无线传感器网络负载均衡数据汇集算法研究
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摘要
无线传感器网络作为普适计算的一种支撑技术,是控制工程和人工智能领域的研究前沿,应用前景十分广泛。
数据汇集应用作为无线传感器网络的一种广泛应用形式,具有网络数据流连续、流量大、“多对一”的特点。数据汇集过程中,当各个数据流分支的流量分配不均衡时,容易导致部分上游节点负载过重,造成其提前“死亡”,严重影响网络寿命。负载均衡对缓解网络拥塞、提高网络服务质量和资源利用率非常有效,被广泛地应用到Internet网络,而针对无线传感器网络的负载均衡研究还处于起步阶段。
本文以提高网络服务质量、延长网络寿命为目标,针对以下三种应用场景,研究无线传感器网络负载均衡数据汇集算法。
①同构网络场景:Sink节点静止,传感器节点产生数据速率一致;
②异构网络场景:Sink节点静止,传感器节点产生数据速率不一致;
③移动用户场景:Sink节点移动。
本文在研究内容上力求有所突破与创新,主要研究成果包括以下四个方面:
1)提出一种动态交叉退避窗口算法。针对上述①和②两种场景,需要采用洪泛方式建立数据汇集树或者层次发现。而洪泛过程中,传统的MAC层碰撞退避机制容易造成消息剧烈碰撞和路径绕行。本文针对该问题分析了其产生原因,并提出了动态交叉退避窗口算法(DOBW)。DOBW算法在洪泛过程中,根据邻居节点的当前状态,自动调整退避窗口大小,以减少消息碰撞,优化数据汇集树的结构。仿真实验表明,相比802.11和802.15.4,本文提出的DOBW算法可显著地减少洪泛时消息碰撞,优化了数据汇集树的拓扑结构。
2)提出一种负载均衡数据汇集树生成算法。DOBW算法虽然可以优化数据汇集树的结构,但不能够达到负载均衡的要求,因此DOBW算法通常用于避免层次发现过程中的消息剧烈碰撞。本文针对上述第①种场景,提出了一种负载均衡数据汇集树生成算法(LDGT-SPT)。在最短路径树拓扑结构保证数据实时性的情况下,LDGT-SPT算法通过邻居发现、基于DOBW的层次发现、度小优先原则和流量均衡策略构造一棵最短路径负载均衡数据汇集树。仿真实验表明,本文提出的LDGT-SPT算法虽然在网络寿命上与SLBT算法相当,但是在网络性能方面有显著的提高。
3)提出一种基于ACO的动态负载均衡数据汇集算法。在上述第②种场景中,由于传感器节点数据产生速率不一致,因此不能使用构造负载均衡数据汇集树的方法,这时需要采用动态负载均衡的方法。本文针对该场景提出了一种基于蚁群优化的动态负载均衡数据汇集算法(LDG-ACO)。为了实现动态负载均衡,LDG-ACO算法将蚂蚁进行分类,使其具有不同的功能,采用节点的负载信息作为启发因子,使得蚂蚁具有负载感知功能,并规定蚂蚁的转移概率按照信息素少概率大的原则进行。仿真实验表明,与ACO,SLBT以及DLBT算法相比,本文提出的LDG-ACO算法在网络性能和网络寿命方面都有显著的提高。
4)提出一种支持移动Sink的动态负载均衡数据汇集算法。针对上述第③种场景中,移动Sink导致路由频繁改变和链路中断问题,本文提出了一种支持移动Sink的动态负载均衡数据汇集算法(LDG-MS)。LDG-MS算法借鉴群体智能的思想,通过定义两个简单的规则,对节点的数据转发行为进行描述,将下一跳节点的决策问题抽象成一个多目标规划问题,并采用距离加权评价法进行求解。为了解决由于Sink节点移动造成的链路中断问题,提出对移动Sink信标消息进行功率控制的策略,并给出了详细的计算方法。仿真实验表明,与SINK_CLAIM、SLM算法相比,本文提出的LDG-MS算法在网络性能和网络寿命方面都有显著的提高。
As a cutting edge research area in the automatic control and artificial intelligence, Wireless Sensor Networks (WSN) is a fundamental of the network infrastructure for Pervasive Computing environments, with very wide application prospects.
The data gathering application is one of the widespread application forms of WSN, characterized with the large continuous data flow ability and "many-to-one" feature. In data gathering process, the load imbalance of each data stream branch would cause the fast energy exhaustion in overburdened nodes which would leads to the death prematurely. The load balancing technology to alleviate network congestion, which improves network service quality and resource utilization, is very effective and widely applied to the Internet network. However the study of load balancing for the wireless sensor networks is still in its early stages.
To improve network service quality and extend the network lifetime, the wireless sensor network load balancing algorithm for data gathering is studied with the following scenarios, respectively:
①The stationary Sink node and sensor nodes with the same data generating rate(Homogeneous Network);
②The stationary Sink node and sensor nodes with the different data generating rate(Homogeneous Network);
③The moving Sink node (mobile user network).
The main research achievements are listed in the following:
1)A dynamic overlapping backoff window method is proposed.
