The interplay between signal processing and networking in sensor networks

This paper provides a signal processing perspective on large-scale sensor networks. It focuses on two characteristics of sensor networks: application specificity and energy constraint. The former requires that network protocols be designed for the application at hand, which is often signal processing in nature, and measured by application-specific metrics. The latter calls for novel distributed signal processing techniques to provide accurate and timely network state information that can be exploited by network protocols to ensure energy efficiency. The underlying theme is about how a principled integration of signal processing and networking can lead to an efficient and fair use of limited resources. The paper aims to demonstrate that capturing and exploiting dependencies between signal processing and networking offer design choices resulting in improved network performance.     

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.     

Using signal processing techniques to model worm propagation over wireless sensor networks

This paper describes how a topologically aware worm propagation model (TWPM) for wireless sensor networks is formulated and derived. The paper defines worm propagation characteristics that are specific to sensor networks. It also parameterize the effects of physical channel conditions, medium access control (MAC) layer contention, network layer routing, and transport layer protocol on worm propagation in sensor networks. The basic model formulation results in a partial differential equation, which is solved in the frequency domain to yield a closed-form solution for the TWPM. It is shown that in the spatial domain, the TWPM spread function is low-pass filtered by a two-dimensional isotropic Gaussian filter, thereby providing an intuitive feel for the dependence of the model on its underlying parameters. Simulation results demonstrate that the TWPM provides an effective and accurate worm propagation model for sensor networks     

Joint multiple target tracking and classification in collaborative sensor networks

We address the problem of jointly tracking and classifying several targets within a sensor network where false detections are present. In order to meet the requirements inherent to sensor networks such as distributed processing and low-power consumption, a collaborative signal processing algorithm is presented. At any time, for a given tracked target, only one sensor is active. This leader node is focused on a single target but takes into account the possible existence of other targets. It is assumed that the motion model of a given target belongs to one of several classes. This class-target dynamic association is the basis of our classification criterion. We propose an algorithm based on the sequential Monte Carlo (SMC) filtering of jump Markov systems to track the dynamic of the system and make the corresponding estimates. A novel class-based resampling scheme is developed in order to get a robust classification of the targets. Furthermore, an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC target tracking framework. Simulation results are presented to illustrate the excellent performance of the proposed multitarget tracking and classification scheme in a collaborative sensor network.     

无线传感器网络协同任务分配研究

无线传感器网络的任务协同主要是任务的描述、分解、分配、调度和执行。任务分配是任务协同的主要内容．任务分配的方案直接决定着网络能耗,从而影响网络的生命周期。着重分析了无线传感器网络协同技术以及启发式算法解决任务分配的问题,并给出了无线传感器网络任务分配需要进一步研究的内容和方向。     

Real-time multimedia processing in video sensor networks

Video sensor networks (VSNs) has become the recent research focus due to the rich information it provides to address various data-hungry applications. However, VSN implementations face stringent constraints of limited communication bandwidth, processing capability, and power supply. In-network processing has been proposed as efficient means to address these problems. The key component of in-network processing, task mapping and scheduling problem, is investigated in this paper. Although task mapping and scheduling in wired networks of processors has been extensively studied, their application to VSNs remains largely unexplored. Existing algorithms cannot be directly implemented in VSNs due to limited resource availability and shared wireless communication medium. In this work, an application-independent task mapping and scheduling solution in multi-hop VSNs is presented that provides real-time guarantees to process video feeds. The processed data is smaller in volume which further releases the burden on the end-to-end communication. Using a novel multi-hop channel model and a communication scheduling algorithm, computation tasks and associated communication events are scheduled simultaneously with a dynamic critical-path scheduling algorithm. Dynamic voltage scaling (DVS) mechanism is implemented to further optimize energy consumption. According to the simulation results, the proposed solution outperforms existing mechanisms in terms of guaranteeing application deadlines with minimum energy consumption.     

Semantic information processing in industrial networks

The industrial Internet of things(industrial IoT, IIoT) aims at connecting everything, which poses severe challenges to existing wireless communication. To handle the demand for massive access in future industrial networks, semantic information processing is integrated into communication systems so as to improve the effectiveness and efficiency of data transmission. The semantic paradigm is particularly suitable for the purpose-oriented information exchanging scheme in industrial networks. To il...     

Spatial correlation-based collaborative medium access control in wireless sensor networks

Wireless Sensor Networks (WSN) are mainly characterized by dense deployment of sensor nodes which collectively transmit information about sensed events to the sink. Due to the spatial correlation between sensor nodes subject to observed events, it may not be necessary for every sensor node to transmit its data. This paper shows how the spatial correlation can be exploited on the Medium Access Control (MAC) layer. To the best of our knowledge, this is the first effort which exploits spatial correlation in WSN on the MAC layer. A theoretical framework is developed for transmission regulation of sensor nodes under a distortion constraint. It is shown that a sensor node can act as a representative node for several other sensor nodes observing the correlated data. Based on the theoretical framework, a distributed, spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol is then designed which has two components: Event MAC (E-MAC) and Network MAC (N-MAC). E-MAC filters out the correlation in sensor records while N-MAC prioritizes the transmission of route-thru packets. Simulation results show that CC-MAC achieves high performance in terms energy, packet drop rate, and latency.     

