无线传感器网络中的节点调度研究
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摘要
无线传感器网络是21世纪信息产业的三大支柱(计算、通信和传感器)相结合的产物,并以其低功耗、低成本、分布式和自组织的特点带来了信息感知的一场变革,已经引起了许多国家学术界和工业界的高度重视,被认为是对21世纪产生巨大影响力的技术之一。无线传感器网络是由部署在监测区域内大量的廉价微型并具有感知、存储、数据处理、无线通信等能力的传感器节点组成,通过无线通信方式形成的一个多跳自组织网络。其目标是从物理空间收集数据,提供网络用户终端与物理现象之间的接口,因此无线传感器网络是一种全新的信息获取平台,能够实时监测和采集网络分布区域内的各种监测对象的信息,并将这些信息发送到监测终端,以实现复杂的指定范围内信息采集、目标检测与跟踪。具有快速展开、抗毁性强等特点,可应用在军事侦察、环境监测、医疗监护、空间探索、仓库管理等领域,具有广阔的应用前景。
跨层设计思想是下一代无线通信系统的一项关键的理论创新,该方法打破了传统的分层设计思想,将原来被割裂的网络各层作为统一的整体进行设计、分析、优化和控制,同时充分利用各层之间强烈的相关性信息,进行无线网络协议的整体优化。跨层设计是能够提供比分层设计更丰富层间交互的算法、协议及体系结构的设计方法,对于无线通信网络尤其是传感器网络这种资源受限网络有着极其重要的意义。
IEEE 802.15.4标准被认为是最适合于无线传感器网络的标准之一,符合该标准的传感器节点可以工作在ISM 2.4G频段,该频段由于无需授权,因此符合802.11gWLAN、802.16e WMAN等标准下的无线设备均可工作在此频段。无线传感器网络如何与这些网络并存甚至协作是具有重要实际意义的研究课题。认知无线电技术的出现,为有效解决这个问题提供了技术支持。认知无线电技术可以感知周围环境,并从周围环境中获取信息,因此借助于该项技术无线传感器网络可以适应多网并存的环境变化,大大提高频谱的利用率。
作为一种特殊的无线自组织网络,传感器网络拥有节点数量大、计算能力和电池能量有限等特点。在我们的研究中将重点考虑无线传感器网络的如下特点：
·由于节点布置密集,传感器节点采集到的原始数据具有很高的相关性和冗余度。
·能量有效性和公平性是影响网络寿命的最关键因素。
针对以上特点,我们将近年来引起广泛关注的跨出设计和认知无线电技术引入到无线传感器网络中,着重研究对传感器节点及其资源的调度问题,以期获得更好的能效性及公平性,延长网络生存寿命。
本文的主要工作和创新点如下：
1.从无线传感器网络的特点及应用场景引出进行跨层设计的必要性和重要性,指出跨层设计在无线传感器网络应用中所面临的挑战及设计时应遵循的原则。将目前无线传感器网络中的跨层设计方案按照优化目标、优化输入变量、优化配置等三个特征,依据联合设计和信息共享两种设计原理进行了分类。
2.提出了一种基于信道状态信息的传感器网络信源区信息采集处理方案。考虑到簇首节点和一般节点之间的信道状态将影响接收信号的信噪比,提出了一种基于CSI的簇首融合算法。簇首节点接收一般节点发送的本地判决信息并融合做出最终判决,具有较好CSI的传感器节点的判决信息在最终判决中发挥更重要的作用。相反地,簇首融合时应尽量少的依赖CSI差的节点信息。在分析算法结果的过程中,注意到簇内节点部署上的冗余提供了合理选取节点的自由度,据此在融合算法的基础上利用跨层设计提出了一种最优的MAC层传输调度算法,通过定义的二元调度矩阵将问题转化为线性整数优化问题并利用分支定界算法找到最优解。仿真结果表明,提出的融合算法较其他的算法有很大的性能提高,利用提出的传输调度算法可以达到系统性能和能量有效性之间的很好折中。
3.针对现有基于分簇的虚拟MIMO传输机制存在的集中控制、没有考虑天线选择过程能耗等弊端,设计了一种全新的虚拟MIMO传输机制。该机制打破了原有的分簇结构,利用分布式的定向扩散协议框架,由目的节点将所需要获取的信息提取特征后利用兴趣包的格式广播出去,网络中的中间节点接收到兴趣包后建立起与兴趣包发送节点间的梯度,更新包含有自己状态信息的新兴趣包后广播出去。当兴趣包完成在整个网络中的广播到达源节点后,各个中间节点也完成了与邻近节点的梯度建立过程。然后根据梯度信息以跳为单位进行天线选择,并完成数据回送过程。这种分布式机制将物理层的MIMO技术与网络层的路由技术结合,将天线选择过程的能量消耗考虑进整个系统能耗中,分布式的处理过程使得整个网络的能量消耗更加平均,可靠性和可扩展性也大大提高。
4.在认知无线传感器网络中,通过引入联合频谱感知机制,即一组传感器节点协作完成频谱感知任务,可以避免由于单一传感器节点感知所造成的检测失误,提高频谱感知的可靠性。从联合频谱感知角度出发,越多传感器节点的参与将会获得越准确的检测结果,但从传感器节点节能的角度考虑,越多节点的参与将造成越多的能量损耗。因此对于无线认知传感器网络来讲,上述两个方面均需考虑以期得到检测准确性与能量有效性的折中。研究中我们分析得到了检测能耗与检测性能之间的函数关系,得到在满足一定检测性能要求下的最短感知时间。在此基础上为了达到节点间能耗的公平性,设计了一种调度矩阵来指导完成联合频谱感知。
5.针对传感器这类资源有限的认知无线网络,研究了能以较少的代价获得理想信道估计的方案。针对指数分布的ON/OFF信道模型,计算出稀疏抽样条件下费舍尔信息的上下限。同时指出在此条件下,均匀抽样是最劣的抽样方案,同时也给出了最优的抽样方案。两种方案可以为比较各种抽样方案提供基准。同一抽样密度即抽样点数相同的前提下,不同的随机抽样方案的估计准确性依赖于抽样间隔的高阶中心矩。通过引入圆周β群,比较了不同分布下的随机抽样性能,得出在该信道模型下,服从指数分布的随机抽样性能优于其他分布。为了达到进一步节能的目的,提出了一种自适应的随机抽样方案以跟踪信道参数的变化,该方案根据已有估计及时调整抽样密度,合理分配资源。
