无线传感器网络时空查询处理技术研究
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摘要
无线传感器网络能够实时地感知、收集、处理部署区域内监控对象的各种信息，利用无线通信将其返回至基站供用户查询和分析。它具有覆盖区域广、监测精度高等优点，在战场监测、医疗卫生、交通控制等领域有着广泛的应用前景，近年来成为研究热点。不同于传统的网络，无线传感器网络在计算、存储、能量、通信带宽等方面有诸多限制。传感器网络部署后，环境噪声、通信干扰、硬件故障均不可控。这些因素导致传感器网络应用开发十分复杂和困难。如何屏蔽传感器网络的复杂性，降低传感器网络应用开发的难度，是目前亟待解决的问题。
考虑到无线传感器网络是以数据为中心的网络，用户使用它的主要目的是查询其感知到的具有时间和空间属性的数据。本文将无线传感器网络整体看成一个分布式的时空数据库，研究无线传感器网络环境下的时空数据查询处理技术，设计并实现无线传感器网络时空数据查询系统，以有效地管理和查询其感知到的时空数据，从而简化无线传感器网络应用的开发。本文的研究成果包含以下几个方面：
(1)对于静态无线传感器网络，现有时空范围查询处理算法将全网络或查询区域中的节点组织成一棵路由树，查询区域内的节点通过该路由树将其查询结果返回给用户。采用单棵路由树会使得查询结果返回Sink节点的路径过长，导致能耗较大。证明了在绝大多数情况下，多路由树在能耗方面优于单路由树。设计了一种在查询区域内构造多棵路由树的协议，并基于该协议提出了一种低能耗的无线传感器网络时空范围查询处理算法E2STA (Energy-EfficientSpatio-Temporal Window Query Processing Algorithm for Wireless Sensor Networks)。实验结果表明，E2STA在能量消耗方面优于现有的算法。
(2)提出了一个高效的动态无线传感器网络时空范围查询处理框架EST(EfficientSpatio-Temporal Query Processing Framework for Wireless Sensor Networks)。它包含三个阶段：查询区域划分、查询消息分发、感知数据收集。通过查询区域划分，查询区域内节点的感知数据通过不同的转发路径返回，减少了网络中的“热点”；提出了一种基于位置路由的查询消息组播协议，并给出了一种基于路线的查询分发和感知数据收集协议，通过调度查询区域内的部分节点广播查询消息，减少了分发查询消息的能耗；感知数据利用位置路由协议直接返回至基站，减少了收集查询结果的能耗。实验结果表明，EST在能量消耗、网络生命周期方面均优于现有的算法。
(3)现有无线传感器网络环境下的空间范围聚集查询和K近邻查询处理算法能耗大,且当节点失效时查询处理过程易被中断，无法返回查询结果。给出了一种基于查询区域划分的容忍节点失效和能耗优化方案。查询区域被划分为若干个查询子区域。当节点失效时，失效节点所在查询子区域中的未失效节点恢复查询处理过程，减少了算法因节点失效而中断的概率。通过推导空间范围聚集查询和K近邻查询的能耗公式得到：在满足无线通信约束条件的前提下，查询子区域面积越大则总能耗越少。基于该结论，提出了最大化查询子区域面积的查子区域划分算法，降低了算法的总能量。
(4)设计并实现了一个具有动态可扩充能力的无线传感器网络时空查询处理系统SensorMapReduce。它由基站端的查询编译器和节点端的虚拟机两部分组成。提出了一个时空查询统一处理框架，将各种不同的时空查询抽象为四个基本操作：Map、Reduce、GetNextClusterNode、GetNextClusterShape。查询编译器将用户提交的各种时空查询编译成相应的Map、Reduce等代码，发送至节点虚拟机上解释执行。SensorMapReduce提供了声明性的时空查询语言，以屏蔽底层分布式查询处理的复杂性。通过扩充查询编译器，节点端程序无需改变，SensorMapReduce即可支持其他种类的查询，降低了节点重编程的代价。
Wireless sensor networks (WSNs) can be used to sense, collect and process the information aboutthe monitored objects in the area of deployment. WSNs have a wide coverage area and high precisionof information. It has become a hot spot in recent years for its broad prospects in applications ofnational defense, environment monitoring, medical services, transportation control and etc. Unliketraditional networks, WSNs have many limitations in terms of computing, storage, energy,communication bandwidth, reliability, fault tolerance. After the deployment of sensor networks,ambient noise, communication interference and hardware failures are not predictable and controllable.These factors make the development of WSN application to be very complex and difficult. How toshield the complexity of WSNs and reduce the difficulty of development of sensor network applicationsis an urgent problem to be solved.
