Due to their easy-to-deploy and self-healing features, WMNs (Wireless Mesh Networks) are emerging as a new promising technology with a rich set of applications. While the IEEE standardization of this new technology is...Due to their easy-to-deploy and self-healing features, WMNs (Wireless Mesh Networks) are emerging as a new promising technology with a rich set of applications. While the IEEE standardization of this new technology is still in progress, its main traits are already set, e.g., architecture and MAC routing. WMNs are attracting considerable research in academia and industry as well, but the lack of open-source testbeds is restricting such a research to simulation tools. The main problem with simulation tools is that they do not reflect the complexity of RF propagation, especially in indoor environments, of which IEEE 802.11s WMNs are an example. This paper presents an open-source implementation of an indoor IEEE 802.11s WMN testbed. The implementation is transparent, easy-to-deploy, and both the source code and deployment instructions are available online. The implementation can serve as a blueprint for the WMN research community to deploy their own testbeds, negating the shortcomings of using simulation tools. By delving into the testbed implementation subtleties, this paper is shedding further light on the details of the ongoing IEEE 802.11s standard. Major encountered implementation problems (e.g., clients association, Internetworking, and supporting multiple gateways) are identified and addressed. To ascertain the functionality of the testbed, both UDP and TCP traffic are supported and operational. The testbed uses the default IEEE 802.11s HWMP (Hybrid Wireless Mesh Protocol) routing protocol along with the default IEEE 802.11s Airtime routing metric.展开更多
无线mesh网络是下一代无线网络技术中人们研究与关注的热点技术之一。根据最新IEEE 802.11s协议,其路由判据是基于无线感知的空时链路判据(airtime link metric,ALM)。这种路由判据比传统的以跳数作为判据要好,但是当多信道或多射频条...无线mesh网络是下一代无线网络技术中人们研究与关注的热点技术之一。根据最新IEEE 802.11s协议,其路由判据是基于无线感知的空时链路判据(airtime link metric,ALM)。这种路由判据比传统的以跳数作为判据要好,但是当多信道或多射频条件下时,这种判据会引起信道容量的衰减,不足以满足如今的网络需求。因此,有许多新的路由判据被提出。例如,加权累计期望传输时间,干扰及信道切换,归一化的瓶颈链路容量等。本文主要定性的比较这些判据的特点,然后通过NS-2进行网络仿真,根据IEEE 802.11s协议中默认的路由协议,将这些多信道条件下的路由判据进行相互比较。由此,得出结论,明确各种路由判据所适用的不同的场合。展开更多
This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networ...This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.展开更多
文摘Due to their easy-to-deploy and self-healing features, WMNs (Wireless Mesh Networks) are emerging as a new promising technology with a rich set of applications. While the IEEE standardization of this new technology is still in progress, its main traits are already set, e.g., architecture and MAC routing. WMNs are attracting considerable research in academia and industry as well, but the lack of open-source testbeds is restricting such a research to simulation tools. The main problem with simulation tools is that they do not reflect the complexity of RF propagation, especially in indoor environments, of which IEEE 802.11s WMNs are an example. This paper presents an open-source implementation of an indoor IEEE 802.11s WMN testbed. The implementation is transparent, easy-to-deploy, and both the source code and deployment instructions are available online. The implementation can serve as a blueprint for the WMN research community to deploy their own testbeds, negating the shortcomings of using simulation tools. By delving into the testbed implementation subtleties, this paper is shedding further light on the details of the ongoing IEEE 802.11s standard. Major encountered implementation problems (e.g., clients association, Internetworking, and supporting multiple gateways) are identified and addressed. To ascertain the functionality of the testbed, both UDP and TCP traffic are supported and operational. The testbed uses the default IEEE 802.11s HWMP (Hybrid Wireless Mesh Protocol) routing protocol along with the default IEEE 802.11s Airtime routing metric.
文摘无线mesh网络是下一代无线网络技术中人们研究与关注的热点技术之一。根据最新IEEE 802.11s协议,其路由判据是基于无线感知的空时链路判据(airtime link metric,ALM)。这种路由判据比传统的以跳数作为判据要好,但是当多信道或多射频条件下时,这种判据会引起信道容量的衰减,不足以满足如今的网络需求。因此,有许多新的路由判据被提出。例如,加权累计期望传输时间,干扰及信道切换,归一化的瓶颈链路容量等。本文主要定性的比较这些判据的特点,然后通过NS-2进行网络仿真,根据IEEE 802.11s协议中默认的路由协议,将这些多信道条件下的路由判据进行相互比较。由此,得出结论,明确各种路由判据所适用的不同的场合。
文摘This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.
