压缩感知技术在无线传感器网络与物联网中的信号与数据采集
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"Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things"
本文是关于压缩感知（Compressed Sensing, CS）在无线传感器网络和物联网中的应用研究论文。压缩感知是一种新兴的信号处理技术，它能显著减少采样点的数量，从而减少收集到的数据量，避免了对部分数据的无效采集。这一特性使得在物联网应用中可以构建资源需求更少的网络中心化和独立应用程序。
在传统信号处理中，奈奎斯特定理规定了为了无损地重构信号，采样率必须至少等于信号最高频率的两倍。然而，压缩感知理论打破了这一限制，尤其是在信号是稀疏或近似稀疏的情况下。稀疏性意味着信号可以在某个基下用较少的非零系数表示。CS方法结合了非线性的重构算法和随机采样，能在稀疏基上有效地进行信号的压缩和信息的获取。
论文深入探讨了如何利用压缩感知来提供对无线传感器网络和物联网中数据采样和获取的新视角。在这些网络中，节点通常具有有限的计算能力和存储资源，而数据传输和存储成本高。通过CS，不仅可以降低数据采集的复杂性和能耗，还可以提高网络的效率和生存能力。此外，由于CS允许不均匀的采样，因此可以优先处理关键或异常数据，增强系统的监控和故障检测能力。
具体来说，文章可能涵盖了以下几个方面：
1. 压缩感知的基本原理：介绍压缩感知的数学基础，包括稀疏表示、采样矩阵设计和重构算法，如L1最小化和迭代软阈值算法。
2. 无线传感器网络与物联网的挑战：分析WSN和IoT中数据采集的挑战，如能量受限、带宽限制和存储容量问题。
3. CS在WSN和IoT中的应用策略：讨论如何设计适应网络特性的CS采样策略，以及如何优化数据压缩和恢复过程。
4. 实验与性能评估：通过仿真或实际测试验证CS技术在降低通信负载、提高能效和保证数据恢复质量方面的效果。
5. 展望与未来研究方向：提出进一步的研究课题，如在动态环境中的实时CS应用、多传感器协作下的CS策略以及CS与机器学习、人工智能等其他先进技术的融合。
该论文对压缩感知在无线传感器网络和物联网领域的应用进行了深入研究，为解决资源受限环境下的数据采集问题提供了新的思路和解决方案。其研究成果有望推动物联网系统的设计和优化，实现更高效、节能的信息获取与处理。
Copyright (c) 2011 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing pubs-permissions@ieee.org.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.
1
Compressed Sensing Signal and Data Acquisition in
Wireless Sensor Networks and Internet of Things
Shancang Li, Li Da Xu, and Xinheng Wang
Abstract—The emerging compressed sensing (CS) method
reduces the number of sampling points that directly correspond
to the volume of data collected, which means that part of
the data that would be thrown away is never acquired. This
enables the creation of net-centric and stand-alone applications
with fewer resources required in internet of things (IoT). CS-
based signal and information acquisition/compression paradigm
combines the nonlinear reconstruction algorithm and random
sampling in a sparse basis that provides a promising approach to
compressing signal and data in information systems. This paper
investigates how CS can provide new insights into data sampling
and acquisition in wireless sensor networks and IoT. At first, we
briefly introduce the CS theory in aspects of the sampling and
transmission coordination during the network lifetime through
providing simple coding process with low computation costs.
Next, a compressed sensing-based framework is proposed for
networks, in which the nodes measure, transmit, and store
the sampled data in the compressed sensing framework. Then,
a cluster-sparse reconstruction algorithm is proposed for in-
network compression aiming at more accurate data reconstruc-
tion and longer network lifetime. Performance is evaluated with
respect to network size using datasets acquired by a real-life
deployment.
Index Terms—Compressed sensing, Wireless Sensor Networks,
Industrial Informatics, Internet of Things, Information Systems,
Enterprise Systems
I. INTRODUCTION
Researchers found that, in information systems, wireless
sensor networks (WSNs), and Internet of Things (IoT), many
types of information have a property called sparseness in trans-
formation process which allows certain number of samples
enabling capturing all required information without loss of
information even after reduction [1], [2], [3], [4]. The emerg-
ing IoT is a technological revolution that represents the future
of computing and communications [1], [2]. IoT is designed
to be a world-wide network of interconnected objects, and its
development depends on technological innovation in a number
of fields from WSNs to nanotechnology [2], [3], [4]. In IoT-
based information systems, an unobtrusive and cost-effective
Manuscript received December 28, 2011; accepted February 14, 2012.
This work was partially supported by the NSFC (National Natural Science
Foundation of China) Grants 71132008, 81101118, and 60972038, Changjiang
Scholar Program of the Ministry of Education of China, and the US National
Science Foundation Grant 1044845.
Copyright (c) 2009 IEEE. Personal use of this material is permitted.
However, permission to use this material for any other purposes must be
obtained from the IEEE by sending a request to pubs-permissions@ieee.org.
S. Li and X. Wang are with College of Engineering, Swansea University,
Swansea SA2 8PP, UK (Phone: +44-1792-602802, fax: +44-1792-602802,
Email:s.li@swansea.ac.uk).
L. Xu is with the Institute of Computing Technology, Chinese Academy
of Sciences, Beijing 100190, China; Old Dominion University, Norfolk, VA
23529, USA (lxu@odu.edu).
data acquisition system is necessary to collect and process data
[2].As the advances in miniaturization and nanotechnology
imply that the interconnected objects may have the ability to
interact and connect, these developments will create an IoT
that connects objects in both a sensory and an intelligent
manner [3], [5], [6]. WSNs have the potential of a wide
range of applications in many industrial systems. WSNs can
be integrated into the IoT, which consists of a number of
interconnected sensor nodes [3], [4], [5].
It is possible that an IoT system can be with thousands of
independent components including computers, sensors, RFID
tags, or mobile phones, all capable of generating and commu-
nicating data, in which many techniques are used or developed
for data collection, processing, and compression [2], [4], [7].
In IoT, a desirable data compression ratio is very important,
which cannot be obtained by current methods without in-
troducing unacceptable distortions [8], [9]. Furthermore, for
most data compression problems in IoT, three main problems
must be solved: resolution, sensitivity, and reliability [2], [10],
[11], [12]. Recently, an effective data compression theory has
emerged that shows the data can be reconstructed from far
fewer samples (measurements) than what the Nyquist/Shannon
sampling theory states. This new CS theory relies on the
compressibility of signals, or more precisely, on the property
that signals can be sparsely represented [3], [4], [8], [13]. From
the compressed sensing viewpoints, sparse signals could be
acquired at low sampling rates without loss of information
[14]. The compressed sensing changes the rule of data acqui-
sition game in information systems by exploiting a priori data
sparsity information [5].
This paper considers a particular situation that involves with
distributed information source of data and their acquisition,
transmission, storage, and processing in a large scale IoT
[15]. The task of transmitting information from one node to
another is a common task, however, the problem of efficient
acquisition, storage, and transmitting from and among a large
number of source nodes remains a great challenge in large-
scale networked systems [4], [14].
Consider a network of n nodes in which each node has
a piece of information or data x
j
, j = 1, . . . n. Assume
that each x
j
is a scalar value. Collectively these data x =
[x
1
, . . . , x
n
]
T
is arranged in a vector, namely measurements.
These measurements are distributed and can be shared over
the network. Since the IoT may be very large, the process
of gathering x at a single node is inefficient and unreliable.
However, it is possible to effectively construct a highly com-
pressed decentralized version of x, which can be processed to
reconstruct x within a reasonable accuracy [4], [16], [17].
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