基于802.11协议MAC层重传策略,提出一个衡量节点接入能力的参数CAM(capacity of access to medium),以体现节点周围信道的繁忙程度及其抢占信道能力。在此基础上,联合MAC层和网络层进行跨层设计,提出了一个拥塞感知路由CAOR(congestion ...基于802.11协议MAC层重传策略,提出一个衡量节点接入能力的参数CAM(capacity of access to medium),以体现节点周围信道的繁忙程度及其抢占信道能力。在此基础上,联合MAC层和网络层进行跨层设计,提出了一个拥塞感知路由CAOR(congestion aware on-demand routing)协议。仿真表明:该协议能够在降低开销的前提下,显著增加网络吞吐量,并降低平均端到端的时延。展开更多
Wireless Sensor Networks (WSNs) have inherent and unique characteristics rather than traditional networks. They have many different constraints, such as computational power, storage capacity, energy supply and etc;of ...Wireless Sensor Networks (WSNs) have inherent and unique characteristics rather than traditional networks. They have many different constraints, such as computational power, storage capacity, energy supply and etc;of course the most important issue is their energy constraint. Energy aware routing protocol is very important in WSN, but routing protocol which only considers energy has not efficient performance. Therefore considering other parameters beside energy efficiency is crucial for protocols efficiency. Depending on sensor network application, different parameters can be considered for its protocols. Congestion management can affect routing protocol performance. Congestion occurrence in network nodes leads to increasing packet loss and energy consumption. Another parameter which affects routing protocol efficiency is providing fairness in nodes energy consumption. When fairness is not considered in routing process, network will be partitioned very soon and then the network performance will be decreased. In this paper a Tree based Energy and Congestion Aware Routing Protocol (TECARP) is proposed. The proposed protocol is an energy efficient routing protocol which tries to manage congestion and to provide fairness in network. Simulation results shown in this paper imply that the TECARP has achieved its goals.展开更多
To meet the demand for high on-chip network performance, flexible routing algorithms supplying path diversity and congestion alleviation are required. We propose a CAOE-FA router as a combination of congestionawarenes...To meet the demand for high on-chip network performance, flexible routing algorithms supplying path diversity and congestion alleviation are required. We propose a CAOE-FA router as a combination of congestionawareness and fair arbitration. Buffer occupancies from downstream neighbors are collected to indicate the congestion levels, among the candidate outputs permitted by the odd-even(OE) turn model, the lightest loaded direction is selected; fair arbitration is employed for the condition of the same congestion level to replace random selection. Experimental results show that the CAOE-FA can reduce the average packet latency by up to 22.18% and improve the network throughput by up to 68.58%, with ignorable price of hardware cost.展开更多
The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for...The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods.However,these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically.To overcome this drawback,we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources.In a proposed routing protocol,either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state,Q-value,and reward function to set the desired path toward the destination.A new reward function that consists of a buffer occupancy,link reliability and hop count is considered.Moreover,look ahead algorithm is employed to update the Q-value with values within two hops simultaneously.This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account,accordingly.Finally,the simulation results presented approximately 20%higher packet delivery ratio and 15%shorter end-to-end delay,compared to those with the existing scheme by avoiding congestion adaptively.展开更多
文摘基于802.11协议MAC层重传策略,提出一个衡量节点接入能力的参数CAM(capacity of access to medium),以体现节点周围信道的繁忙程度及其抢占信道能力。在此基础上,联合MAC层和网络层进行跨层设计,提出了一个拥塞感知路由CAOR(congestion aware on-demand routing)协议。仿真表明:该协议能够在降低开销的前提下,显著增加网络吞吐量,并降低平均端到端的时延。
文摘Wireless Sensor Networks (WSNs) have inherent and unique characteristics rather than traditional networks. They have many different constraints, such as computational power, storage capacity, energy supply and etc;of course the most important issue is their energy constraint. Energy aware routing protocol is very important in WSN, but routing protocol which only considers energy has not efficient performance. Therefore considering other parameters beside energy efficiency is crucial for protocols efficiency. Depending on sensor network application, different parameters can be considered for its protocols. Congestion management can affect routing protocol performance. Congestion occurrence in network nodes leads to increasing packet loss and energy consumption. Another parameter which affects routing protocol efficiency is providing fairness in nodes energy consumption. When fairness is not considered in routing process, network will be partitioned very soon and then the network performance will be decreased. In this paper a Tree based Energy and Congestion Aware Routing Protocol (TECARP) is proposed. The proposed protocol is an energy efficient routing protocol which tries to manage congestion and to provide fairness in network. Simulation results shown in this paper imply that the TECARP has achieved its goals.
基金Project supported by the National Natural Science Foundation of China(No.61625403)
文摘To meet the demand for high on-chip network performance, flexible routing algorithms supplying path diversity and congestion alleviation are required. We propose a CAOE-FA router as a combination of congestionawareness and fair arbitration. Buffer occupancies from downstream neighbors are collected to indicate the congestion levels, among the candidate outputs permitted by the odd-even(OE) turn model, the lightest loaded direction is selected; fair arbitration is employed for the condition of the same congestion level to replace random selection. Experimental results show that the CAOE-FA can reduce the average packet latency by up to 22.18% and improve the network throughput by up to 68.58%, with ignorable price of hardware cost.
基金This work was supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2019-0-01343,Training Key Talents in Industrial Convergence Security)and Research Cluster Project,R20143,by Zayed University Research Office.
文摘The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes.Among lots of feasible approaches to avoid congestion efficiently,congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods.However,these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically.To overcome this drawback,we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources.In a proposed routing protocol,either one of uncongested neighboring nodes are randomly selected as next hop to distribute traffic load to multiple paths or Q-learning algorithm is applied to decide the next hop by modeling the state,Q-value,and reward function to set the desired path toward the destination.A new reward function that consists of a buffer occupancy,link reliability and hop count is considered.Moreover,look ahead algorithm is employed to update the Q-value with values within two hops simultaneously.This approach leads to a decision of the optimal next hop by taking congestion status in two hops into account,accordingly.Finally,the simulation results presented approximately 20%higher packet delivery ratio and 15%shorter end-to-end delay,compared to those with the existing scheme by avoiding congestion adaptively.
