Side information aware coding strategies for sensor networks

We develop coding strategies for estimation under communication constraints in tree-structured sensor networks. The strategies have a modular and decentralized architecture. This promotes the flexibility, robustness, and scalability that wireless sensor networks need to operate in uncertain, changing, and resource-constrained environments. The strategies are based on a generalization of Wyner-Ziv source coding with decoder side information. We develop solutions for general trees, and illustrate our results in serial (pipeline) and parallel (hub-and-spoke) networks. Additionally, the strategies can be applied to other network information theory problems. They have a successive coding structure that gives an inherently less complex way to attain a number of prior results, as well as some novel results, for the Chief Executive Officer problem, multiterminal source coding, and certain classes of relay channels.     

A distributed and adaptive signal processing approach to exploiting correlation in sensor networks

We propose a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm.¹ While the topic of energy-aware routing to alleviate energy consumption in sensor networks has received attention recently [C. Toh, IEEE Commun. Mag. June (2001) 138; R. Shah, J. Rabaey, Proc. IEEE WCNC, March 2002], in this paper, we propose an orthogonal approach to complement previous methods. Specifically, we propose a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. Our approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive inter-sensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data gathering unit to continuously learn the relevant correlation structures in the sensor data. Applying the algorithm to testbed data resulted in energy savings of 10–65% for a multitude of sensor modalities.     

Sensing reality and communicating bits: a dangerous liaison

To illustrate the conceptual issues related to sampling, source representation/coding and communication in sensor networks, we review the underlying theory and discuss specific examples. We show how the structure of the distributed sensing and communication problem dictates new processing architectures. The key challenge lies in the discretization of space, time and amplitude, since most of the advanced signal processing systems operate in discrete domain.     

Communication architecture for processing spatio-temporal continuous queries in sensor networks

Wireless sensor networks have revolutionized distributed micro-sensing because of their ease of deployment, ad hoc connectivity and cost-effectiveness. They have also enabled collecting and monitoring data from a very large area or possibly several independent areas geographically separated from each other and such a process is known as spatio-temporal data monitoring. In this paper, we define an energy-aware routing infrastructure that enables distributed query processing and supports processing of spatio-temporal queries within the network. As operator execution demands high computation capability, we propose a possible use of a heterogeneous sensor network where query operators are assigned to sparsely-deployed resource-rich nodes within a dense network of low power sensor nodes. We have designed an adaptive, decentralized, low communication overhead algorithm to determine optimal operator placement on the resource-rich nodes such that data transfer cost in the network is minimized. To the best of our knowledge, this is the first attempt to build an energy-aware communication architecture to enable in-network processing of spatio-temporal queries.     

Dual link distributed source coding scheme for the transmission of satellite hyperspectral imagery

Traditional lossless compression methods for satellite hyperspectral imagery focus on exploiting spatial and/or spectral redundancy. Those methods do not consider the temporal redundancy between images of the same area that are captured at different times. To exploit the temporal redundancy between hyperspectral images and reduce the amount of information to be transmitted from the space-satellite to the ground station via the downlink, this paper introduces a dual link distributed source coding (DLDSC) scheme for hyperspectral space-satellite communication. The proposed scheme employs the space-satellite dual link (i.e., the downlink and the uplink). The satellite onboard uses some side information from the ground station to calculate the hyperspectral image band coset values, and then, without syndrome coding, transmits to the ground station via the downlink. Coset coding is a typical technique used in distributed source coding (DSC), and here the coset values represent the timely hyperspectral image details. Typically, the coset values have lower entropy than that of the original source values. To exploit the temporal redundancy, the side information is computed in the ground station using the image captured at the previous time for the same area and transmitted to the space-satellite via the uplink. Hyperspectral images from the Hyperion satellite are used for the validation of the proposed scheme. The experimental results indicate that the proposed DLDSC scheme can reduce the original signal entropy by approximately 3.2 bits per sample (bps) and can achieve up to 1.0 bps and 1.6 bps gains over the lossless JPEG2000 standard and the state-of-art predictive CCSDS-123 method, respectively.     

