无线传感器网络聚集查询关键技术研究
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摘要
无线传感器网络是目前IT研究领域中的热点研究之一。面向大规模、资源有限的无线传感器网络,如何进行节能的,可扩展的,可容错的数据聚集查询是我们面临的一大挑战。聚集查询是传感器网络查询处理中的关键查询之一。本文针对WSN(Wireless Sensor Networks,WSN)中聚集查询技术的通信量、可扩展性、容错性等问题,对不重复记录值查询、中位数查询、均值查询、以及移动环境下的聚集查询等关键技术进行了研究,对于推进无线传感器网络高效聚集查询的研究和实用化具有一定的理论意义和应用价值。
本文首先对聚集查询中不重复记录值查询进行了研究。由于传感器网络在节能,网络规模,容错性等方面的要求,集中式聚集算法已经非常不适合用于这种环境下。提出一种基于WSN的不重复记录值近似算法。该算法能充分利用网内数据聚合和多路径路由技术来减少网络能耗,同时能避免重复计数,提高算法的容错性。在算法中,网络中各节点产生其摘要数据-子FM (Flajolet Martin,FM)序列,然后,各FM序列经过网内数据聚合以及层层传递后,最终汇集到根节点形成全网的FM序列,最后在汇聚节点使用这个远小于全网数据集规模的、可用于代表全网数据集结构的FM序列,迅速获得不重复记录值的近似结果,从而避免了将各传感器节点的所有数据都传输至根节点。仿真结果显示该算法能耗低、容错能力强、误差范围可控,能有效地延长网络的生存期。
由于节点失效、通信失败以及重复计数等问题都将导致对副本敏感的聚集查询算法无法得到正确的聚集值。为了减少误差,降低能耗,提出了两种中位数查询算法-中位数查询抽样算法和中位数查询近似算法。中位数查询抽样算法结合抽样理论和网内数据聚合技术能得到中位数的近似值,能较大地减少网络通信量。在中位数查询近似算法中,各节点分别统计出每个感知值出现的不重复次数,并抽取节点的K%个最常用感知值作为子样本集;将子样本集传递给上一层节点,经过层层传递,最终在根节点形成全网的样本集;将这个全网样本集排序后获得中位数的近似结果。实验结果显示所设计的两种算法能较大地减少网络通信量、提高系统的健壮性。
目前几乎所有的聚集算法都要通过数据融合以及远距离通信等方式收集全局的节点信息,然后聚集到一个单一的管理节点进行处理,因此这些算法存在着扩展性差、能耗较高等缺点。本文提出一种基于无线传感器网络的分布式均值查询聚集算法,该算法只需在局部范围内计算结果而无需收集全局的节点信息,也不必完成数据融合以及远距离通信等任务,还可以根据当前查询结果自适应的调整查询范围,是一种分布式、低能耗、扩展性强、能自适应查询范围的聚集查询算法。
本文针对LEACH(Low Energy Adaptive Clustering Hierarchy,LEACH)算法没有考虑簇头能量和地理位置的缺点,以及现有的集中式分簇算法虽然考虑了节点的地理位置,但只适用于静态网络环境下,提出一种适用于移动环境的无线传感器网络分布式分簇算法。算法根据节点的剩余能量以及与动态变化的簇心之间的距离来挑选簇头,从而使网络能量均匀消耗。与集中式算法不同,该算法只需和部分邻居节点交换阈值信息而无需收集全局节点的位置信息,也不必完成远距离通信等任务。仿真结果表明,移动环境下,该算法具有良好的负载平衡性能和较小的协议开销,与LEACH算法相比,能有效减少能量消耗。
最后,为了更好的研究无线传感器网络的各方面特性,分析算法在实际系统中的性能,设计并实现了一个基于WSN室内温度监测系统。
It is currently a hot topic to study the Wireless Sensor Networks (WSN) that is self-organized distributed those randomly exiting sensors. In the emerging area of wireless sensor system, a significant challenge is to develop scalable, energy efficiency, fault-tolerant algorithms to extract useful information from the data the sensors collect. Aggregate query is one of the key operations in query processing for wireless sensor networks (WSN). Aiming at scalability, robust and less transmission cost on high efficient aggregation query, our works present algrithems to several key queries of distinct count query, median query, average query, and aggregation query in mobile environments, which have academic and practical value for advancing the theory and practicability of high efficient aggregation queries in WSN.
This dissertation firstly studied the distinct count query in wireless sensor networks. Due to power, network size and robust constraint, centralized algorithms are generally impractical, so many systems used in-network aggregation and multi-path routing methods to reduce network traffic and increased fault–tolerant in these environments. To conserve energy and to avoid double-counting, an approximate algorithm for distinct count query (AADC) based on WSN was proposed. In AADC, each node in WSN created a new Flajolet Martin (FM) subsequence summarizing its own observed values and the received FM subsequences from child nodes, and then broadcasted its subsequence to the parents. Finally, these FM subsequences were combined to a single FM sequence in the root node which its data structure was far smaller than the size of the whole data set. The approximate value for distinct count query could be introduced from the FM sequence quickly. Analytical and experimental results show that the proposed algorithm has the advantages of low power consumption, strong fault-tolerant capability, adjusting error range, and is able to significantly prolong system life.
