面向不确定环境的物联网压缩感知问题研究
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摘要
物联网是将“互联”延伸和扩展到了任何物品与物品之间,各种信息感知元件和传感设备实时采集和交换信息,并与互联网结合形成的巨大网络。无线网络是实现物联网的核心接入技术。在物联网中,对不确定环境下信息的可靠感知和能量有效的传输是至关重要的问题。近年来,一种新的信息获取理论压缩感知被提出。利用该理论,只需要传感器进行少量的采样并将它们发送到接收端,然后接收端就能够以极大精度恢复出原始信号,这对能耗有限的无线传感网络来说是一个极大的优势。在当前的应用中,有些采样信号的稀疏表示被预先设定且感知元件能量不受限,或者为满足信号稀疏性,需要传感器采集大量冗余信息且采样维度非常大,产生不必要地浪费。
在物联网中,常常感知到源自不同情况下的信号,其环境和场景都是不同的。不确定环境下的采样信号一般很难找到对应的稀疏变换且传感器的处理能力和能耗都是有限的,因此如果要很好的利用压缩感知的优势,就需要分析不同环境下信号的特性,找到合适的可压缩空间。基于实际环境中信号难以稀疏表示的现实,本文分析了三种情况下的采样信号,分别是采样信号有关联(包括全局强相关、全局弱相关局部强相关)和采样信号无关联,并设计了相应的压缩感知方法。基于这三种情况下的信号都是物联网中常见的采样信号,共同的特点都是很难找到对应的稀疏变换。论文的主要工作如下：
针对采样信号有相关性但难以获得稀疏度的问题,本文设计了基于局部区域的压缩感知方法。产生这种信号是因为：当感知区域较小时,信号间会有较强的相关性或者紧耦合,但由于环境信息的不确定,信号稀疏度很难获得。因此,本文首先根据分布式压缩感知理论分析了传感器在局部区域收集到的数据,然后给出时空相关性模型。最后,针对四类常见的数据分布,设计相应的压缩矩阵。实验显示,所需采样仅为原采样的25%-37.%就可以得到相同的重构准确度。
针对多类采样信号很难统一重构的问题,本文设计了基于分类的压缩感知方法。产生这种信号是因为：随着环境规模的扩大,信号集相应增大,信号之间关系变得复杂,可以认为信号集内存在多种类信号,且类内相关性强而类间相关性弱,即耦合程度是内紧外松,因此需要从全局角度考虑采样信号。在本文的方法中,首先提出一种有效的混合传输方式避免传输能耗的浪费。然后分析测量矩阵的选择和分类标准,以进一步提高信号的恢复精度。最后设计基于分类的重构算法并提出采样原则以避免冗余采样。实验显示,与传统压缩感知相比,信号在传输过程中能消减少60%左右,且采样量减少30%。
针对采样信号在取值空间内随机呈现且无法稀疏表示的情况,本文设计了基于有限区间随机信号的压缩感知方法。产生这种信号是因为：当环境时常发生变化时,采样信号也会随机呈现,并不存在任何关联,或者很难展现它们的关系,当然,由于感知元件的物理特性,信号区间是有限的,这意味着虽然采样信号是随机的,但其数据值却在一定区间内。根据这种情况,本文对无稀疏性的有限随机数据进行了稀疏编码。核心思想是将信号在统一的格式下稀疏化。为达到数据稀疏化的要求,对二进制数据简单编码,同时,进行适当分层,减少信号恢复过程中的匹配空间。最后设计了基于层稀疏的重构算法,减少了采样次数。理论证明,基于节点数n的数据包期望长度为o((?)n)。实验结果也验证了这一结论。
本文的创新点是：
1.基于局部区域的压缩感知方法压缩矩阵尽可能地提取出公共部分,极大地减少了采样量,并且对信号的压缩是在采样过程中同步实现的,因此不需要额外耗费传感器能量。
2.基于分类的压缩感知算法减少了采样次数且所提出的采样原则也避免了冗余采样采用。同时,采用混合传输方式减少了信号传输次数并尽可能的保证了网络的负载均衡。
3.基于有限区间随机信号的压缩感知方法将随机信号转换成格式统一的稀疏信号,满足了压缩感知要求,减少了采样,并且传输过程中的数据包长度变化稳定。
To connect things, a variety of sense components and equipments are adopted to gather and exchange information in real time and are combined with Internet as a huge network, which is called Internet of Things(IOT). In this huge network, the wirless technology is critical. In IOT, it is crucial to be sensed information of uncertain environment reliably and transmit them effectively. Recently, a novel signal sampling theory Compressive Sensing is proposed, which only acquires few samplings from sparse signals and then could recover them precisely. Therefore, it has been paid attention and applied in many fieds. Based on compressive sensing, sensors only need to transmission a small number of samplings to the termination, and then the receiver can recover the original signals accurately, which is an advantage for wireless sensor network.