无线传感器网络WSN探究

集传感器技术、无线传输技术、网络通信技术、分布式信息处理技术与编程技术于一体而形成的无线传感器网络（WSN）,是一种全新的信息获取和处理系统,具有十分广阔的应用前景。介绍了无线传感器网络WSN的发展历程和现状,论述了无线传感器网络WSN系统的构成,阐述了WSN系统的主要技术指标和设计特点。同时,结合实际应用,对无线传感器网络系统的架构、网络通信模型结构以及当前的主要应用领域作了深入地探讨和研究,最后指出了当前WSN关键技术亟需解决的问题。     

Efficient Sensing Topology Management for Spatial Monitoring with Sensor Networks

We focus on exploiting redundancy for sensor networks in the context of spatial interpolation. The network acts as a distributed sampling system, where sensors periodically sample a physical phenomenon of interest, e.g. temperature. Samples are then used to construct a continuous spatial estimate of the phenomenon over time through interpolation. In this regime, the notion of sensing range typically utilized to characterize redundancy in event detection applications is meaningless and sensor selection schemes based on it become unsuitable. Instead, this paper presents pragmatic approaches for exploiting redundancy in such applications. Their underlying characteristic is that no a-priori assumptions need to be made on the statistical properties of the physical phenomenon. These are instead learned by the network after deployment. Our approaches are evaluated through real as well as synthetic sensor network data showing that significant reductions in the number of active sensors are indeed possible.     

无线传感器网络与自组织网络的研究现状

传感器网络综合了传感器技术、嵌入式计算技术、分布式信息处理技术和无线通信技术,是计算机科学技术的一个新的研究领域;自组织网络是一种移动、多跳、自律式系统,两者均具有十分广阔的应用前景.文章对传感器网络和自组织网络的概念、特征和发展动态等问题进行了介绍,重点分析了目前这两种网络的关键技术和研究现状.文章认为:传感器网络在特殊领域有着传统技术不可比拟的优势,而无线自组织网络通过临时组网的方式在恶劣环境中支持移动节点之间的无线传输,应用范围广阔.对这些网络的研究是中国未来高技术民用和军事发展的需要.     

Towards a flexible trust establishment framework for sensor networks

Wireless sensor networks highly depend on the distributed cooperation among network nodes. Trust establishment frameworks provide the means for representing, evaluating, maintaining and distributing trust within the network, and serve as the basis for higher level security services. In this paper, we propose a trust establishment framework targeted sensor networks that can uniformly support the needs of nodes with highly diverse network roles and capabilities, by exploiting the pre-deployment knowledge on the network topology and the information flows. The framework allows for flexibility by combining aspects from alternative approaches on trust establishment on common evaluation metrics, and enables controlled trust evolution based on the network pre-configuration.     

无线传感器网络的体系结构与关键技术

无线传感器网络（WSN）是通过无线通信方式形成的一个多跳自组织网络。它综合了微传感器、微机电系统（MEMS）、嵌入式、网络通信和分布式信息处理等技术,是集信息采集、信息传输、信息处理于一体的智能化信息系统。本文主要介绍了无线传感器网络的体系结构、传感器节点的组成、协议栈和关键技术。     

A joint resource allocation‐channel coding design based on distributed source coding

Wireless sensor networks (WSNs) have found a wide variety of applications recently. However, the challenges in WSNs still remain in improving the sensor energy efficiency and information quality (distortion reduction) of the sensing data transmissions. In this paper, we propose a novel cross‐layer design of resource allocation and channel coding to protect distributed source coding (DSC)‐based data transmission. Resource allocation strategies include rate adaptation and automatic repeat‐request retransmissions. Our proposed joint design of resource allocation, channel coding, and DSC can improve the network energy efficiency and information quality while meeting the data transmission latency requirements. Further, we investigate how the resource allocation enables the network to achieve unequal error protection among correlated DSC streams. Our simulation studies demonstrate that the proposed joint design significantly improves the DSC‐based data transmission quality and the network energy efficiency. Copyright © 2013 John Wiley & Sons, Ltd.     

