无线传感器网络中的数据压缩与数据认证研究
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摘要
无线传感器网络(Wireless Sensor Networks, WSN)潜在的广泛应用前景吸引了众多研究者,是目前一个非常活跃的研究领域。如何在资源受限、节点密集分布的WSN中实现低能耗、高安全的数据收集是研究人员需要解决的一个重要问题。由于传感器节点通常具有一定的数据处理能力,因此可以通过传感器节点间的协作,在网内对原始传感数据进行处理,再将处理结果传送到Sink节点,以减少传输数据量、降低能耗、提高带宽利用率、延长网络寿命。同时,在数据传输到Sink的过程中,要考虑数据的机密性、源认证、完整性、新鲜性等安全需求。
本文以能量、延迟、安全为目标,围绕无线传感器网络中的数据收集问题展开研究,着重研究数据采集和传输过程中数据压缩算法和数据认证机制。主要工作和贡献包括以下几个方面：
(1)针对传感器数据中的时间相关性和多属性间相关性,研究了基于小波和回归的无穷范数误差有界的数据压缩算法。主要工作包括：①提出了单属性数据的误差有界小波压缩算法(SWCEB)。通过分析一维Haar小波变换过程,设计误差树便于分析每个小波系数所影响的重构数据及带来的误差；SWCEB从整体上选择小波系数,使保留的系数个数最少且每个重构数据的误差有界。分析和实验表明,SWCEB消除了单个数据流中的时间相关性,减少了数据量。②提出了基于回归的多属性数据的误差有界小波压缩算法(MWCEB)。若单个传感器节点可以采集多种物理量,即产生多维数据流,则根据相关系数矩阵选择其中的若干个数据流作为基信号,其它数据流借助一个基用线性回归参数来表示。基数据流采用SWCEB压缩；非基数据流采用MWCEB压缩。通过调整收益界和每次处理的数据个数,MWCEB可以确保回归重构出的数据误差有界。此外,MWCEB可降低多个数据流(簇头)或多维数据流(多模节点)中的相关性。
(2)虽然MWCEB可以确保误差有界,但需要人工干预。如果相邻的数据变化剧烈,将难以用一个线性回归模型来描述。为此,本文提出了一种基于自适应回归的误差有界的多属性数据压缩算法(AR-MWCEB)。自适应指该算法可以根据误差限和压缩收益,自动选择传输原始数据还是传输回归系数,自动确定每次参与回归计算的数据个数。分析和实验表明,该算法能够有效地利用传感器数据中存在的时间相关性、空间相关性和多属性间相关性,大大减少数据量。另外,当多属性间相关性减小或不稳定时,其压缩效果也比较理想。
(3)针对传感器数据流的时间序列模型,研究了计算简单的单遍扫描分段逼近算法,在保证数据质量前提下对持续到达的采样数据进行在线式压缩。主要工作包括：①利用传感器节点内置的缓冲区,提出了单传感器节点上基于分段常量逼近的数据压缩算法(PCADC-Sensor),并给出了在无穷范数误差度量下的实现。②提出了单传感器节点上基于分段线性逼近的数据压缩算法(PLADC-Sensor)。分别在无穷范数和2范数误差度量下,给出了计算PLA的两种简单快速算法。推导了分段线性一致逼近的充要条件。③簇头或基站不需要接收各传感器的原始采样数据,提出了直接基于数据的分段线性表示(PLR)的压缩算法(PLRDC-Cluster),推导了相同节点不同时间段、不同节点相同时间段这两种情况下的计算公式。
(4)数据汇聚通过在路由中间节点上检查数据内容来减少数据量,是降低能耗的重要技术。针对由此带来的信息泄露问题,研究了安全汇聚问题。主要工作包括：①提出了一种安全的数据汇聚与认证方案(SEDAA)。将采集的数据映射成无物理意义的模式码,根据模式码进行数据汇聚,构建出汇聚树；再将被选中节点的采集数据以加密形式传输,保证了数据的机密性；利用会话密钥进行延迟汇聚和延迟认证,进行源认证,保证了数据在传输过程中的完整性和真实性；采用计数器生成会话密钥保证了数据的新鲜性。②考虑方案的可扩展性,提出了可扩展性好的安全数据汇聚与认证方案(SSDAA)。SSDAA本地逐步公布用于认证的μTESLA密钥,节点完成本地汇聚后经过两跳传输时间延迟就可开始认证,认证不必等整个汇聚完成,使网络时延小,可适用于较大规模网络。这两种方案都可以抵御植入节点攻击和重放攻击,可部分解决妥协节点攻击问题。
(5)设计和实现了一个基于数据压缩的监控原型系统。原型系统采用传感器网络层、数据服务层、应用层三层架构。传感器网络层是基于TinyOS通过对传感器节点进行嵌入式程序设计实现的,主要功能为采集数据,建立分簇路由,然后进行压缩、传输,数据最终到达网关节点(Sink)。网关节点通过USB口连接到现场PC。数据服务层是在现场PC上实现的,在整个原型系统中起到一个上下桥接的作用,主要用于应用层程序和网关节点之间的数据转发。应用层是在现场或远程PC上实现的,可完成本地或远程的WSN监控管理、数据分析与可视化等任务。
Wireless sensor networks (WSN), attracting plentiful research efforts due to their wide range of potential applications, have been a very active research area. WSN usually consist of a large number of inexpensive sensor nodes that have strictly limited sensing, computation, and communication capabilities. The main tasks for WSN are to collect information from areas under surveillance. It is an important issue to save communication energy, and meanwhile to ensure the sampled data secure. The benefits of in-network processing include minimized amount of data transmission, reduced energy consumption, improved overall bandwidth utilization, and prolonged lifetime. In hostile environments, such as battlefield monitoring and home security, we must take account of the data security, including confidentiality, authentication, integrity, and freshness during transmitting data to the Sink node.