Duplicate-sensitive aggregates can’t be computed exactly because of node and communication failures or double-counting. To avid this expense, two approximate algorithms for median query were proposed. Firstly, a sampling algorithm for median query (SAMQ) was proposed, which used sampling theory and in-network aggregation technology to produce approximate results with low communication. Secondly, an approximate algorithm for median query (AAMQ) was proposed. In AAMQ, each node estimated the number of times a value appeared, and created a subsequence samples which get k percent of the most commonly used values form its own observed values, and then broadcasted its subsequence to the parents. Finally, these samples were combined to a single sample across the network in the root node. The approximate value for median query could be introduced quickly from the sample across the network. Analytical and experimental results show that the proposed algorithm can greatly reduce the communication, and has strong fault-tolerant capability.
At the present time,almost all the aggregate algorithm need collect data about the global status of the system, which required data fusion and long-distance communication .Therefore these algorithm have disadvantages of bad scalability,high power consumption. A distributed aggregate algorithm for average query (DAAQ) based on WSN was proposed. In DAAQ, the computation process of each node in WSN used the information gathered from just a few nearby neighbors. The algorithm offered a fundamentally distributed solution to analyze data locally without necessarily collected the information of whole nodes to a single central site, and did not require data fusion and long-distance communication. The algorithm could adaptively adjust query range according to query results as well. Experimental results show that the proposed algorithm has the advantages of good scalability, low power consumption, and is able to significantly prolong system life.
While there are some advantages to using LEACH distributed cluster formation algorithm, this protocol offers no guarantee about the placement and energy of node. A distributed clustering algorithm using local threshold (DCLT) base on WSN in mobile environments was proposed. In DCLT, cluster-heads are elected based on the residual energy of node and the distance between node and variable centroid of the cluster, which can evenly distribute the energy load among all the nodes. The algorithm offered a fundamentally distributed solution to analyze data locally without necessarily collected the information of whole nodes, and did not require long-distance communication. The results of simulation indicate that the DCLT can provide better load-balancing of cluster heads and less protocol overhead. Comparing with LEACH protocol, DCLT saves energy greatly so that the network lifetime was prolonged.
Finally, for better research the characters of wireless sensor networks and analysis the performance of the proposed algorithms in real system; we implemented an indoor temperature monitoring systems for wireless sensor networks.
本文首先对聚集查询中不重复记录值查询进行了研究。由于传感器网络在节能,网络规模,容错性等方面的要求,集中式聚集算法已经非常不适合用于这种环境下。提出一种基于WSN的不重复记录值近似算法。该算法能充分利用网内数据聚合和多路径路由技术来减少网络能耗,同时能避免重复计数,提高算法的容错性。在算法中,网络中各节点产生其摘要数据-子FM (Flajolet Martin,FM)序列,然后,各FM序列经过网内数据聚合以及层层传递后,最终汇集到根节点形成全网的FM序列,最后在汇聚节点使用这个远小于全网数据集规模的、可用于代表全网数据集结构的FM序列,迅速获得不重复记录值的近似结果,从而避免了将各传感器节点的所有数据都传输至根节点。仿真结果显示该算法能耗低、容错能力强、误差范围可控,能有效地延长网络的生存期。
由于节点失效、通信失败以及重复计数等问题都将导致对副本敏感的聚集查询算法无法得到正确的聚集值。为了减少误差,降低能耗,提出了两种中位数查询算法-中位数查询抽样算法和中位数查询近似算法。中位数查询抽样算法结合抽样理论和网内数据聚合技术能得到中位数的近似值,能较大地减少网络通信量。在中位数查询近似算法中,各节点分别统计出每个感知值出现的不重复次数,并抽取节点的K%个最常用感知值作为子样本集;将子样本集传递给上一层节点,经过层层传递,最终在根节点形成全网的样本集;将这个全网样本集排序后获得中位数的近似结果。实验结果显示所设计的两种算法能较大地减少网络通信量、提高系统的健壮性。
目前几乎所有的聚集算法都要通过数据融合以及远距离通信等方式收集全局的节点信息,然后聚集到一个单一的管理节点进行处理,因此这些算法存在着扩展性差、能耗较高等缺点。本文提出一种基于无线传感器网络的分布式均值查询聚集算法,该算法只需在局部范围内计算结果而无需收集全局的节点信息,也不必完成数据融合以及远距离通信等任务,还可以根据当前查询结果自适应的调整查询范围,是一种分布式、低能耗、扩展性强、能自适应查询范围的聚集查询算法。
本文针对LEACH(Low Energy Adaptive Clustering Hierarchy,LEACH)算法没有考虑簇头能量和地理位置的缺点,以及现有的集中式分簇算法虽然考虑了节点的地理位置,但只适用于静态网络环境下,提出一种适用于移动环境的无线传感器网络分布式分簇算法。算法根据节点的剩余能量以及与动态变化的簇心之间的距离来挑选簇头,从而使网络能量均匀消耗。与集中式算法不同,该算法只需和部分邻居节点交换阈值信息而无需收集全局节点的位置信息,也不必完成远距离通信等任务。仿真结果表明,移动环境下,该算法具有良好的负载平衡性能和较小的协议开销,与LEACH算法相比,能有效减少能量消耗。
最后,为了更好的研究无线传感器网络的各方面特性,分析算法在实际系统中的性能,设计并实现了一个基于WSN室内温度监测系统。
It is currently a hot topic to study the Wireless Sensor Networks (WSN) that is self-organized distributed those randomly exiting sensors. In the emerging area of wireless sensor system, a significant challenge is to develop scalable, energy efficiency, fault-tolerant algorithms to extract useful information from the data the sensors collect. Aggregate query is one of the key operations in query processing for wireless sensor networks (WSN). Aiming at scalability, robust and less transmission cost on high efficient aggregation query, our works present algrithems to several key queries of distinct count query, median query, average query, and aggregation query in mobile environments, which have academic and practical value for advancing the theory and practicability of high efficient aggregation queries in WSN.