WSNs are data-centric networks. The main purpose of using it is to query the spatio-temporalsensor readings. We view the whole sensor network as a distributed spatio-temporal database, and studythe spatio-temporal query processing technology in WSNs. We designed a spatio-temporal queryprocessing system for WSNs to manage and query the spatio-temporal data sensed. Our final aims is toshields the complexity and difficulty of development of sensor network applications. The maincontributions of this dissertation are summarized as follows:
(1) In static wireless sensor networks, most of the existing spatio-temporal window queryprocessing algorithms organized all the nodes in the whole network or the nodes in the query area intoa single routing tree. The sensor readings of the nodes in the query area are sent back to the sink guidedby the routing tree. We point out that the path along which the query results are sent back to the sink isfairly long when a single routing tree is adopted, which leads to a large amount of energy consumption.Organizing the nodes in the query area into multiple routing trees can avoid this problem. Based on theabove findings, we design a protocol of constructing multiple routing trees for the nodes in the queryarea, and propose an energy-efficient spatio-temporal query processing algorithm called E2STA.Theoretical and experimental results show that the proposed algorithm based on multiple routing treesoutperforms the existing algorithms based on one single routing tree in terms of energy consumption.
(2) We propose an efficient spatio-temporal query processing framework for dynamic wirelesssensor networkscalled EST in this paper. It consists of three stages: dividing query area, distributingquery message and collecting sensor readings. Through dividing query area, the sensory data within thequery area been returned through a number of different data forwarding paths, which reduces thenetwork "hot spots". We also proposed a geographic routing based query message multicast protocoland an itinerary based data collection protocol, which saves energy of distributing query messages byscheduling some nodes with the query area broadcast query messages. The proposed data collectionprotocol returns the query results back to the sink through geographic routing protocol, which reducesthe number of data forwarding. Experimental results show that EST outperforms the existingalgorithms in terms of energy consumption and lifetime.
(3) The energy consumption of existing spatial window query processing algorithms and k nearestneighbor query processing algorithms in wireless sensor networks is fairy high. When some sensornodes fail, the query process of these algorithms is very likely to be interrupted and unable to returnquery results. We propose a node failure tolerant and energy optimization method based on dividingquery area. The query area is divided into several sub-regions. While a node fails, the alive nodes in the sub-region where the failed node resides in recover the interrupted process of query processing, whichreduces the outage probability of query processing due to node failures. We prove that largersub-regions result in less energy consumption while meeting the constraints of wireless communication.Based on this theory, we propose a query area dividing algorithm which maximizes the area of eachsub-region to reduce the total energy consumption.
(4) We design and implement SensorMapReduce: a dynamically scalable spatio-temporal queryprocessing system in wireless sensor networks. It is composed of the query compiler at the base stationend and the virtual machine at the node side. A unified spatio-temporal query processing framework isproposed. It abstract various spatio-temporal query to four basis operation: Map, Reduce,GetNextClusterNode and GetNextClusterShape. The query compiler compiles the submitted query intoMap and Reduce code which are sent to run in the virtual machine of sensor nodes. SensorMapReduceprovides declarative spatio-temporal query language to shield the complexity of the underlyingdistributed query processing. Through extending the query compiler, SensorMapReduce can supportother types of queries without changing the node-side code, which reduces the cost of nodereprogramming.
考虑到无线传感器网络是以数据为中心的网络，用户使用它的主要目的是查询其感知到的具有时间和空间属性的数据。本文将无线传感器网络整体看成一个分布式的时空数据库，研究无线传感器网络环境下的时空数据查询处理技术，设计并实现无线传感器网络时空数据查询系统，以有效地管理和查询其感知到的时空数据，从而简化无线传感器网络应用的开发。本文的研究成果包含以下几个方面：
(1)对于静态无线传感器网络，现有时空范围查询处理算法将全网络或查询区域中的节点组织成一棵路由树，查询区域内的节点通过该路由树将其查询结果返回给用户。采用单棵路由树会使得查询结果返回Sink节点的路径过长，导致能耗较大。证明了在绝大多数情况下，多路由树在能耗方面优于单路由树。设计了一种在查询区域内构造多棵路由树的协议，并基于该协议提出了一种低能耗的无线传感器网络时空范围查询处理算法E2STA (Energy-EfficientSpatio-Temporal Window Query Processing Algorithm for Wireless Sensor Networks)。实验结果表明，E2STA在能量消耗方面优于现有的算法。
(2)提出了一个高效的动态无线传感器网络时空范围查询处理框架EST(EfficientSpatio-Temporal Query Processing Framework for Wireless Sensor Networks)。它包含三个阶段：查询区域划分、查询消息分发、感知数据收集。通过查询区域划分，查询区域内节点的感知数据通过不同的转发路径返回，减少了网络中的“热点”；提出了一种基于位置路由的查询消息组播协议，并给出了一种基于路线的查询分发和感知数据收集协议，通过调度查询区域内的部分节点广播查询消息，减少了分发查询消息的能耗；感知数据利用位置路由协议直接返回至基站，减少了收集查询结果的能耗。实验结果表明，EST在能量消耗、网络生命周期方面均优于现有的算法。
(3)现有无线传感器网络环境下的空间范围聚集查询和K近邻查询处理算法能耗大,且当节点失效时查询处理过程易被中断，无法返回查询结果。给出了一种基于查询区域划分的容忍节点失效和能耗优化方案。查询区域被划分为若干个查询子区域。当节点失效时，失效节点所在查询子区域中的未失效节点恢复查询处理过程，减少了算法因节点失效而中断的概率。通过推导空间范围聚集查询和K近邻查询的能耗公式得到：在满足无线通信约束条件的前提下，查询子区域面积越大则总能耗越少。基于该结论，提出了最大化查询子区域面积的查子区域划分算法，降低了算法的总能量。
