多汇聚节点无线传感器网络关键技术研究
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摘要
作为物联网的重要组成部分，无线传感器网络因其具有良好的信息感知及数据交互能力，近年来格外受到研究者们的关注与重视。无线传感器网络由大量低功耗、低成本、多功能的无线传感器节点组成，这些传感节点既能够进行数据采集及信息处理，又可以通过无线信道进行通信互联，因此由它们组成的无线传感器网络便可通过无线通信技术及自组织方式形成一种面向任务的网络系统。但由于在传统无线传感器网络中密集部署的传感节点需要通过网络中唯一的汇聚节点(sink)才可将数据信息传送至用户终端或控制中心，而这种多对一的传输模式使无线传感器网络在能耗均衡性、可靠性等诸多方面均面临着很大的问题，同时又因无线传感器网络应用领域的不断拓展，也使得单汇聚节点结构更加不能满足无线传感器网络快速发展的要求。因此，本文通过在网络中增加汇聚节点个数的方式来解决上述问题，从而使无线传感器网络的运行变得更高效、更稳定、更健壮，并且便于管理。本文针对多汇聚节点的无线传感器网络开展研究，分别从路由协议设计、多汇聚节点的重定位技术及数据融合策略等三方面进行了深入研究并提出了相关算法。本文的研究成果主要包括以下三个方面：
第一，本文提出了一种基于模糊综合评判理论的最优路由选择算法ORFCE。该算法通过对传感节点到汇聚节点的跳数、路径最小剩余能量与最小平均链路质量三方面因素进行模糊综合评判的方式，提供了传感节点到对应汇聚节点的多路径、层次型、主动与按需混合并考虑QoS的分布式最优路由选择，在赋予路由选择以人性化思考的同时，可保证无线传感器网络具有更长的网络生存期、更高的数据包交付率和更少的路由控制信息。仿真实验结果表明，与未使用模糊理论及未对影响因素进行全面考虑的其他多汇聚节点路由协议相比，本文所提出的ORFCE算法可平均延长约2.8倍的网络生存时间，提高约14%的数据包交付率，并减少了约37%在路由过程所产生的控制信息数量。
第二，本文提出了一种基于质心原理的多汇聚节点重定位算法EEMSR。该算法通过将汇聚节点的一跳邻居传感节点视作质点系、其向汇聚节点传输的数据包数量作为质点质量的方式，利用质心位置的计算方法将多个汇聚节点当做各自质点系的质心用协作移动的方式逐步逼近并最终定位至各自的最优位置，且汇聚节点的当前最优位置可因网络运行状态的不断变化而进行相应的调整，从而始终保证无线传感器网络在传输数据信息时可经历更少的平均跳数及消耗更少的平均节点能量。仿真实验结果表明，与汇聚节点固定不动时相比，本文所提出的EEMSR算法可平均延长约69%的网络生存时间，提高约5%的数据包交付率；而与采用其他策略的汇聚节点重定位算法相比，该算法也能平均延长约18%的网络使用寿命，并提升约6%的数据信息传输的成功率。
第三，本文提出了一种可权衡传感节点平均能耗及数据传输平均延迟的数据融合算法ECLT。该算法首先使用二级模糊综合评判方法对原有最优路由信息利用传感节点的一跳邻居节点当前一段时间内所转发的数据包个数进行有利于数据融合的相关调整，而使新的数据转发路径在很好的均衡网络中各节点能耗并有效提高数据传输可靠性的同时，通过增加原有路径间交叠的方式来高效的提高数据融合度；其次，该算法还可利用传感节点的自身状态来动态调整其当前的融合等待时间，使该数据融合定时机制(timing)既能有效的均衡网络中各传感节点的平均能耗，又可很好的降低网络中的平均数据延迟。仿真实验结果表明，与未考虑数据融合的算法相比，本文所提出的ECLT算法可平均延长约1倍的网络生存时间，但会降低约3%的数据包交付率；而与采用其他数据融合机制与融合等待定制策略的算法相比，该算法也能平均延长约10%的网络使用寿命，并提高约6%的数据包交付率、降低约6%的平均传输延迟及提升约16%的数据融合度。
As a significant component of Internet of Things, Wireless Sensor Network(WSN) with its advanced information perception, data interaction and othercapabilities has attracted extensive attention of the researchers in recent years. WSNconsists of a large number of low-power, low-cost, and multi-functional microsensors which provide data acquisition and information processing service, and alsobe able to communicate with each other through the wireless channels. Hence, it canform a task-oriented network system on the basis of wireless communicationtechnology and self-organization method. However, densely deployed sensor nodesneed to transmit the data information to the user terminal or the control center throughthe sole sink in the traditional wireless sensor networks. This many-to-onetransmission mode put WSNs at enormous risks in many aspects such as energybalancing and reliability. Meanwhile, with the widespread applications of WSN, thesingle sink node structure can not meet the demand for rapid development of WSNs.Therefore, this paper explains how to solve the above problems by means ofincreasing the number of sink nodes, so that WSNs becomes more efficient, morestable, more robust, and easier to manage. This paper focusing on multi-sink nodeWSNs performs in-depth study from the routing protocol design, multiple sink nodesre-positioning technology and data aggregation strategies respectively, and alsoproposes relative algorithms. Achievements are summarized as follows:
First, this paper proposes an optimal routing selection algorithm based on fuzzycomprehensive evaluation theory. By fuzzy comprehensive evaluating three factors,hops of sensor node to the sink node, the minimum residual energy of the path, aswell as the minimum average link quality, this algorithm provides a distributedoptimal multi-path routing corresponding to the sink node which is hierarchical,active and on-demand mixing and QoS in view. In consideration of humane thinking,routing in wireless sensor networks can provide a longer network lifetime, higherpacket delivery rate and less routing control information. Hence, in comparison with other multi-path routing protocols which don’t apply the fuzzy theory and takecomprehensive considerations to the effect factors, this algorithm will extend about2.8times of the lifetime of network and increase about14percent of the packetdelivery rate on average. But at the same time, it also reduces about37percent of thecontrol information during routing process.