6.对于无线传感器网络来讲其最基本、最重要的理论支撑便是信息获取理论。但是传统的Nyquist理论指导下的信息获取、存储、融合、处理及传输等已经成为目前信息领域进一步发展的主要瓶颈之一。近年来提出的压缩感知理论对于稀疏信号可通过远低于Nyquist标准的方式进行数据采样,仍能够精确地恢复出原始信号。我们将压缩感知理论引入到无线认知传感器网络的信道估计问题中,通过较低的采样率恢复出原始信道状态序列,继而进行最大似然估计。从整体来看,由于测量矩阵和基矩阵的相关性使得基于压缩感知的估计方法在性能上并没有优势。如何能够找到一个既能稀疏化信号,又能保证与测量矩阵不相关的基矩阵是今后研究的重点。
Wireless sensor networks (WSNs) were identified by Business Week as one of the most important and impactive technologies for the 21st century. Wireless sensor networks are the product of the combination of computing, communications and sensor which are the three pillars of the information industry in the 21st century. The central premise of sensor networks is the distributed collection and digitization of data from a physical space, providing an interface between the physical and digital domains. Wireless sensor networks consist of a potentially large number of sensor modules that integrate memory, communication, processing, and sensing capabilities. The sensor modules form ad hoc networks in order to share the collected physical data and to provide this data to the network user or operator. Sensor networks have a wide range of applications, including medical, environmental, military, industrial, and commercial applications.
Layered communication approaches typically separate communication tasks into several layers, with a clear definition of the functionality of each layer. In a layered communication stack, interaction among layers occurs through well-defined standard-ized interfaces that connect only the neighboring layers in the stack. In contrast, cross layer approaches attempt to exploit a richer interaction among communication layers to achieve performance gains. It led researchers to consider cross layer design for wireless sensor networks.
With the development of wireless technologies, multifarious standards are cur-rently emerging. For example, the unlicensed 2.45 GHz ISM band can host various net-works with different standards, such as IEEE 802.11g WLANs, IEEE 802.16e WMANs and IEEE 802.15.4 WPANs. Thus, the coexistence issue of such networks challenges the reasonable and efficient use of the scarce spectrum. Fortunately, cognitive radios have been proposed as a technology to implement opportunistic sharing. They are able to sense the spectral environment over a wide frequency band and use this information to opportunistically provide wireless links that can satisfy the user communications requirements optimally.
