基于无线自组织网络的智能路由协议的研究
摘要
随着物联网和泛在网的兴起,无线自组织网络进一步成为无线通信领域的关键技术。网络智能化需求日渐强烈,对路由协议的智能性提出了新的需求。新型的智能路由协议不仅需要具备自感知、自学习、自决策、自配置的能力,而且要能与业务模型、调制、功率控制、频谱感知等技术相结合,进行联合优化。本论文以设计路由算法和路由机制为研究内容,以用户的QoS (Quality of Service)需求、通信节能、负载均衡为优化目标,以最优化理论、多目标决策、机器学习、智能搜索算法为理论基础,开展无线自组织网络的智能路由协议的研究。
本论文受国家863项目、国家自然科学基金、国家重大专项及国际科技合作等项目的支持,围绕路由协议的智能机制,积极探索新的解决方案,取得了以下的研究成果：
(1)针对智能路由的框架建模问题,创新性的提出了基于OODA (Observe, Orient, Decide and Act)控制环的智能路由机制。该智能路由协议能充分利用自感知信息,具备自适应特性,能独立完成自配置过程,同时具有好的健壮性、负载均衡能力、QoS保障能力。它基于认知无线电,认知包技术,实现网络状态、节点状态信息的交互、收集过程,获得路由信息库；具备自主学习能力,实现过滤,推理,判断,更新信息过程,获得路由知识库；针对不同的网络结构、区分不同的用户等级、面向不同的业务类型,获得网络优化目标,并针对该目标自适应的从支持多种优化方案的策略库中选择路由策略；根据决策结果进行自组织,自配置,完成路由过程。本文认为基于该路由处理架构,具备自感知、自学习、自决策、自配置能力的路由协议称为智能路由协议。
(2)针对无线信道的时变特性和自组织网络带宽受限的现状,引入强化学习机制,通过与环境的交互,用较低的路由开销完成对环境的学习和感知,设计了针对QoS保障目标的,基于Q-强化学习的多播路由协议和机会路由协议。所设计的多播路由协议创新性的将Q-强化学习引入多播领域,基于Q-强化学习的学习和预测能力,用较少的发送机会,完成具备QOS保证能力的多播传输。所设计的机会路由协议创新性的将Q-强化学习引入机会路由领域,能合理选择中继集并控制了副本数量,在保证传输率和时延的高性能的前提下,凭借Q-强化学习对动态环境的适应能力,更加适应速率较快的应用场景。
(3)为了满足绿色通信的需求,设计了基于联合优化和模拟退火算法的绿色路由机制。基于绿色节能的目标和需求,创新性的提出了联合调制、功率控制和路由的跨层优化设计,该设计能通过求解节能的优化目标和流量约束组成的优化问题,获得较节能的调制方式、发送功率和传输路径。然后,针对网络场景过大,节点过多的场景,创新性的提出了基于节点相关性的模拟退火的路由搜索方法。结果表明,该机制能用较少的计算资源获取较好的数据传输性能。
(4)为了在智能路由中利用认知无线电技术的自感知能力,设计了基于协作的频谱感知算法和考虑频谱感知结果的负载均衡路由协议。针对认知无线电在无线自组织网络的应用场景,创新性的设计了分布式的基于信道估计与激励的频谱感知算法,在保证误警率的前提下,提高了算法的检测概率。并且,基于该算法创新性的提出了考虑频谱稳定性、负载均衡、QoS保障等性能,在不同的网络环境下动态调整路由度量权重的多策略路由,提高了认知无线电环境下的路由效率。
(5)针对实物实验验证智能路由技术的需求,搭架了由嵌入式开发板、台式机、笔记本电脑组成的无线自组织网络实验平台。在基于Linux操作系统的实验平台中,遵循OODA的智能路由框架,创新性的设计了自感知模块、自学习模块、自决策模块、自配置模块等模块,实现了多模式多策略的智能路由。通过实物实验验证了基于OODA的智能路由框架的可行性,并为其他研究成果提供测试场所和进一步改进的依据。
综上所述,本论文针对智能路由协议开展了深入的研究,为无线自组织网络领域乃至整个通信领域的新型智能路由的设计进行了探索。
With the development of the Internet of Things and Ubiquitous Network, wireless Ad Hoc network become a key technology in wireless communications. The increasing requirements of network intelligence make new requests on the intelligence of routing protocols. The new intelligent routing protocols not only need to be self-awareness, self-learning, self-decision-making, self-configuration capability, but also need to be able to combine with service model, modulation, power control, spectrum sensing technology for joint optimization. This dissertation focuses on the design of routing algorithms and routing mechanisms, aiming at optimizing the QoS requirement of users, energy efficiency and load balance. Optimization theory, multi-objective decision-making, machine learning, intelligent search algorithm are employed as academic rudiment to research on the intelligent routing of wireless Ad Hoc networks.