This dissertation focuses on the challenges of data gathering in wireless sensor networks, aiming at high energy efficiency, low network delay and secure aggregation. We make our great efforts to design data compression algorithms and data authentification schemes tailored for WSN. The main works are as follows:
(1) When the spatial correlations among the sensory data don't exist or vary, it is better to design algorithms running on a sensor independently. By designing an error tree and solving the regression equations set, we propose a data compression scheme with infinite norm error bound for wireless sensor networks. The algorithms in the scheme can simultaneously explore the temporal and multiple-dimension-stream correlations among the sensory data. The temporal correlation in one stream is captured by the 1D Haar wavelet transform. We propose a single data stream wavelet compression algorithm with error bound, named SWCEB. For multivariate monitoring sensor networks, some streams from one sensor node are elected as the bases according to the correlation coefficient matrix, and the other streams from the same sensor node can be expressed with one of these bases using linear regression. Thus we propose a regression-based multiple data streams wavelet compression algorithm with error bound, named MWCEB. Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploits the temporal and multiple-dimension-stream correlations on the same sensor node and achieve a significant data reduction.
(2) MWCEB needs manual intervention. By designing an incremental algorithm for computing regression coefficients, a self-adaptive regression-based multiple-streams wavelet compression algorithm with infinite norm error bound (AR-MWCEB) is proposed. Based on error bounds and compression gains, the self-adaptiveness means that our algorithms make decisions automatically to transmit raw data or regression coefficients and to select the number of data involved in regression. Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploit the temporal and multiple-dimension-stream correlations on the single sensor node and exploit the temporal and spatial correlations among multiple streams on the cluster head and achieve a significant data reduction. Furthermore, we observe that the algorithms are also pretty good when multiple-streams correlations are reduced or non-stationary.