Secondly, this paper presents a relocation algorithm based on the centroidprinciple of multi-sink node. The algorithm utilizes the methods that make one-hopneighbors of the sink node as the system of particles and the number of packetstransmitted to the sink node as the particle quality, and regards the multiple sink nodeas the centroid of each system of particles by means of calculating the centroidposition in order to collaboratively approach and pinpoint the their correspondingoptimal position. In addition, the optimal position of the sink node can be adjustedaccording to the changes of network state, thus it can guarantee smaller averagenumber of hops and fewer average node energy. Therefore, compared with thealgorithms in which sinks are fixed, the relocation algorithm based on the centroidprinciple can extend about69percent of the network lifetime and improve about5percent of the packet delivery rate on average. It also has advantages over other sinkrelocation algorithms in the network lifetime by about18percent and enhances about6percent of the success rate of data transmission on average.
Third, this paper proposes a data aggregation algorithm which is able to balancethe average energy consumption of sensor nodes and average data transmission delay.It first makes use of secondary fuzzy comprehensive evaluation to make relevantadjustment about the number of packets forward in a period through the originaloptimal routing to fit for data aggregation. As a result, new data forwarding paths canmore efficiently balance energy consumption of the sensor node and improve datatransmission reliability, as well as improving data aggregation performance byincreasing the overlap of the original paths. Moreover, the algorithm also can utilizethe states of the sensor nodes to dynamically adjust its current wait time ofaggregation, thus the data aggregation timing mechanism can effectively balance theaverage energy consumption of each sensor node, and reduce average data delay in network. Therefore, compared with algorithms that not involve data aggregation, itcan extend about one times of the network lifetime, but will reduce about3percent ofthe network packet delivery rate on average. It can also achieve better lifetime thanother data aggregation algorithms by about10percent, and improve about6percentof the packet delivery rate and about16percent of the aggregation rate as well asreducing about6percent of the average transmission delay.
第一，本文提出了一种基于模糊综合评判理论的最优路由选择算法ORFCE。该算法通过对传感节点到汇聚节点的跳数、路径最小剩余能量与最小平均链路质量三方面因素进行模糊综合评判的方式，提供了传感节点到对应汇聚节点的多路径、层次型、主动与按需混合并考虑QoS的分布式最优路由选择，在赋予路由选择以人性化思考的同时，可保证无线传感器网络具有更长的网络生存期、更高的数据包交付率和更少的路由控制信息。仿真实验结果表明，与未使用模糊理论及未对影响因素进行全面考虑的其他多汇聚节点路由协议相比，本文所提出的ORFCE算法可平均延长约2.8倍的网络生存时间，提高约14%的数据包交付率，并减少了约37%在路由过程所产生的控制信息数量。
第二，本文提出了一种基于质心原理的多汇聚节点重定位算法EEMSR。该算法通过将汇聚节点的一跳邻居传感节点视作质点系、其向汇聚节点传输的数据包数量作为质点质量的方式，利用质心位置的计算方法将多个汇聚节点当做各自质点系的质心用协作移动的方式逐步逼近并最终定位至各自的最优位置，且汇聚节点的当前最优位置可因网络运行状态的不断变化而进行相应的调整，从而始终保证无线传感器网络在传输数据信息时可经历更少的平均跳数及消耗更少的平均节点能量。仿真实验结果表明，与汇聚节点固定不动时相比，本文所提出的EEMSR算法可平均延长约69%的网络生存时间，提高约5%的数据包交付率；而与采用其他策略的汇聚节点重定位算法相比，该算法也能平均延长约18%的网络使用寿命，并提升约6%的数据信息传输的成功率。
第三，本文提出了一种可权衡传感节点平均能耗及数据传输平均延迟的数据融合算法ECLT。该算法首先使用二级模糊综合评判方法对原有最优路由信息利用传感节点的一跳邻居节点当前一段时间内所转发的数据包个数进行有利于数据融合的相关调整，而使新的数据转发路径在很好的均衡网络中各节点能耗并有效提高数据传输可靠性的同时，通过增加原有路径间交叠的方式来高效的提高数据融合度；其次，该算法还可利用传感节点的自身状态来动态调整其当前的融合等待时间，使该数据融合定时机制(timing)既能有效的均衡网络中各传感节点的平均能耗，又可很好的降低网络中的平均数据延迟。仿真实验结果表明，与未考虑数据融合的算法相比，本文所提出的ECLT算法可平均延长约1倍的网络生存时间，但会降低约3%的数据包交付率；而与采用其他数据融合机制与融合等待定制策略的算法相比，该算法也能平均延长约10%的网络使用寿命，并提高约6%的数据包交付率、降低约6%的平均传输延迟及提升约16%的数据融合度。
As a significant component of Internet of Things, Wireless Sensor Network(WSN) with its advanced information perception, data interaction and othercapabilities has attracted extensive attention of the researchers in recent years. WSNconsists of a large number of low-power, low-cost, and multi-functional microsensors which provide data acquisition and information processing service, and alsobe able to communicate with each other through the wireless channels. Hence, it canform a task-oriented network system on the basis of wireless communicationtechnology and self-organization method. However, densely deployed sensor nodesneed to transmit the data information to the user terminal or the control center throughthe sole sink in the traditional