This dissertation firstly studied the distinct count query in wireless sensor networks. Due to power, network size and robust constraint, centralized algorithms are generally impractical, so many systems used in-network aggregation and multi-path routing methods to reduce network traffic and increased fault–tolerant in these environments. To conserve energy and to avoid double-counting, an approximate algorithm for distinct count query (AADC) based on WSN was proposed. In AADC, each node in WSN created a new Flajolet Martin (FM) subsequence summarizing its own observed values and the received FM subsequences from child nodes, and then broadcasted its subsequence to the parents. Finally, these FM subsequences were combined to a single FM sequence in the root node which its data structure was far smaller than the size of the whole data set. The approximate value for distinct count query could be introduced from the FM sequence quickly. Analytical and experimental results show that the proposed algorithm has the advantages of low power consumption, strong fault-tolerant capability, adjusting error range, and is able to significantly prolong system life.
Duplicate-sensitive aggregates can’t be computed exactly because of node and communication failures or double-counting. To avid this expense, two approximate algorithms for median query were proposed. Firstly, a sampling algorithm for median query (SAMQ) was proposed, which used sampling theory and in-network aggregation technology to produce approximate results with low communication. Secondly, an approximate algorithm for median query (AAMQ) was proposed. In AAMQ, each node estimated the number of times a value appeared, and created a subsequence samples which get k percent of the most commonly used values form its own observed values, and then broadcasted its subsequence to the parents. Finally, these samples were combined to a single sample across the network in the root node. The approximate value for median query could be introduced quickly from the sample across the network. Analytical and experimental results show that the proposed algorithm can greatly reduce the communication, and has strong fault-tolerant capability.
At the present time,almost all the aggregate algorithm need collect data about the global status of the system, which required data fusion and long-distance communication .Therefore these algorithm have disadvantages of bad scalability,high power consumption. A distributed aggregate algorithm for average query (DAAQ) based on WSN was proposed. In DAAQ, the computation process of each node in WSN used the information gathered from just a few nearby neighbors. The algorithm offered a fundamentally distributed solution to analyze data locally without necessarily collected the information of whole nodes to a single central site, and did not require data fusion and long-distance communication. The algorithm could adaptively adjust query range according to query results as well. Experimental results show that the proposed algorithm has the advantages of good scalability, low power consumption, and is able to significantly prolong system life.
While there are some advantages to using LEACH distributed cluster formation algorithm, this protocol offers no guarantee about the placement and energy of node. A distributed clustering algorithm using local threshold (DCLT) base on WSN in mobile environments was proposed. In DCLT, cluster-heads are elected based on the residual energy of node and the distance between node and variable centroid of the cluster, which can evenly distribute the energy load among all the nodes. The algorithm offered a fundamentally distributed solution to analyze data locally without necessarily collected the information of whole nodes, and did not require long-distance communication. The results of simulation indicate that the DCLT can provide better load-balancing of cluster heads and less protocol overhead. Comparing with LEACH protocol, DCLT saves energy greatly so that the network lifetime was prolonged.
Finally, for better research the characters of wireless sensor networks and analysis the performance of the proposed algorithms in real system; we implemented an indoor temperature monitoring systems for wireless sensor networks.
引文
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