沌染色体初始化和非线性适应度函数以优化公平性与干扰，该方法相比现有方法将节点饥饿减少了22%，并将网络容量利用率提高了23%。

ABSTRACT

Multi-Radio Multi-Channel Wireless Mesh Networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT) and vehicular networks. Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms either reduce the total network interference or maximize the total network throughput, which neither guarantees a fair distribution of the channels nor eliminates node starvation. To this end, the Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) was proposed in this paper for Nodes Starvation Problem in Wireless Mesh Networks. FA-SCGA-CAA optimizes fairness based on multiple-criterion using a modified version of the Genetic Algorithm (GA). The modification includes proposing a semi-chaotic technique for creating the primary chromosome with powerful genes. Such a chromosome was used to create a strong population that directs the search towards the global minima in an effective and efficient way. The outcome is a nonlinear fairness oriented fitness function that aims at maximizing the link fairness while minimizing the link interference. Comparison with related work shows that the proposed FA_SCGA_CAA reduced the potential nodes starvation by 22% and improved network capacity utilization by 23%.

研究动机与目标

• 解决多无线电多信道无线网状网络（WMNs）中因信道分配不公而被忽视的节点饥饿问题。
• 开发一种优先考虑网状客户端之间公平性的信道分配技术，而非单纯最大化吞吐量或最小化干扰。
• 通过确保所有网状节点之间的带宽分配均衡，提升网络资源利用率。
• 提出一种采用半混沌初始化的改进遗传算法，以提高在信道分配问题中向全局最优解收敛的性能。

提出的方法

• 采用改进的遗传算法（GA），利用半混沌技术生成初始种群的主要染色体，以增强遗传多样性并加快收敛速度。
• 该算法使用非线性适应度函数，同时最大化链路公平性并最小化网络中的干扰。
• 半混沌初始化确保强基因被嵌入初始种群，促进对解空间的有效探索。
• 适应度函数基于公平性度量和干扰水平评估信道分配，引导搜索朝向最优且均衡的配置。
• 该算法通过选择、交叉和突变迭代演化种群，目标是最小化不公平性和干扰。
• 在仿真环境中对方法进行评估，以对比现有信道分配技术在公平性、饥饿减少和容量利用率方面的表现。

实验结果

研究问题

• RQ1 改进的遗传算法在多无线电多信道无线网状网络中能在多大程度上减少节点饥饿？
• RQ2 与标准初始化相比，半混沌初始化在信道分配中如何提升遗传算法的性能？
• RQ3 面向公平性的适应度函数能否有效平衡干扰减少与WMNs中带宽的公平分配？
• RQ4 与现有算法相比，所提出的 FA-SCGA-CAA 方法在减少饥饿和提升网络容量利用率方面表现如何？

主要发现

• 所提出的 FA-SCGA-CAA 技术相比现有信道分配方法，将潜在的节点饥饿减少了22%。
• 由于更均衡高效的信道分配，网络容量利用率提升了23%。
• 半混沌初始化增强了种群多样性，并加速了向最优解的收敛。
• 非线性公平性导向的适应度函数成功地在最大化公平性的同时最小化了干扰。
• 该方法优于传统方法，后者虽优先考虑吞吐量或干扰减少，却以牺牲公平性为代价。