A distributed lightweight Redundancy aware Topology Control Protocol for wireless sensor networks

WSN consists of a large number of sensor nodes randomly deployed, and, in many cases, it is impossible to replace sensors when a node failure occurs. Thus, applications tend to deploy more nodes than necessary to cope with possible node failures and to increase the network lifetime, which leads to create some sensing and communication redundancy. However, sensors in the same region, may collect and forward the same information, which will waste more energy. In this paper, we propose a distributed Lightweight Redundancy aware Topology Control Protocol (LRTCP) for wireless sensor networks. It exploits the sensor redundancy in the same region by dividing the network into groups so that a connected backbone can be maintained by keeping a minimum of working nodes and turning off the redundant ones. LRTCP identifies equivalent nodes in terms of communication based on their redundancy degrees with respect of some eligibility rules. Simulation results indicate that, compared with existing distributed topology control algorithms, LRTCP improves network capacity and energy efficiency.     

Distributed video coding in wireless sensor networks

This paper addresses the important aspect of compressing and transmitting video signals generated by wireless broadband networks while heeding the architectural demands imposed by these networks in terms of energy constraints as well as the channel uncertainty related to the wireless communication medium. Driven by the need to develop light, robust, energy-efficient, and low delay video delivery schemes, a distributed video coding based framework dubbed PRISM is introduced. PRISM addresses the wireless video sensor network requirements far more effectively than current state-of-the-art standards like MPEG. This paper focuses on the case of a single video camera and use it as a platform to describe the theoretical principles and practical aspects underlying distributed video coding.     

一种分簇无线传感器网络中的分布式信源编码算法

针对密集型无线传感器网络中信息存在大量冗余的问题,该文提出了一种适用于分簇无线传感网络的分布式信源编码算法.该算法以边信息作为初始参考信源,利用信源间的相关性来决定各信源的编码顺序和参考信源,然后由各信源相对于参考信源进行相关编码,接收端则根据编码顺序和参考信源进行相关译码.该文同时针对模值编码的方式,给出了一种低复杂度的译码算法.理论分析和仿真结果表明,将该算法应用于分簇路由协议中可以有效地降低节点的发送比特数,从而降低网络的能耗以延长网络寿命.     

Source localization with distributed sensor arrays and partial spatial coherence

Multiple sensor arrays provide the means for highly accurate localization of the (x,y) position of a source. In some applications, such as microphone arrays receiving aeroacoustic signals from ground vehicles, random fluctuations in the air lead to frequency-selective coherence losses in the signals that arrive at widely separated sensors. We present performance analysis for localization of a wideband source using multiple, distributed sensor arrays. The wavefronts are modeled with perfect spatial coherence over individual arrays and frequency-selective coherence between distinct arrays, and the sensor signals are modeled as wideband, Gaussian random processes. Analysis of the Cramer-Rao bound (CRB) on source localization accuracy reveals that a distributed processing scheme involving bearing estimation at the individual arrays and time-delay estimation (TDE) between sensors on different arrays performs nearly as well as the optimum scheme while requiring less communication bandwidth with a central processing node. We develop Ziv-Zakai bounds for TDE with partially coherent signals in order to study the achievability of the CRB. This analysis shows that a threshold value of coherence is required in order to achieve accurate time-delay estimates, and the threshold coherence value depends on the source signal bandwidth, the additive noise level, and the observation time. Results are included based on processing measured aeroacoustic data from ground vehicles to illustrate the frequency-dependent signal coherence and the TDE performance.     