Supported by several project of National High Tech Research and Development Plan of China, National Natural Science Foundation of China, National International Science and Technology Cooperation Project with Sweden and Canada, the dissertation aims at the intelligent mechanism of the routing protocols, exploiting new solving methods.The main contributions of the dissertation can be described as follows:
(1) The dissertation investigates a framework of intelligent routing based on OODA innovatively, which is aim at the modeling of intelligent routing. The intelligent routing protocol can make full use of self-cognitive informations adaptively. It is also able to complete the self-configuration process, at the same time, it has good robustness, load balancing capabilities, QoS guarantee capabilities. By the method of cross-layer design, based on cognitive radio and cognitive packet technology, it can actualize the collection, interaction and harvest the information of network status and node status, then it obtain a routing information data-base. With the self-learning ability, it can filter, reasoning, judge and update the information, then construct knowledge data base of routing. It can obtain the network optimization goals different from Network structure, user class and service types, and against the goal choose different routing policy adaptively from multiple of optimization strategies. According to the optimization results, it completes the routing process base on self-organizing and self-configuration.
(2) The dissertation proposes a reinforcement learning mechanism to learn and predict the time-varying characteristics of wireless channels and the status of the network. The mechanism completed the learning and predict of the environment through the interaction with the environment with a low routing overhead. First, the dissertation studies the method of reinforcement learning and Q-learning, then design the multicast routing protocol and opportunistic routing protocol based on Q-learning. The design of multicast routing protocols brings Q-learning into the field of multicast innovatively. It can complete the multicast transmission with QoS guarantees under fewer transmit opportunities. The design of opportunistic routing protocol introduces the Q-learning into that field innovatively. With the ability to adapt to the dynamic environment of Q-learning, it can be more adaptable to the fast change scenarios under the premise of ensuring the high-performance of the transmission rate and delay.
(3) The dissertation designs the green routing mechanism based on the joint optimization and simulated annealing algorithm to meet the needs of green communications. The dissertation first completes the survey of green communications and energy efficient routing, and then it proposes a joint modulation, power control and routing cross-layer optimization innovatively. It can obtain the most energy-efficient modulation, transmit power and transmission path by solving the optimization problem of optimization objectives of energy saving and flow constraints. Finally, focusing on the problem that the network topology is too large and the nodes are too dense, the dissertation proposes a routing search method based on simulated annealing innovatively. The result shows that the mechanism can obtain better data transmission performance with fewer computing resources.
(4) For the application of cognitive radio in wireless Ad Hoc network, the dissertation design a collaborative spectrum sensing algorithm and a load balancing routing protocol considering the spectrum sensing results. In cognitive radio scenarios, the dissertation designs a distributed spectrum sensing algorithm based on channel estimation and incentive innovatively. The algorithm improves the detection probability under the premise of ensuring the false alarm radio. Based on the collaborative spectrum sensing algorithm, the dissertation designs a Multi-policy routing considering the spectrum stability, load balancing, QoS guarantee performance innovatively. The routing adjusts the weight of routing metrics dynamically to improve the routing efficiency in the cognitive radio environment.
(5) Facing the demand for physical experiments of intelligent routing technology, Set up a testbed with embedded development board, desktops, laptops, wireless Ad Hoc network. Follow the OODA intelligent routing framework, we design self-cognitive module, self-learning module, self-decision making module and self-configuration module in this testbed. Through the experiment, we verify the feasibility of the intelligent routing framework based on OODA. And the testbed provide the test sites and the experiment basis for further improvement of other research.