As a special kind of Ad-hoc networks, wireless sensor networks have some different characteristics such as:
Sensor nodes are densely deployed. The data collected by the sensor nodes are highly correlated and redundant.
The lifetime of the wireless sensor networks is highly dependent on the energy efficiency and fairness among sensor nodes.
Considering the above characteristics of wireless sensor networks, we introduce cross layer design and cognitive radio technique, focus on the node and resource scheduling, to obtain better energy efficiency and fairness.
The main work and the innovations are as the following:
1. Considering the characteristics of wireless sensor networks and their applying sce-narios, we introduce the design challenges and guidelines for wireless sensor network cross layer design. We provide an overview of the features of existing cross layer approaches for wireless sensor networks that rely on information sharing and design coupling. The classification is by the features such as input aspect, configuration optimizations and performance goals.
2. Noting that the channel state information (CSI) between the cluster head and the sensor nodes will affect the received bit energy noise ratio of the sensor nodes, we propose an optimal data fusion algorithm based on CSI for a one-hop clustered wireless sensor network. The cluster head receives the information bits from the sensor nodes for the final decision-making. The ones with good channel state should play more important role in the final decision-making. On the other hand, the ones, which have higher error probability, may have opposite effect on the fusion. So the fusion could not rely on them very much or even ignore them. On the basis of the fusion algorithm, we consider the redundancy of the sensor deployment and propose a cross layer transmission scheduling scheme. By selecting proper set of sensor nodes to transmit their local decision back in turn, the scheme can prolong the lifetime of the sensor network. The numerical and simulation results show that it can get a good tradeoff between the energy efficiency and the performance.
3. Considering the shortcomings of centralized control and no energy consumption consideration for antenna selection in the existing clustered virtual MIMO trans-mission mechanism, we propose a new virtual MIMO transmission scheme based on the directed diffusion routing protocol. The operations of the proposed scheme are broken into rounds. In each round, firstly the sink node disseminates the inter-est packets throughout the sensor network. These interest packets dissemination are used for setting up gradients within the network. Then the sensor nodes se-lect appropriate nodes to take part in the transmission or reception according to the gradients, finally complete the process of data sending back. This distributed scheme combines the MIMO technique on the physical layer and the directed dif-fusion protocol on the routing layer together. It can improve the energy efficiency, reliability and scalability of the networks.
4. We consider the coexistence of wireless sensor networks with other wireless net-works using cognitive radio technique. Multiple sensor nodes are involved in the spectrum sensing to avoid the interferences from other wireless users. The more sensor nodes cooperate in the sensing, the better detection performance can be obtained, however, more energy is consumed. How to get the tradeoff between the energy efficiency and the detection performance is a key problem. We first obtain the least required detection time of a single sensor node when given the requirements on detection. Then, the voting fusion rule is adopted for the final decision making, the relationship between the final detection performance and the energy consumption is analyzed. Based on the considerations above, a detection scheduling matrix is presented in order to make the cooperative sensing more fairly. The cooperative sensing scheduled by the matrix can achieve a balance of energy consumption among the cooperative sensor nodes.
5. We study the problem of optimally placing sensing times over a time window so as to get the best estimate on the parameters of an on-off renewal channel for the wireless cognitive sensor networks. We demonstrate that when sampling is done sparsely, random sensing significantly outperforms uniform sensing. In the special case of exponentially distributed ON/OFF durations, we derive tight lower and upper bounds on the Fisher information under a sparsity condition, while obtaining the best and worst possible sampling schemes measured by the Fisher information. We show that uniform sensing is the worst one can do; any deviation from it improves the estimation accuracy. We present a dynamic programming approach to obtain the best and worst sampling sequences in the more general case without the sparsity condition. We show that under the same channel statistics and the same average sampling interval (or frequency), a random sensing scheme affects the estimation accuracy through the higher-order central moments of the sampling intervals, and use the circularβensemble to study a family of distributions. We present an adaptive random sensing scheme that can very effectively track time-varying channel parameters, and is shown to outperform its counterpart using uniform sensing.