However, it is difficult for uncertain environment information in IOT to be satified the requirement of sparsity of compressive sensing. Therefore, the characters of the sensed siginals should be further analyzed to obtain the compresiable content, and then design suitable approaches. Due to the diffucult of sparsing signals, this paper studies on signals which are appeared in three scenes in IOT and then design corresponding methords. The main contents in the paper are as follow:
Based on the scene that it is difficult for relative signals to obtain their sparsity, a compresensive sensing approach based on local regions has been proposed. The structure of this kind of data consists of two parts, which are common sparse part and special sparse part respectively. In this case, the former should be abstracted as much as possible. In this paper, two reasonable assumptions are given and then a spatical-temporal correlation model is presented. In this model, data gathered in local region satisfy spatical-temporal correlation in a certain extend. To abstract their common parts, compressive matrices are designed for four data distributions, which are uniform distribution, Guassian distribution, expential distribution and Poission distribution. For the two former ones, hybrid compressive matrices are proposed. For the two later ones, the functions of transformation are proved and the paramaters estimation could be easily achieved.
Based on the scene of multi-classifications signals, a compressive sensing method based on classification is proposed. The effective hybrid transmition schem is present, in which both the advantage of compressive sening is retained and the unnecessary energy cost is avoided. To increase the accurate of reconstructing signals, the choice of measurement matrices and the number of classes are considered. The classification-based algorithm is designed and the principle of sampling is presented to decrease required samplings and redundant samplings.
Based on the scene of random signals without sparsity, a compressive sensing approach for limited random signals is proposed. Due to the limitation of the sensing ability of equipments, the gathered data are limited.. In this limited range, random signals are encoded for sparsity, which turn this kind of signals into sparse ones in a uniform formate to satisfy the requirement of theory of compressive sensing. To satisfy the requeirement of CS, binary digits are sparsing encoded. Meanwhile, suitably layering binary digits to decrease the spase of matching and increase the success of pursuiting. Afterwards, the reconstruction algorithm based on layer is designed to reduce the requirement of samplings.
Our contributions in the paper are as follows:
1. The CS approach based on local region data abstract the common part from the data which immensely the required number of samplings and the process of compressing signal need rarely energy cost.
2. The CS approach based on classification reduces the samplings and avoids redundant ones. Meanwhile, the hybrid scheme decreases the number of signal transmission and guarantees the load balance.
4. The CS approach based on limited random signals transforms random signas to sparase signals with an uniform format which satisfies the requriemnt of CS and is in favor of diminish samplings, and the lengh of package is stable in the process of transmission.
在物联网中,常常感知到源自不同情况下的信号,其环境和场景都是不同的。不确定环境下的采样信号一般很难找到对应的稀疏变换且传感器的处理能力和能耗都是有限的,因此如果要很好的利用压缩感知的优势,就需要分析不同环境下信号的特性,找到合适的可压缩空间。基于实际环境中信号难以稀疏表示的现实,本文分析了三种情况下的采样信号,分别是采样信号有关联(包括全局强相关、全局弱相关局部强相关)和采样信号无关联,并设计了相应的压缩感知方法。基于这三种情况下的信号都是物联网中常见的采样信号,共同的特点都是很难找到对应的稀疏变换。论文的主要工作如下：
针对采样信号有相关性但难以获得稀疏度的问题,本文设计了基于局部区域的压缩感知方法。产生这种信号是因为：当感知区域较小时,信号间会有较强的相关性或者紧耦合,但由于环境信息的不确定,信号稀疏度很难获得。因此,本文首先根据分布式压缩感知理论分析了传感器在局部区域收集到的数据,然后给出时空相关性模型。最后,针对四类常见的数据分布,设计相应的压缩矩阵。实验显示,所需采样仅为原采样的25%-37.%就可以得到相同的重构准确度。
针对多类采样信号很难统一重构的问题,本文设计了基于分类的压缩感知方法。产生这种信号是因为：随着环境规模的扩大,信号集相应增大,信号之间关系变得复杂,可以认为信号集内存在多种类信号,且类内相关性强而类间相关性弱,即耦合程度是内紧外松,因此需要从全局角度考虑采样信号。在本文的方法中,首先提出一种有效的混合传输方式避免传输能耗的浪费。然后分析测量矩阵的选择和分类标准,以进一步提高信号的恢复精度。最后设计基于分类的重构算法并提出采样原则以避免冗余采样。实验显示,与传统压缩感知相比,信号在传输过程中能消减少60%左右,且采样量减少30%。
针对采样信号在取值空间内随机呈现且无法稀疏表示的情况,本文设计了基于有限区间随机信号的压缩感知方法。产生这种信号是因为：当环境时常发生变化时,采样信号也会随机呈现,并不存在任何关联,或者很难展现它们的关系,当然,由于感知元件的物理特性,信号区间是有限的,这意味着虽然采样信号是随机的,但其数据值却在一定区间内。根据这种情况,本文对无稀疏性的有限随机数据进行了稀疏编码。核心思想是将信号在统一的格式下稀疏化。为达到数据稀疏化的要求,对二进制数据简单编码,同时,进行适当分层,减少信号恢复过程中的匹配空间。最后设计了基于层稀疏的重构算法,减少了采样次数。理论证明,基于节点数n的数据包期望长度为o((?)n)。实验结果也验证了这一结论。
本文的创新点是：