In summary, this dissertation researches the intelligent routing protocol in-depth. The researche fruits explore the design of new intelligent routing for wireless Ad Hoc network, the works even have meaning to the whole field of communications.
本论文受国家863项目、国家自然科学基金、国家重大专项及国际科技合作等项目的支持,围绕路由协议的智能机制,积极探索新的解决方案,取得了以下的研究成果：
(1)针对智能路由的框架建模问题,创新性的提出了基于OODA (Observe, Orient, Decide and Act)控制环的智能路由机制。该智能路由协议能充分利用自感知信息,具备自适应特性,能独立完成自配置过程,同时具有好的健壮性、负载均衡能力、QoS保障能力。它基于认知无线电,认知包技术,实现网络状态、节点状态信息的交互、收集过程,获得路由信息库；具备自主学习能力,实现过滤,推理,判断,更新信息过程,获得路由知识库；针对不同的网络结构、区分不同的用户等级、面向不同的业务类型,获得网络优化目标,并针对该目标自适应的从支持多种优化方案的策略库中选择路由策略；根据决策结果进行自组织,自配置,完成路由过程。本文认为基于该路由处理架构,具备自感知、自学习、自决策、自配置能力的路由协议称为智能路由协议。
(2)针对无线信道的时变特性和自组织网络带宽受限的现状,引入强化学习机制,通过与环境的交互,用较低的路由开销完成对环境的学习和感知,设计了针对QoS保障目标的,基于Q-强化学习的多播路由协议和机会路由协议。所设计的多播路由协议创新性的将Q-强化学习引入多播领域,基于Q-强化学习的学习和预测能力,用较少的发送机会,完成具备QOS保证能力的多播传输。所设计的机会路由协议创新性的将Q-强化学习引入机会路由领域,能合理选择中继集并控制了副本数量,在保证传输率和时延的高性能的前提下,凭借Q-强化学习对动态环境的适应能力,更加适应速率较快的应用场景。
(3)为了满足绿色通信的需求,设计了基于联合优化和模拟退火算法的绿色路由机制。基于绿色节能的目标和需求,创新性的提出了联合调制、功率控制和路由的跨层优化设计,该设计能通过求解节能的优化目标和流量约束组成的优化问题,获得较节能的调制方式、发送功率和传输路径。然后,针对网络场景过大,节点过多的场景,创新性的提出了基于节点相关性的模拟退火的路由搜索方法。结果表明,该机制能用较少的计算资源获取较好的数据传输性能。
(4)为了在智能路由中利用认知无线电技术的自感知能力,设计了基于协作的频谱感知算法和考虑频谱感知结果的负载均衡路由协议。针对认知无线电在无线自组织网络的应用场景,创新性的设计了分布式的基于信道估计与激励的频谱感知算法,在保证误警率的前提下,提高了算法的检测概率。并且,基于该算法创新性的提出了考虑频谱稳定性、负载均衡、QoS保障等性能,在不同的网络环境下动态调整路由度量权重的多策略路由,提高了认知无线电环境下的路由效率。
(5)针对实物实验验证智能路由技术的需求,搭架了由嵌入式开发板、台式机、笔记本电脑组成的无线自组织网络实验平台。在基于Linux操作系统的实验平台中,遵循OODA的智能路由框架,创新性的设计了自感知模块、自学习模块、自决策模块、自配置模块等模块,实现了多模式多策略的智能路由。通过实物实验验证了基于OODA的智能路由框架的可行性,并为其他研究成果提供测试场所和进一步改进的依据。
综上所述,本论文针对智能路由协议开展了深入的研究,为无线自组织网络领域乃至整个通信领域的新型智能路由的设计进行了探索。
With the development of the Internet of Things and Ubiquitous Network, wireless Ad Hoc network become a key technology in wireless communications. The increasing requirements of network intelligence make new requests on the intelligence of routing protocols. The new intelligent routing protocols not only need to be self-awareness, self-learning, self-decision-making, self-configuration capability, but also need to be able to combine with service model, modulation, power control, spectrum sensing technology for joint optimization. This dissertation focuses on the design of routing algorithms and routing mechanisms, aiming at optimizing the QoS requirement of users, energy efficiency and load balance. Optimization theory, multi-objective decision-making, machine learning, intelligent search algorithm are employed as academic rudiment to research on the intelligent routing of wireless Ad Hoc networks.