6. The basic but the most important theory guidance for wireless sensor networks is the theory of information acquisition. However, the theory of information ac-quisition, storage, integration, processing and transmission under the guidance of Nyquist theory become the main bottlenecks for further development. So a new theory guidance, that is compressive sensing or compressive sampling is proposed. The theory demonstrates that:the signal can be compressed far below the Nyquist sampling rate in a standard way, still be able to accurately recovered. It is tempting to examine whether this technique brings any advantage for our channel estima-tion problem. The idea is to randomly sample the channel state, use compressive sensing techniques to reconstruct the entire sequence of channel state evolution, and then use the ML estimator to determine the channel parameter. While the compressive sensing technique can result in a whole sequence, its estimation per-formance is ultimately fairly poor due to the unsatisfactory reconstruction process. This points to an interesting direction of future study, which is to find a better basis matrix that can both sparsify signal and at the same time is sufficiently incoherent with the measurement matrix.
跨层设计思想是下一代无线通信系统的一项关键的理论创新,该方法打破了传统的分层设计思想,将原来被割裂的网络各层作为统一的整体进行设计、分析、优化和控制,同时充分利用各层之间强烈的相关性信息,进行无线网络协议的整体优化。跨层设计是能够提供比分层设计更丰富层间交互的算法、协议及体系结构的设计方法,对于无线通信网络尤其是传感器网络这种资源受限网络有着极其重要的意义。
IEEE 802.15.4标准被认为是最适合于无线传感器网络的标准之一,符合该标准的传感器节点可以工作在ISM 2.4G频段,该频段由于无需授权,因此符合802.11gWLAN、802.16e WMAN等标准下的无线设备均可工作在此频段。无线传感器网络如何与这些网络并存甚至协作是具有重要实际意义的研究课题。认知无线电技术的出现,为有效解决这个问题提供了技术支持。认知无线电技术可以感知周围环境,并从周围环境中获取信息,因此借助于该项技术无线传感器网络可以适应多网并存的环境变化,大大提高频谱的利用率。
作为一种特殊的无线自组织网络,传感器网络拥有节点数量大、计算能力和电池能量有限等特点。在我们的研究中将重点考虑无线传感器网络的如下特点：
·由于节点布置密集,传感器节点采集到的原始数据具有很高的相关性和冗余度。
·能量有效性和公平性是影响网络寿命的最关键因素。
针对以上特点,我们将近年来引起广泛关注的跨出设计和认知无线电技术引入到无线传感器网络中,着重研究对传感器节点及其资源的调度问题,以期获得更好的能效性及公平性,延长网络生存寿命。
本文的主要工作和创新点如下：
1.从无线传感器网络的特点及应用场景引出进行跨层设计的必要性和重要性,指出跨层设计在无线传感器网络应用中所面临的挑战及设计时应遵循的原则。将目前无线传感器网络中的跨层设计方案按照优化目标、优化输入变量、优化配置等三个特征,依据联合设计和信息共享两种设计原理进行了分类。
2.提出了一种基于信道状态信息的传感器网络信源区信息采集处理方案。考虑到簇首节点和一般节点之间的信道状态将影响接收信号的信噪比,提出了一种基于CSI的簇首融合算法。簇首节点接收一般节点发送的本地判决信息并融合做出最终判决,具有较好CSI的传感器节点的判决信息在最终判决中发挥更重要的作用。相反地,簇首融合时应尽量少的依赖CSI差的节点信息。在分析算法结果的过程中,注意到簇内节点部署上的冗余提供了合理选取节点的自由度,据此在融合算法的基础上利用跨层设计提出了一种最优的MAC层传输调度算法,通过定义的二元调度矩阵将问题转化为线性整数优化问题并利用分支定界算法找到最优解。仿真结果表明,提出的融合算法较其他的算法有很大的性能提高,利用提出的传输调度算法可以达到系统性能和能量有效性之间的很好折中。
3.针对现有基于分簇的虚拟MIMO传输机制存在的集中控制、没有考虑天线选择过程能耗等弊端,设计了一种全新的虚拟MIMO传输机制。该机制打破了原有的分簇结构,利用分布式的定向扩散协议框架,由目的节点将所需要获取的信息提取特征后利用兴趣包的格式广播出去,网络中的中间节点接收到兴趣包后建立起与兴趣包发送节点间的梯度,更新包含有自己状态信息的新兴趣包后广播出去。当兴趣包完成在整个网络中的广播到达源节点后,各个中间节点也完成了与邻近节点的梯度建立过程。然后根据梯度信息以跳为单位进行天线选择,并完成数据回送过程。这种分布式机制将物理层的MIMO技术与网络层的路由技术结合,将天线选择过程的能量消耗考虑进整个系统能耗中,分布式的处理过程使得整个网络的能量消耗更加平均,可靠性和可扩展性也大大提高。
4.在认知无线传感器网络中,通过引入联合频谱感知机制,即一组传感器节点协作完成频谱感知任务,可以避免由于单一传感器节点感知所造成的检测失误,提高频谱感知的可靠性。从联合频谱感知角度出发,越多传感器节点的参与将会获得越准确的检测结果,但从传感器节点节能的角度考虑,越多节点的参与将造成越多的能量损耗。因此对于无线认知传感器网络来讲,上述两个方面均需考虑以期得到检测准确性与能量有效性的折中。研究中我们分析得到了检测能耗与检测性能之间的函数关系,得到在满足一定检测性能要求下的最短感知时间。在此基础上为了达到节点间能耗的公平性,设计了一种调度矩阵来指导完成联合频谱感知。