Connected sensor cover for area information coverage in wireless sensor networks

Coverage is an important issue in wireless sensor networks (WSNs) and is often used to measure how well a sensor field is monitored by the deployed sensors. If the area covered by a sensor can also be covered by some other sensors, this sensor can go into an energy‐saving sleep state without sacrificing the coverage requirement. In this paper, we study the problem of how to select active sensors with the constraints that the selected active sensors can provide complete field coverage and are completely connected. We propose to use the notion of information coverage, which is based on estimation theory to exploit the collaborative nature of WSNs, instead of using the conventional definition of coverage. Owing to the use of information coverage, a point that is not within the sensing disk of any sensor can still be considered to be covered without loss of estimation reliability. We propose a heuristic to approximately solve our problem. The basic idea is to grow a connected sensor tree to maximize the profit from the covered points of the selected sensors in each step. Simulations are used to validate the effectiveness of the proposed algorithm and the results illustrate that the number of active sensors to provide area coverage can be greatly reduced by using the notion of information coverage compared with that by using the conventional definition of coverage. Copyright © 2008 John Wiley & Sons, Ltd.     

Optimal information extraction in energy-limited wireless sensor networks

The current practice in wireless sensor networks (WSNs) is to develop functional system designs and protocols for information extraction using intuition and heuristics, and validate them through simulations and implementations. We address the need for a complementary formal methodology by developing nonlinear optimization models of static WSN that yield fundamental performance bounds and optimal designs. We present models both for maximizing the total information gathered subject to energy constraints (on sensing, transmission, and reception), and for minimizing the energy usage subject to information constraints. Other constraints in these models correspond to fairness and channel capacity (assuming noise but no interference). We also discuss extensions of these models that can handle data aggregation, interference, and even node mobility. We present results and illustrations from computational experiments using these models that show how the optimal solution varies as a function of the energy/information constraints, network size, fairness constraints, and reception power. We also compare the performance of some simple heuristics with respect to the optimal solutions.     

All-optical signal processing technologies in flexible optical networks

Photonic Network Communications - All-optical signal processing has always been critical assistance for the flexible optical networks (FONs) development which realizes various signal processing...     

基于多信道协作负载均衡的WSN路由

在无线传感网络中,节点的资源限制给路由协议的设计提出了挑战。在高数据率应用场景中,带宽和存储容量成为其主要问题。为此,提出基于多信道协作负载均衡算法(M-CoLBA)的路由协议来提升网络带宽,并避免因队列溢出导致的数据包丢失。M-CoLBA协议先利用拥塞感知的动态路由度量均衡流量负载,再依据队列时延选择下一跳转发节点。实验数据表明,与单一信道路由协议(S-CoLBA)和多信道协议(M-HopCount)相比,提出的M-CoLBA协议具有较高的数据包传递率。     

Optical signal processing for lightwave communications networks

A number of optical signal processing functions that might be potentially important for future lightwave communication networks are described. An optical network with a distribution capacity of 100 HDTV channels is considered along with how such a network can be implemented using the following functional subsystems: frequency converters; transmitter banks; modified (wavelength division multiplexing) WDM demultiplexers; and tunable optical receivers. Discussed are the key network-level issues: the power budget, the channel separation, and the overall rationale for selection of multiplexing techniques. A hardware implementation of the functional subsystems using three basic building blocks-tunable amplifiers/filters, phase locked loops, and comb generators-is discussed     

光纤电压传感的DSP实现

光纤电压传感可以用于电网电压的监测和保护.本文在体调制型电压传感器的原理基础上,提出信息处理单元采用DSP技术,讨论了整个系统的构成、信息处理单元的硬件和软件结构.     

Scheduling sensor activity for information coverage of discrete targets in sensor networks

In this paper, we study the problem of scheduling sensor activity to cover a set of targets with known locations such that all targets can be monitored all the time and the network can operate as long as possible. A solution to this scheduling problem is to partition all sensors into some sensor covers such that each cover can monitor all targets and the covers are activated sequentially. In this paper, we propose to provide information coverage instead of the conventional sensing disk coverage for target. The notion of information coverage is based on estimation theory to exploit the collaborative nature of geographically distributed sensors. Due to the use of information coverage, a target that is not within the sensing disk of any single sensor can still be considered to be monitored (information covered) by the cooperation of more than one sensor. This change of the problem settings complicates the solutions compared to that by using a disk coverage model. We first define the target information coverage (TIC) problem and prove its NP‐completeness. We then propose a heuristic to approximately solve our problem. Simulation results show that our heuristic is better than an existing algorithm and is close to the upper bound when only the sensing disk coverage model is used. Furthermore, simulation results also show that the network lifetime can be significantly improved by using the notion of information coverage compared with that by using the conventional definition of sensing disk coverage. Copyright © 2008 John Wiley & Sons, Ltd.     

基于磁通门传感器的数字信号处理系统

针对磁通门传感器信号处理电路中模拟元器件存在的温度性能较差、维护和升级困难、且元件之间容易出现感应等缺点,提出了利用DSP的磁通门信号处理电路的数字化解决方案,设计了硬件设计,并给出了信号处理中相敏检波、低通滤波的软件设计。实验结果表明,系统灵敏度为28.67m V/Am-1,线性度为2.38%,且具有稳定性好、抗干扰能力强、升级方便等特点。