Supported by several project of National High Tech Research and Development Plan of China, National Natural Science Foundation of China, National International Science and Technology Cooperation Project with Sweden and Canada, the dissertation aims at the intelligent mechanism of the routing protocols, exploiting new solving methods.The main contributions of the dissertation can be described as follows:
(1) The dissertation investigates a framework of intelligent routing based on OODA innovatively, which is aim at the modeling of intelligent routing. The intelligent routing protocol can make full use of self-cognitive informations adaptively. It is also able to complete the self-configuration process, at the same time, it has good robustness, load balancing capabilities, QoS guarantee capabilities. By the method of cross-layer design, based on cognitive radio and cognitive packet technology, it can actualize the collection, interaction and harvest the information of network status and node status, then it obtain a routing information data-base. With the self-learning ability, it can filter, reasoning, judge and update the information, then construct knowledge data base of routing. It can obtain the network optimization goals different from Network structure, user class and service types, and against the goal choose different routing policy adaptively from multiple of optimization strategies. According to the optimization results, it completes the routing process base on self-organizing and self-configuration.
(2) The dissertation proposes a reinforcement learning mechanism to learn and predict the time-varying characteristics of wireless channels and the status of the network. The mechanism completed the learning and predict of the environment through the interaction with the environment with a low routing overhead. First, the dissertation studies the method of reinforcement learning and Q-learning, then design the multicast routing protocol and opportunistic routing protocol based on Q-learning. The design of multicast routing protocols brings Q-learning into the field of multicast innovatively. It can complete the multicast transmission with QoS guarantees under fewer transmit opportunities. The design of opportunistic routing protocol introduces the Q-learning into that field innovatively. With the ability to adapt to the dynamic environment of Q-learning, it can be more adaptable to the fast change scenarios under the premise of ensuring the high-performance of the transmission rate and delay.
(3) The dissertation designs the green routing mechanism based on the joint optimization and simulated annealing algorithm to meet the needs of green communications. The dissertation first completes the survey of green communications and energy efficient routing, and then it proposes a joint modulation, power control and routing cross-layer optimization innovatively. It can obtain the most energy-efficient modulation, transmit power and transmission path by solving the optimization problem of optimization objectives of energy saving and flow constraints. Finally, focusing on the problem that the network topology is too large and the nodes are too dense, the dissertation proposes a routing search method based on simulated annealing innovatively. The result shows that the mechanism can obtain better data transmission performance with fewer computing resources.
(4) For the application of cognitive radio in wireless Ad Hoc network, the dissertation design a collaborative spectrum sensing algorithm and a load balancing routing protocol considering the spectrum sensing results. In cognitive radio scenarios, the dissertation designs a distributed spectrum sensing algorithm based on channel estimation and incentive innovatively. The algorithm improves the detection probability under the premise of ensuring the false alarm radio. Based on the collaborative spectrum sensing algorithm, the dissertation designs a Multi-policy routing considering the spectrum stability, load balancing, QoS guarantee performance innovatively. The routing adjusts the weight of routing metrics dynamically to improve the routing efficiency in the cognitive radio environment.
(5) Facing the demand for physical experiments of intelligent routing technology, Set up a testbed with embedded development board, desktops, laptops, wireless Ad Hoc network. Follow the OODA intelligent routing framework, we design self-cognitive module, self-learning module, self-decision making module and self-configuration module in this testbed. Through the experiment, we verify the feasibility of the intelligent routing framework based on OODA. And the testbed provide the test sites and the experiment basis for further improvement of other research.
In summary, this dissertation researches the intelligent routing protocol in-depth. The researche fruits explore the design of new intelligent routing for wireless Ad Hoc network, the works even have meaning to the whole field of communications.
引文
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