5.针对传感器这类资源有限的认知无线网络,研究了能以较少的代价获得理想信道估计的方案。针对指数分布的ON/OFF信道模型,计算出稀疏抽样条件下费舍尔信息的上下限。同时指出在此条件下,均匀抽样是最劣的抽样方案,同时也给出了最优的抽样方案。两种方案可以为比较各种抽样方案提供基准。同一抽样密度即抽样点数相同的前提下,不同的随机抽样方案的估计准确性依赖于抽样间隔的高阶中心矩。通过引入圆周β群,比较了不同分布下的随机抽样性能,得出在该信道模型下,服从指数分布的随机抽样性能优于其他分布。为了达到进一步节能的目的,提出了一种自适应的随机抽样方案以跟踪信道参数的变化,该方案根据已有估计及时调整抽样密度,合理分配资源。
6.对于无线传感器网络来讲其最基本、最重要的理论支撑便是信息获取理论。但是传统的Nyquist理论指导下的信息获取、存储、融合、处理及传输等已经成为目前信息领域进一步发展的主要瓶颈之一。近年来提出的压缩感知理论对于稀疏信号可通过远低于Nyquist标准的方式进行数据采样,仍能够精确地恢复出原始信号。我们将压缩感知理论引入到无线认知传感器网络的信道估计问题中,通过较低的采样率恢复出原始信道状态序列,继而进行最大似然估计。从整体来看,由于测量矩阵和基矩阵的相关性使得基于压缩感知的估计方法在性能上并没有优势。如何能够找到一个既能稀疏化信号,又能保证与测量矩阵不相关的基矩阵是今后研究的重点。
Wireless sensor networks (WSNs) were identified by Business Week as one of the most important and impactive technologies for the 21st century. Wireless sensor networks are the product of the combination of computing, communications and sensor which are the three pillars of the information industry in the 21st century. The central premise of sensor networks is the distributed collection and digitization of data from a physical space, providing an interface between the physical and digital domains. Wireless sensor networks consist of a potentially large number of sensor modules that integrate memory, communication, processing, and sensing capabilities. The sensor modules form ad hoc networks in order to share the collected physical data and to provide this data to the network user or operator. Sensor networks have a wide range of applications, including medical, environmental, military, industrial, and commercial applications.
Layered communication approaches typically separate communication tasks into several layers, with a clear definition of the functionality of each layer. In a layered communication stack, interaction among layers occurs through well-defined standard-ized interfaces that connect only the neighboring layers in the stack. In contrast, cross layer approaches attempt to exploit a richer interaction among communication layers to achieve performance gains. It led researchers to consider cross layer design for wireless sensor networks.
With the development of wireless technologies, multifarious standards are cur-rently emerging. For example, the unlicensed 2.45 GHz ISM band can host various net-works with different standards, such as IEEE 802.11g WLANs, IEEE 802.16e WMANs and IEEE 802.15.4 WPANs. Thus, the coexistence issue of such networks challenges the reasonable and efficient use of the scarce spectrum. Fortunately, cognitive radios have been proposed as a technology to implement opportunistic sharing. They are able to sense the spectral environment over a wide frequency band and use this information to opportunistically provide wireless links that can satisfy the user communications requirements optimally.
As a special kind of Ad-hoc networks, wireless sensor networks have some different characteristics such as:
Sensor nodes are densely deployed. The data collected by the sensor nodes are highly correlated and redundant.
The lifetime of the wireless sensor networks is highly dependent on the energy efficiency and fairness among sensor nodes.
Considering the above characteristics of wireless sensor networks, we introduce cross layer design and cognitive radio technique, focus on the node and resource scheduling, to obtain better energy efficiency and fairness.