1.基于局部区域的压缩感知方法压缩矩阵尽可能地提取出公共部分,极大地减少了采样量,并且对信号的压缩是在采样过程中同步实现的,因此不需要额外耗费传感器能量。
2.基于分类的压缩感知算法减少了采样次数且所提出的采样原则也避免了冗余采样采用。同时,采用混合传输方式减少了信号传输次数并尽可能的保证了网络的负载均衡。
3.基于有限区间随机信号的压缩感知方法将随机信号转换成格式统一的稀疏信号,满足了压缩感知要求,减少了采样,并且传输过程中的数据包长度变化稳定。
To connect things, a variety of sense components and equipments are adopted to gather and exchange information in real time and are combined with Internet as a huge network, which is called Internet of Things(IOT). In this huge network, the wirless technology is critical. In IOT, it is crucial to be sensed information of uncertain environment reliably and transmit them effectively. Recently, a novel signal sampling theory Compressive Sensing is proposed, which only acquires few samplings from sparse signals and then could recover them precisely. Therefore, it has been paid attention and applied in many fieds. Based on compressive sensing, sensors only need to transmission a small number of samplings to the termination, and then the receiver can recover the original signals accurately, which is an advantage for wireless sensor network.
However, it is difficult for uncertain environment information in IOT to be satified the requirement of sparsity of compressive sensing. Therefore, the characters of the sensed siginals should be further analyzed to obtain the compresiable content, and then design suitable approaches. Due to the diffucult of sparsing signals, this paper studies on signals which are appeared in three scenes in IOT and then design corresponding methords. The main contents in the paper are as follow:
Based on the scene that it is difficult for relative signals to obtain their sparsity, a compresensive sensing approach based on local regions has been proposed. The structure of this kind of data consists of two parts, which are common sparse part and special sparse part respectively. In this case, the former should be abstracted as much as possible. In this paper, two reasonable assumptions are given and then a spatical-temporal correlation model is presented. In this model, data gathered in local region satisfy spatical-temporal correlation in a certain extend. To abstract their common parts, compressive matrices are designed for four data distributions, which are uniform distribution, Guassian distribution, expential distribution and Poission distribution. For the two former ones, hybrid compressive matrices are proposed. For the two later ones, the functions of transformation are proved and the paramaters estimation could be easily achieved.
Based on the scene of multi-classifications signals, a compressive sensing method based on classification is proposed. The effective hybrid transmition schem is present, in which both the advantage of compressive sening is retained and the unnecessary energy cost is avoided. To increase the accurate of reconstructing signals, the choice of measurement matrices and the number of classes are considered. The classification-based algorithm is designed and the principle of sampling is presented to decrease required samplings and redundant samplings.
Based on the scene of random signals without sparsity, a compressive sensing approach for limited random signals is proposed. Due to the limitation of the sensing ability of equipments, the gathered data are limited.. In this limited range, random signals are encoded for sparsity, which turn this kind of signals into sparse ones in a uniform formate to satisfy the requirement of theory of compressive sensing. To satisfy the requeirement of CS, binary digits are sparsing encoded. Meanwhile, suitably layering binary digits to decrease the spase of matching and increase the success of pursuiting. Afterwards, the reconstruction algorithm based on layer is designed to reduce the requirement of samplings.
Our contributions in the paper are as follows:
1. The CS approach based on local region data abstract the common part from the data which immensely the required number of samplings and the process of compressing signal need rarely energy cost.
2. The CS approach based on classification reduces the samplings and avoids redundant ones. Meanwhile, the hybrid scheme decreases the number of signal transmission and guarantees the load balance.
4. The CS approach based on limited random signals transforms random signas to sparase signals with an uniform format which satisfies the requriemnt of CS and is in favor of diminish samplings, and the lengh of package is stable in the process of transmission.
引文
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