(4)设计并实现了一个具有动态可扩充能力的无线传感器网络时空查询处理系统SensorMapReduce。它由基站端的查询编译器和节点端的虚拟机两部分组成。提出了一个时空查询统一处理框架，将各种不同的时空查询抽象为四个基本操作：Map、Reduce、GetNextClusterNode、GetNextClusterShape。查询编译器将用户提交的各种时空查询编译成相应的Map、Reduce等代码，发送至节点虚拟机上解释执行。SensorMapReduce提供了声明性的时空查询语言，以屏蔽底层分布式查询处理的复杂性。通过扩充查询编译器，节点端程序无需改变，SensorMapReduce即可支持其他种类的查询，降低了节点重编程的代价。
Wireless sensor networks (WSNs) can be used to sense, collect and process the information aboutthe monitored objects in the area of deployment. WSNs have a wide coverage area and high precisionof information. It has become a hot spot in recent years for its broad prospects in applications ofnational defense, environment monitoring, medical services, transportation control and etc. Unliketraditional networks, WSNs have many limitations in terms of computing, storage, energy,communication bandwidth, reliability, fault tolerance. After the deployment of sensor networks,ambient noise, communication interference and hardware failures are not predictable and controllable.These factors make the development of WSN application to be very complex and difficult. How toshield the complexity of WSNs and reduce the difficulty of development of sensor network applicationsis an urgent problem to be solved.
WSNs are data-centric networks. The main purpose of using it is to query the spatio-temporalsensor readings. We view the whole sensor network as a distributed spatio-temporal database, and studythe spatio-temporal query processing technology in WSNs. We designed a spatio-temporal queryprocessing system for WSNs to manage and query the spatio-temporal data sensed. Our final aims is toshields the complexity and difficulty of development of sensor network applications. The maincontributions of this dissertation are summarized as follows:
(1) In static wireless sensor networks, most of the existing spatio-temporal window queryprocessing algorithms organized all the nodes in the whole network or the nodes in the query area intoa single routing tree. The sensor readings of the nodes in the query area are sent back to the sink guidedby the routing tree. We point out that the path along which the query results are sent back to the sink isfairly long when a single routing tree is adopted, which leads to a large amount of energy consumption.Organizing the nodes in the query area into multiple routing trees can avoid this problem. Based on theabove findings, we design a protocol of constructing multiple routing trees for the nodes in the queryarea, and propose an energy-efficient spatio-temporal query processing algorithm called E2STA.Theoretical and experimental results show that the proposed algorithm based on multiple routing treesoutperforms the existing algorithms based on one single routing tree in terms of energy consumption.
(2) We propose an efficient spatio-temporal query processing framework for dynamic wirelesssensor networkscalled EST in this paper. It consists of three stages: dividing query area, distributingquery message and collecting sensor readings. Through dividing query area, the sensory data within thequery area been returned through a number of different data forwarding paths, which reduces thenetwork "hot spots". We also proposed a geographic routing based query message multicast protocoland an itinerary based data collection protocol, which saves energy of distributing query messages byscheduling some nodes with the query area broadcast query messages. The proposed data collectionprotocol returns the query results back to the sink through geographic routing protocol, which reducesthe number of data forwarding. Experimental results show that EST outperforms the existingalgorithms in terms of energy consumption and lifetime.
(3) The energy consumption of existing spatial window query processing algorithms and k nearestneighbor query processing algorithms in wireless sensor networks is fairy high. When some sensornodes fail, the query process of these algorithms is very likely to be interrupted and unable to returnquery results. We propose a node failure tolerant and energy optimization method based on dividingquery area. The query area is divided into several sub-regions. While a node fails, the alive nodes in the sub-region where the failed node resides in recover the interrupted process of query processing, whichreduces the outage probability of query processing due to node failures. We prove that largersub-regions result in less energy consumption while meeting the constraints of wireless communication.Based on this theory, we propose a query area dividing algorithm which maximizes the area of eachsub-region to reduce the total energy consumption.
(4) We design and implement SensorMapReduce: a dynamically scalable spatio-temporal queryprocessing system in wireless sensor networks. It is composed of the query compiler at the base stationend and the virtual machine at the node side. A unified spatio-temporal query processing framework isproposed. It abstract various spatio-temporal query to four basis operation: Map, Reduce,GetNextClusterNode and GetNextClusterShape. The query compiler compiles the submitted query intoMap and Reduce code which are sent to run in the virtual machine of sensor nodes. SensorMapReduceprovides declarative spatio-temporal query language to shield the complexity of the underlyingdistributed query processing. Through extending the query compiler, SensorMapReduce can supportother types of queries without changing the node-side code, which reduces the cost of nodereprogramming.
引文
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