Distributed source coding using short to moderate length rate-compatible LDPC codes: the entire Slepian-Wolf rate region

In this paper, we propose a scheme for distributed source coding of correlated sources using a single systematic LDPC code. In particular, since we are interested in wireless sensor network applications, we consider LDPC codes with short to moderate lengths that achieve every arbitrary coding rate on the Slepian-Wolf rate region. We simplify the distributed source coding problem to the rate-compatible LDPC code design with an unequal error protection property. The decoders communicate to each other to exchange information bits prior to decoding. However, thereafter, each performs the decoding independently. Therefore, errors in one decoder do not affect the other one. The simulation results confirm that the gap from the theoretical limit remains almost the same for different rates on the Slepian-Wolf rate region. First, we consider two correlated sources. We show that our proposed scheme improves the performance of distributed source coding of two sources considerably. This benefit is more stressed for application with short to moderate length sequences. Then, we study distributed source coding of three sources. As a special case, we investigate three sources that are pairwise correlated with the same correlation probability. We show that the gap from the theoretical limit is smaller than that of previous work. We also investigate the distributed source coding of correlated sources when there is no prior knowledge of the correlation parameter at the time of code design. We note that although the proposed distributed source coding is well suited for sensor networks (where sequences with less than 10000 bits are used), the method can be generalized to other distributed source coding applications.     

Duality between source coding and channel coding and its extension to the side information case

We explore the information-theoretic duality between source coding with side information at the decoder and channel coding with side information at the encoder. We begin with a mathematical characterization of the functional duality between classical source and channel coding, formulating the precise conditions under which the optimal encoder for one problem is functionally identical to the optimal decoder for the other problem. We then extend this functional duality to the case of coding with side information. By invoking this duality, we are able to generalize the result of Wyner and Ziv (1976) relating to no rate loss for source coding with side information from Gaussian to more arbitrary distributions. We consider several examples corresponding to both discrete- and continuous-valued cases to illustrate our formulation. For the Gaussian cases of coding with side information, we invoke geometric arguments to provide further insights into their duality. Our geometric treatment inspires the construction and dual use of practical coset codes for a large class of emerging applications for coding with side information, such as distributed sensor networks, watermarking, and information-hiding communication systems.     

无线传感器网络中的分布式压缩感知技术

本文对无线传感器网络中分布式压缩感知的几个关键技术进行了详细阐述。首先,简要论述了压缩感知方法的基本原理;其次,分析了无线传感器网络中的分布式压缩感知技术与单个信号的压缩感知技术的区别,针对无线传感器网络中联合稀疏模型的建立、分布式信源编码以及联合稀疏信号的重构技术等问题进行了详细讨论;分析了在无线传感器网络的实际应用中,联合稀疏模型、分布式信源编码方式及联合稀疏信号重构方法的性能。最后,对无线传感器网络中分布式压缩感知技术的未来研究方向进行了展望。      

Nested linear/lattice codes for structured multiterminal binning

Network information theory promises high gains over simple point-to-point communication techniques, at the cost of higher complexity. However, lack of structured coding schemes limited the practical application of these concepts so far. One of the basic elements of a network code is the binning scheme. Wyner (1974, 1978) and other researchers proposed various forms of coset codes for efficient binning, yet these schemes were applicable only for lossless source (or noiseless channel) network coding. To extend the algebraic binning approach to lossy source (or noisy channel) network coding, previous work proposed the idea of nested codes, or more specifically, nested parity-check codes for the binary case and nested lattices in the continuous case. These ideas connect network information theory with the rich areas of linear codes and lattice codes, and have strong potential for practical applications. We review these developments and explore their tight relation to concepts such as combined shaping and precoding, coding for memories with defects, and digital watermarking. We also propose a few novel applications adhering to a unified approach     

高冗余光纤光栅传感网络的设计与实验研究

结合无源光学器件,提出并设计了一种新型的高冗余光纤布拉格光栅(FBG)传感模块,并将设计的FBG传感模块与波分复用技术相结合,构建了高冗余FBG传感网络.以长方形铝合金板为研究对象,对高冗余FBG传感网络的可靠性进行研究,理论比较并实验分析了高冗余FBG传感阵列的适用性与可靠性.研究结果表明,利用光开关在传感阵列支路之间的切换,使得FBG传感网络更具有冗余性.这一方面能够解决使用过程中多个部位出现故障导致的某些FBG传感模块无法被计算机检测到的问题,有效提高了传感系统的可靠性、容错性;另一方面为工程应用中结构健康监测以及特殊部位监测提供了一种有效可行的监测手段.