In response to the scenarios①and②, the flooding mechanism is approached to build the data gathering tree or layer discovery. Considering the traditional back-off mechanisms in MAC layer would result in severe collisions and route circumambulating in flooding, the Dynamic Overlapping Back-off Window (DOBW) algorithm was presented. According to current rate of neighbors, sensor nodes adjust their back-off windows automatically to reduce message collision and optimize the topology. Comparing with 802.11 and 802.15.4, the simulations shown that DOBW algorithm proposed in this thesis can significantly reduce the impact of flooding and optimize the topology of the data gathering tree.
2)A load-balanced data gathering tree generation algorithm is proposed.
Though, the DOBW algorithm could construct a certain degree of load-balanced tree, the requirements of load balance is still un-achieved. Therefore, the DOBW algorithm is commonly used in the layer discovery process to avoid the message collision. The Load-balanced Data Gathering Tree based on SPT (LDGT-SPT) to establish a shortest load-balanced path tree was presented for scenario①. The LDGT-SPT is consisted of the neighbor discovery, the layer discovery, the priority principle of minimal degree and the traffic balance strategy. Simulation results show that LDGT-SPT algorithm can significantly improve the performance of the network compared with SLBT with the same network lifetime.
3) An ACO-based dynamic load balancing algorithm for data collection is proposed.
In the scenario②, due to different generating rate of sensor node data, the load-balancing data gathering tree construction methods is un-available, thus the dynamic load balancing approach is adapted. In this thesis, the Load-balanced Data Gathering algorithm based on Ant Colony Optimization (LDG-ACO) was presented. In order to achieve dynamic load balancing, LDG-ACO algorithm classifies ants to have different functions, taking the node load information as inspiration factor, making ants with the load-sensing function and working according to the principle of lower pheromone higher probability. Simulation results show that compared with the ACO, SLBT and DLBT algorithm, LDG-ACO algorithm presented in this thesis has increased markedly in network performance and network service life.
4)A dynamic load balancing algorithm supporting mobile Sink for data collection is proposed.
In response to the scenario③, the Mobile Sink led to frequent route changes and link interrupt problem, a mobile Sink dynamic load-balancing algorithm for data collection (LDG-MS) is presented to solve these problems. Referring from the swarm intelligence algorithm, LDG-MS defines two simple rules to describe the data forwarding. The problem how to choose next hop is described as a multi-objective programming. Furtherly, the evaluation method of weighting distance is used in the multi-objective programming. To solve link-break problem, a method of the power control for the Sink beacon messages was proposed. Simulation results show that, compared with SINK_CLAIM, SLM algorithm, LDG-MS algorithm presented in this thesis in network performance and network lifetime has increased profoundly.
数据汇集应用作为无线传感器网络的一种广泛应用形式,具有网络数据流连续、流量大、“多对一”的特点。数据汇集过程中,当各个数据流分支的流量分配不均衡时,容易导致部分上游节点负载过重,造成其提前“死亡”,严重影响网络寿命。负载均衡对缓解网络拥塞、提高网络服务质量和资源利用率非常有效,被广泛地应用到Internet网络,而针对无线传感器网络的负载均衡研究还处于起步阶段。
本文以提高网络服务质量、延长网络寿命为目标,针对以下三种应用场景,研究无线传感器网络负载均衡数据汇集算法。
①同构网络场景:Sink节点静止,传感器节点产生数据速率一致;
②异构网络场景:Sink节点静止,传感器节点产生数据速率不一致;
③移动用户场景:Sink节点移动。
本文在研究内容上力求有所突破与创新,主要研究成果包括以下四个方面:
1)提出一种动态交叉退避窗口算法。针对上述①和②两种场景,需要采用洪泛方式建立数据汇集树或者层次发现。而洪泛过程中,传统的MAC层碰撞退避机制容易造成消息剧烈碰撞和路径绕行。本文针对该问题分析了其产生原因,并提出了动态交叉退避窗口算法(DOBW)。DOBW算法在洪泛过程中,根据邻居节点的当前状态,自动调整退避窗口大小,以减少消息碰撞,优化数据汇集树的结构。仿真实验表明,相比802.11和802.15.4,本文提出的DOBW算法可显著地减少洪泛时消息碰撞,优化了数据汇集树的拓扑结构。
2)提出一种负载均衡数据汇集树生成算法。DOBW算法虽然可以优化数据汇集树的结构,但不能够达到负载均衡的要求,因此DOBW算法通常用于避免层次发现过程中的消息剧烈碰撞。本文针对上述第①种场景,提出了一种负载均衡数据汇集树生成算法(LDGT-SPT)。在最短路径树拓扑结构保证数据实时性的情况下,LDGT-SPT算法通过邻居发现、基于DOBW的层次发现、度小优先原则和流量均衡策略构造一棵最短路径负载均衡数据汇集树。仿真实验表明,本文提出的LDGT-SPT算法虽然在网络寿命上与SLBT算法相当,但是在网络性能方面有显著的提高。
3)提出一种基于ACO的动态负载均衡数据汇集算法。在上述第②种场景中,由于传感器节点数据产生速率不一致,因此不能使用构造负载均衡数据汇集树的方法,这时需要采用动态负载均衡的方法。本文针对该场景提出了一种基于蚁群优化的动态负载均衡数据汇集算法(LDG-ACO)。为了实现动态负载均衡,LDG-ACO算法将蚂蚁进行分类,使其具有不同的功能,采用节点的负载信息作为启发因子,使得蚂蚁具有负载感知功能,并规定蚂蚁的转移概率按照信息素少概率大的原则进行。仿真实验表明,与ACO,SLBT以及DLBT算法相比,本文提出的LDG-ACO算法在网络性能和网络寿命方面都有显著的提高。
4)提出一种支持移动Sink的动态负载均衡数据汇集算法。针对上述第③种场景中,移动Sink导致路由频繁改变和链路中断问题,本文提出了一种支持移动Sink的动态负载均衡数据汇集算法(LDG-MS)。LDG-MS算法借鉴群体智能的思想,通过定义两个简单的规则,对节点的数据转发行为进行描述,将下一跳节点的决策问题抽象成一个多目标规划问题,并采用距离加权评价法进行求解。为了解决由于Sink节点移动造成的链路中断问题,提出对移动Sink信标消息进行功率控制的策略,并给出了详细的计算方法。仿真实验表明,与SINK_CLAIM、SLM算法相比,本文提出的LDG-MS算法在网络性能和网络寿命方面都有显著的提高。