(3) A critical and practical demand is to online compress sensor data streams continuously with quality guarantee. We make the following contributes. First, using the built-in buffer of sensor node, we present a piecewise constant approximation based data compression algorithm with infinite norm error bound, which is named PCADC-Sensor and is a near online algorithm. Second, with infinite norm and square norm error bound respectively, we propose two online piecewise linear approximation based data compression algorithms in sensor node, named PLADC-Sensor. A necessary and sufficient condition of PLA uniform approximation is given. Third, we propose a piecewise linear representations based data compression algorithm in cluster head or sink, named PLRDC-Cluster. It need not raw sensory data and can be applied to calculate aggregate functions. Last, our experiments on real-world sensor dataset show that the proposed algorithms match the sensor data stream model and can achieve a significant data reduction.
(4) Data aggregation techniques can greatly help conserve the scarce energy resources in sensor networks by minimizing the number of data transmissions. Conventional data aggregation methods are vulnerable, as cluster-heads receive all the data from sensor nodes and then eliminate the redundancy by checking the contents of the data. A secure energy-efficient data aggregation and authentication scheme called SEDAA is presented. Intermediate nodes, i.e. cluster-heads in each level, implement data aggregation based on pattern codes without leaking the contents of the raw data and only distinct data in encrypted form is transmitted from sensor nodes to the base station, so SEDAA is confidential. Data integrity and authentication exploit two main ideas:delayed aggregation and delayed authentication. Instead of aggregation messages at the immediate next hop, messages are forwarded unchanged over the first hop and then aggregated at the second hop. TheμTESLA is adopted for authentication of messages transmitted by the base station. Data freshness is gained by using session keys calculated by counters. Moreover, a scalable secure data aggregation and authentication scheme called SSDAA is also presented. TheμTESLA key chains are used to reveal and authenticate keys locally. At every round, authentication need not wait until aggregation has been completed, so it can be applied to large scale WSN with a little delayed time. Both SEDAA and SSDAA can defend against intruder node attacks and replay attacks, and can limit the effectiveness of compromised node attacks.
(5) Based on the above achievements, we design and implement a data compression based monitor prototype system. The system architecture consists of three layers:sensor networks tier, data service tier, application tier. On the basis of TinyOS, the sensor networks tier implements a data sampling module, a clustering and routing protocol, and some data compression algorithms in nesC. The sensor networks tier runs in Micaz mote. All compressed sensing data are transmitted to the gateway mote. The gateway mote is connected to the on-site PC via USB port. The data service tier is a middleware for message/data exchange between application tier programs and the WSN gateway mote. The application tier is implemented on the on-site PC or remote client PC. Its functions include local or remote monitoring, data analysis and visualization.