The main work and the innovations are as the following:
1. Considering the characteristics of wireless sensor networks and their applying sce-narios, we introduce the design challenges and guidelines for wireless sensor network cross layer design. We provide an overview of the features of existing cross layer approaches for wireless sensor networks that rely on information sharing and design coupling. The classification is by the features such as input aspect, configuration optimizations and performance goals.
2. Noting that the channel state information (CSI) between the cluster head and the sensor nodes will affect the received bit energy noise ratio of the sensor nodes, we propose an optimal data fusion algorithm based on CSI for a one-hop clustered wireless sensor network. The cluster head receives the information bits from the sensor nodes for the final decision-making. The ones with good channel state should play more important role in the final decision-making. On the other hand, the ones, which have higher error probability, may have opposite effect on the fusion. So the fusion could not rely on them very much or even ignore them. On the basis of the fusion algorithm, we consider the redundancy of the sensor deployment and propose a cross layer transmission scheduling scheme. By selecting proper set of sensor nodes to transmit their local decision back in turn, the scheme can prolong the lifetime of the sensor network. The numerical and simulation results show that it can get a good tradeoff between the energy efficiency and the performance.
3. Considering the shortcomings of centralized control and no energy consumption consideration for antenna selection in the existing clustered virtual MIMO trans-mission mechanism, we propose a new virtual MIMO transmission scheme based on the directed diffusion routing protocol. The operations of the proposed scheme are broken into rounds. In each round, firstly the sink node disseminates the inter-est packets throughout the sensor network. These interest packets dissemination are used for setting up gradients within the network. Then the sensor nodes se-lect appropriate nodes to take part in the transmission or reception according to the gradients, finally complete the process of data sending back. This distributed scheme combines the MIMO technique on the physical layer and the directed dif-fusion protocol on the routing layer together. It can improve the energy efficiency, reliability and scalability of the networks.
4. We consider the coexistence of wireless sensor networks with other wireless net-works using cognitive radio technique. Multiple sensor nodes are involved in the spectrum sensing to avoid the interferences from other wireless users. The more sensor nodes cooperate in the sensing, the better detection performance can be obtained, however, more energy is consumed. How to get the tradeoff between the energy efficiency and the detection performance is a key problem. We first obtain the least required detection time of a single sensor node when given the requirements on detection. Then, the voting fusion rule is adopted for the final decision making, the relationship between the final detection performance and the energy consumption is analyzed. Based on the considerations above, a detection scheduling matrix is presented in order to make the cooperative sensing more fairly. The cooperative sensing scheduled by the matrix can achieve a balance of energy consumption among the cooperative sensor nodes.
5. We study the problem of optimally placing sensing times over a time window so as to get the best estimate on the parameters of an on-off renewal channel for the wireless cognitive sensor networks. We demonstrate that when sampling is done sparsely, random sensing significantly outperforms uniform sensing. In the special case of exponentially distributed ON/OFF durations, we derive tight lower and upper bounds on the Fisher information under a sparsity condition, while obtaining the best and worst possible sampling schemes measured by the Fisher information. We show that uniform sensing is the worst one can do; any deviation from it improves the estimation accuracy. We present a dynamic programming approach to obtain the best and worst sampling sequences in the more general case without the sparsity condition. We show that under the same channel statistics and the same average sampling interval (or frequency), a random sensing scheme affects the estimation accuracy through the higher-order central moments of the sampling intervals, and use the circularβensemble to study a family of distributions. We present an adaptive random sensing scheme that can very effectively track time-varying channel parameters, and is shown to outperform its counterpart using uniform sensing.
6. The basic but the most important theory guidance for wireless sensor networks is the theory of information acquisition. However, the theory of information ac-quisition, storage, integration, processing and transmission under the guidance of Nyquist theory become the main bottlenecks for further development. So a new theory guidance, that is compressive sensing or compressive sampling is proposed. The theory demonstrates that:the signal can be compressed far below the Nyquist sampling rate in a standard way, still be able to accurately recovered. It is tempting to examine whether this technique brings any advantage for our channel estima-tion problem. The idea is to randomly sample the channel state, use compressive sensing techniques to reconstruct the entire sequence of channel state evolution, and then use the ML estimator to determine the channel parameter. While the compressive sensing technique can result in a whole sequence, its estimation per-formance is ultimately fairly poor due to the unsatisfactory reconstruction process. This points to an interesting direction of future study, which is to find a better basis matrix that can both sparsify signal and at the same time is sufficiently incoherent with the measurement matrix.
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