As a cutting edge research area in the automatic control and artificial intelligence, Wireless Sensor Networks (WSN) is a fundamental of the network infrastructure for Pervasive Computing environments, with very wide application prospects.
The data gathering application is one of the widespread application forms of WSN, characterized with the large continuous data flow ability and "many-to-one" feature. In data gathering process, the load imbalance of each data stream branch would cause the fast energy exhaustion in overburdened nodes which would leads to the death prematurely. The load balancing technology to alleviate network congestion, which improves network service quality and resource utilization, is very effective and widely applied to the Internet network. However the study of load balancing for the wireless sensor networks is still in its early stages.
To improve network service quality and extend the network lifetime, the wireless sensor network load balancing algorithm for data gathering is studied with the following scenarios, respectively:
①The stationary Sink node and sensor nodes with the same data generating rate(Homogeneous Network);
②The stationary Sink node and sensor nodes with the different data generating rate(Homogeneous Network);
③The moving Sink node (mobile user network).
The main research achievements are listed in the following:
1)A dynamic overlapping backoff window method is proposed.
In response to the scenarios①and②, the flooding mechanism is approached to build the data gathering tree or layer discovery. Considering the traditional back-off mechanisms in MAC layer would result in severe collisions and route circumambulating in flooding, the Dynamic Overlapping Back-off Window (DOBW) algorithm was presented. According to current rate of neighbors, sensor nodes adjust their back-off windows automatically to reduce message collision and optimize the topology. Comparing with 802.11 and 802.15.4, the simulations shown that DOBW algorithm proposed in this thesis can significantly reduce the impact of flooding and optimize the topology of the data gathering tree.
2)A load-balanced data gathering tree generation algorithm is proposed.
Though, the DOBW algorithm could construct a certain degree of load-balanced tree, the requirements of load balance is still un-achieved. Therefore, the DOBW algorithm is commonly used in the layer discovery process to avoid the message collision. The Load-balanced Data Gathering Tree based on SPT (LDGT-SPT) to establish a shortest load-balanced path tree was presented for scenario①. The LDGT-SPT is consisted of the neighbor discovery, the layer discovery, the priority principle of minimal degree and the traffic balance strategy. Simulation results show that LDGT-SPT algorithm can significantly improve the performance of the network compared with SLBT with the same network lifetime.
3) An ACO-based dynamic load balancing algorithm for data collection is proposed.
In the scenario②, due to different generating rate of sensor node data, the load-balancing data gathering tree construction methods is un-available, thus the dynamic load balancing approach is adapted. In this thesis, the Load-balanced Data Gathering algorithm based on Ant Colony Optimization (LDG-ACO) was presented. In order to achieve dynamic load balancing, LDG-ACO algorithm classifies ants to have different functions, taking the node load information as inspiration factor, making ants with the load-sensing function and working according to the principle of lower pheromone higher probability. Simulation results show that compared with the ACO, SLBT and DLBT algorithm, LDG-ACO algorithm presented in this thesis has increased markedly in network performance and network service life.
4)A dynamic load balancing algorithm supporting mobile Sink for data collection is proposed.
In response to the scenario③, the Mobile Sink led to frequent route changes and link interrupt problem, a mobile Sink dynamic load-balancing algorithm for data collection (LDG-MS) is presented to solve these problems. Referring from the swarm intelligence algorithm, LDG-MS defines two simple rules to describe the data forwarding. The problem how to choose next hop is described as a multi-objective programming. Furtherly, the evaluation method of weighting distance is used in the multi-objective programming. To solve link-break problem, a method of the power control for the Sink beacon messages was proposed. Simulation results show that, compared with SINK_CLAIM, SLM algorithm, LDG-MS algorithm presented in this thesis in network performance and network lifetime has increased profoundly.
引文
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