本文以能量、延迟、安全为目标,围绕无线传感器网络中的数据收集问题展开研究,着重研究数据采集和传输过程中数据压缩算法和数据认证机制。主要工作和贡献包括以下几个方面：
(1)针对传感器数据中的时间相关性和多属性间相关性,研究了基于小波和回归的无穷范数误差有界的数据压缩算法。主要工作包括：①提出了单属性数据的误差有界小波压缩算法(SWCEB)。通过分析一维Haar小波变换过程,设计误差树便于分析每个小波系数所影响的重构数据及带来的误差；SWCEB从整体上选择小波系数,使保留的系数个数最少且每个重构数据的误差有界。分析和实验表明,SWCEB消除了单个数据流中的时间相关性,减少了数据量。②提出了基于回归的多属性数据的误差有界小波压缩算法(MWCEB)。若单个传感器节点可以采集多种物理量,即产生多维数据流,则根据相关系数矩阵选择其中的若干个数据流作为基信号,其它数据流借助一个基用线性回归参数来表示。基数据流采用SWCEB压缩；非基数据流采用MWCEB压缩。通过调整收益界和每次处理的数据个数,MWCEB可以确保回归重构出的数据误差有界。此外,MWCEB可降低多个数据流(簇头)或多维数据流(多模节点)中的相关性。
(2)虽然MWCEB可以确保误差有界,但需要人工干预。如果相邻的数据变化剧烈,将难以用一个线性回归模型来描述。为此,本文提出了一种基于自适应回归的误差有界的多属性数据压缩算法(AR-MWCEB)。自适应指该算法可以根据误差限和压缩收益,自动选择传输原始数据还是传输回归系数,自动确定每次参与回归计算的数据个数。分析和实验表明,该算法能够有效地利用传感器数据中存在的时间相关性、空间相关性和多属性间相关性,大大减少数据量。另外,当多属性间相关性减小或不稳定时,其压缩效果也比较理想。
(3)针对传感器数据流的时间序列模型,研究了计算简单的单遍扫描分段逼近算法,在保证数据质量前提下对持续到达的采样数据进行在线式压缩。主要工作包括：①利用传感器节点内置的缓冲区,提出了单传感器节点上基于分段常量逼近的数据压缩算法(PCADC-Sensor),并给出了在无穷范数误差度量下的实现。②提出了单传感器节点上基于分段线性逼近的数据压缩算法(PLADC-Sensor)。分别在无穷范数和2范数误差度量下,给出了计算PLA的两种简单快速算法。推导了分段线性一致逼近的充要条件。③簇头或基站不需要接收各传感器的原始采样数据,提出了直接基于数据的分段线性表示(PLR)的压缩算法(PLRDC-Cluster),推导了相同节点不同时间段、不同节点相同时间段这两种情况下的计算公式。
(4)数据汇聚通过在路由中间节点上检查数据内容来减少数据量,是降低能耗的重要技术。针对由此带来的信息泄露问题,研究了安全汇聚问题。主要工作包括：①提出了一种安全的数据汇聚与认证方案(SEDAA)。将采集的数据映射成无物理意义的模式码,根据模式码进行数据汇聚,构建出汇聚树；再将被选中节点的采集数据以加密形式传输,保证了数据的机密性；利用会话密钥进行延迟汇聚和延迟认证,进行源认证,保证了数据在传输过程中的完整性和真实性；采用计数器生成会话密钥保证了数据的新鲜性。②考虑方案的可扩展性,提出了可扩展性好的安全数据汇聚与认证方案(SSDAA)。SSDAA本地逐步公布用于认证的μTESLA密钥,节点完成本地汇聚后经过两跳传输时间延迟就可开始认证,认证不必等整个汇聚完成,使网络时延小,可适用于较大规模网络。这两种方案都可以抵御植入节点攻击和重放攻击,可部分解决妥协节点攻击问题。
(5)设计和实现了一个基于数据压缩的监控原型系统。原型系统采用传感器网络层、数据服务层、应用层三层架构。传感器网络层是基于TinyOS通过对传感器节点进行嵌入式程序设计实现的,主要功能为采集数据,建立分簇路由,然后进行压缩、传输,数据最终到达网关节点(Sink)。网关节点通过USB口连接到现场PC。数据服务层是在现场PC上实现的,在整个原型系统中起到一个上下桥接的作用,主要用于应用层程序和网关节点之间的数据转发。应用层是在现场或远程PC上实现的,可完成本地或远程的WSN监控管理、数据分析与可视化等任务。
Wireless sensor networks (WSN), attracting plentiful research efforts due to their wide range of potential applications, have been a very active research area. WSN usually consist of a large number of inexpensive sensor nodes that have strictly limited sensing, computation, and communication capabilities. The main tasks for WSN are to collect information from areas under surveillance. It is an important issue to save communication energy, and meanwhile to ensure the sampled data secure. The benefits of in-network processing include minimized amount of data transmission, reduced energy consumption, improved overall bandwidth utilization, and prolonged lifetime. In hostile environments, such as battlefield monitoring and home security, we must take account of the data security, including confidentiality, authentication, integrity, and freshness during transmitting data to the Sink node.