wireless sensor networks. This many-to-onetransmission mode put WSNs at enormous risks in many aspects such as energybalancing and reliability. Meanwhile, with the widespread applications of WSN, thesingle sink node structure can not meet the demand for rapid development of WSNs.Therefore, this paper explains how to solve the above problems by means ofincreasing the number of sink nodes, so that WSNs becomes more efficient, morestable, more robust, and easier to manage. This paper focusing on multi-sink nodeWSNs performs in-depth study from the routing protocol design, multiple sink nodesre-positioning technology and data aggregation strategies respectively, and alsoproposes relative algorithms. Achievements are summarized as follows:
First, this paper proposes an optimal routing selection algorithm based on fuzzycomprehensive evaluation theory. By fuzzy comprehensive evaluating three factors,hops of sensor node to the sink node, the minimum residual energy of the path, aswell as the minimum average link quality, this algorithm provides a distributedoptimal multi-path routing corresponding to the sink node which is hierarchical,active and on-demand mixing and QoS in view. In consideration of humane thinking,routing in wireless sensor networks can provide a longer network lifetime, higherpacket delivery rate and less routing control information. Hence, in comparison with other multi-path routing protocols which don’t apply the fuzzy theory and takecomprehensive considerations to the effect factors, this algorithm will extend about2.8times of the lifetime of network and increase about14percent of the packetdelivery rate on average. But at the same time, it also reduces about37percent of thecontrol information during routing process.
Secondly, this paper presents a relocation algorithm based on the centroidprinciple of multi-sink node. The algorithm utilizes the methods that make one-hopneighbors of the sink node as the system of particles and the number of packetstransmitted to the sink node as the particle quality, and regards the multiple sink nodeas the centroid of each system of particles by means of calculating the centroidposition in order to collaboratively approach and pinpoint the their correspondingoptimal position. In addition, the optimal position of the sink node can be adjustedaccording to the changes of network state, thus it can guarantee smaller averagenumber of hops and fewer average node energy. Therefore, compared with thealgorithms in which sinks are fixed, the relocation algorithm based on the centroidprinciple can extend about69percent of the network lifetime and improve about5percent of the packet delivery rate on average. It also has advantages over other sinkrelocation algorithms in the network lifetime by about18percent and enhances about6percent of the success rate of data transmission on average.
Third, this paper proposes a data aggregation algorithm which is able to balancethe average energy consumption of sensor nodes and average data transmission delay.It first makes use of secondary fuzzy comprehensive evaluation to make relevantadjustment about the number of packets forward in a period through the originaloptimal routing to fit for data aggregation. As a result, new data forwarding paths canmore efficiently balance energy consumption of the sensor node and improve datatransmission reliability, as well as improving data aggregation performance byincreasing the overlap of the original paths. Moreover, the algorithm also can utilizethe states of the sensor nodes to dynamically adjust its current wait time ofaggregation, thus the data aggregation timing mechanism can effectively balance theaverage energy consumption of each sensor node, and reduce average data delay in network. Therefore, compared with algorithms that not involve data aggregation, itcan extend about one times of the network lifetime, but will reduce about3percent ofthe network packet delivery rate on average. It can also achieve better lifetime thanother data aggregation algorithms by about10percent, and improve about6percentof the packet delivery rate and about16percent of the aggregation rate as well asreducing about6percent of the average transmission delay.
引文
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