This dissertation focuses on the challenges of data gathering in wireless sensor networks, aiming at high energy efficiency, low network delay and secure aggregation. We make our great efforts to design data compression algorithms and data authentification schemes tailored for WSN. The main works are as follows:
(1) When the spatial correlations among the sensory data don't exist or vary, it is better to design algorithms running on a sensor independently. By designing an error tree and solving the regression equations set, we propose a data compression scheme with infinite norm error bound for wireless sensor networks. The algorithms in the scheme can simultaneously explore the temporal and multiple-dimension-stream correlations among the sensory data. The temporal correlation in one stream is captured by the 1D Haar wavelet transform. We propose a single data stream wavelet compression algorithm with error bound, named SWCEB. For multivariate monitoring sensor networks, some streams from one sensor node are elected as the bases according to the correlation coefficient matrix, and the other streams from the same sensor node can be expressed with one of these bases using linear regression. Thus we propose a regression-based multiple data streams wavelet compression algorithm with error bound, named MWCEB. Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploits the temporal and multiple-dimension-stream correlations on the same sensor node and achieve a significant data reduction.
(2) MWCEB needs manual intervention. By designing an incremental algorithm for computing regression coefficients, a self-adaptive regression-based multiple-streams wavelet compression algorithm with infinite norm error bound (AR-MWCEB) is proposed. Based on error bounds and compression gains, the self-adaptiveness means that our algorithms make decisions automatically to transmit raw data or regression coefficients and to select the number of data involved in regression. Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploit the temporal and multiple-dimension-stream correlations on the single sensor node and exploit the temporal and spatial correlations among multiple streams on the cluster head and achieve a significant data reduction. Furthermore, we observe that the algorithms are also pretty good when multiple-streams correlations are reduced or non-stationary.
(3) A critical and practical demand is to online compress sensor data streams continuously with quality guarantee. We make the following contributes. First, using the built-in buffer of sensor node, we present a piecewise constant approximation based data compression algorithm with infinite norm error bound, which is named PCADC-Sensor and is a near online algorithm. Second, with infinite norm and square norm error bound respectively, we propose two online piecewise linear approximation based data compression algorithms in sensor node, named PLADC-Sensor. A necessary and sufficient condition of PLA uniform approximation is given. Third, we propose a piecewise linear representations based data compression algorithm in cluster head or sink, named PLRDC-Cluster. It need not raw sensory data and can be applied to calculate aggregate functions. Last, our experiments on real-world sensor dataset show that the proposed algorithms match the sensor data stream model and can achieve a significant data reduction.
(4) Data aggregation techniques can greatly help conserve the scarce energy resources in sensor networks by minimizing the number of data transmissions. Conventional data aggregation methods are vulnerable, as cluster-heads receive all the data from sensor nodes and then eliminate the redundancy by checking the contents of the data. A secure energy-efficient data aggregation and authentication scheme called SEDAA is presented. Intermediate nodes, i.e. cluster-heads in each level, implement data aggregation based on pattern codes without leaking the contents of the raw data and only distinct data in encrypted form is transmitted from sensor nodes to the base station, so SEDAA is confidential. Data integrity and authentication exploit two main ideas:delayed aggregation and delayed authentication. Instead of aggregation messages at the immediate next hop, messages are forwarded unchanged over the first hop and then aggregated at the second hop. TheμTESLA is adopted for authentication of messages transmitted by the base station. Data freshness is gained by using session keys calculated by counters. Moreover, a scalable secure data aggregation and authentication scheme called SSDAA is also presented. TheμTESLA key chains are used to reveal and authenticate keys locally. At every round, authentication need not wait until aggregation has been completed, so it can be applied to large scale WSN with a little delayed time. Both SEDAA and SSDAA can defend against intruder node attacks and replay attacks, and can limit the effectiveness of compromised node attacks.
(5) Based on the above achievements, we design and implement a data compression based monitor prototype system. The system architecture consists of three layers:sensor networks tier, data service tier, application tier. On the basis of TinyOS, the sensor networks tier implements a data sampling module, a clustering and routing protocol, and some data compression algorithms in nesC. The sensor networks tier runs in Micaz mote. All compressed sensing data are transmitted to the gateway mote. The gateway mote is connected to the on-site PC via USB port. The data service tier is a middleware for message/data exchange between application tier programs and the WSN gateway mote. The application tier is implemented on the on-site PC or remote client PC. Its functions include local or remote monitoring, data analysis and visualization.
引文
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