Collaborative Signal Processing Research Articles

On the Design of Probe Signals in Wireless Acoustic Sensor Networks Self-Positioning Algorithms

A wireless acoustic sensor network comprises a distributed group of devices equipped with audio transducers. Typically, these devices can interoperate with each other using wireless links and perform collaborative audio signal processing. Ranging and self-positioning of the network nodes are examples of tasks that can be carried out collaboratively using acoustic signals. However, the environmental conditions can distort the emitted signals and complicate the ranging process. In this context, the selection of proper acoustic signals can facilitate the attainment of this goal and improve the localization accuracy. This letter deals with the design and evaluation of acoustic probe signals allowing the implementation of ranging applications using unsynchronized audio signals acquired by different network nodes. The proposed signals have been evaluated in diverse simulated environments and implemented in a real deployment. The results confirm the suitability of these signals for implementing localization applications with an accuracy that is in the order of 1 cm.

Parallel Nonuniform Discrete Fourier Transform (P-NDFT) Over a Random Wireless Sensor Network

Reduced execution time and increased power efficiency are important objectives in the distributed execution of collaborative signal processing tasks over wireless sensor networks (WSNs). Meanwhile, Fourier transforms are among the most widely used frequency analysis tools in WSNs for studying the behavior of sensed phenomena. Several energy-efficient in-network Fourier transform computation algorithms have been proposed for WSNs. Most of these works assume that the sensors are equally spaced over a one-dimensional (1D) region. However, in practice, the sensors are usually randomly distributed over a two-dimensional (2D) plane. Consequently, the conventional 2D Fast Fourier Transform (FFT) designed for data sampled on a uniform grid is not applicable in such environments. We address this problem by designing a distributed hybrid structure consisting of local Non-equispaced Discrete Fourier Transform (NDFT) and global FFT computations. First, the NDFT method is applied within suitably selected clusters to obtain the initial uniform Fourier coefficients within allowable estimation error bounds. We investigate both classical linear and generalized interpolation methods for computing the NDFT coefficients within each cluster. Second, a separable 2D FFT is applied over all clusters using our proposed energy-efficient 1D FFT computation method, which reduces communication costs by employing a novel binary representation mapping strategy for data exchanges between sensors. The proposed techniques are implemented on the SIDnet-SWANS platform, and the tradeoffs between communication cost, execution time, and energy consumption are studied.

Advances and challenges toward a scalable cloud radio access network

With centralized processing, cooperative radio, real-time cloud computing, and clean infrastructure, C-RAN is a future-proof solution to sustain the mobile data explosion in future wireless networks. The technology holds great potential in enhancing LTE with the necessary capability to accommodate the unprecedented traffic volume that today's wireless cellular system is facing. However, the high density of RRHs in C-RANs leads to severe scalability issues in terms of computational and implementation complexities. This article discusses the challenges and recent developments in the technologies that potentially address the scalability issues of C-RANs. In particular, we focus on the collaborative signal processing, resource management, and green architecture of C-RAN systems. This article is a humble attempt to draw the attention of the research community to the following important question: how to leverage the revolutionary architecture of C-RAN to attain unprecedented system capacity at an affordable cost and complexity.

Distributed compressed sensing for multi-sourced fusion and secure signal processing in private cloud

In this paper, a novel scheme is proposed for multi-sourced signal fusion and secure processing. Within a distributed compressed sensing (DCS) framework, traditional sampling, compression and encryption for signal acquisition are unified under the secure multiparty computation protocol. In the proposed scheme, generation of the pseudo-random sensing matrix offers a natural method for data encryption in DCS, allowing for joint recovery of multiparty data at legal users' side. Experimental analysis and results indicate that the secure signal processing and recovery in DCS domain is feasible, and requires fewer measurements than the achievable approach of separate CS and Nyquist processing. The proposed scheme can be also extended to other cloud-based collaborative secure signal processing and data-mining applications.

EMIP: energy-efficient itinerary planning for multiple mobile agents in wireless sensor network

As software entities that migrate among nodes, mobile agents (MAs) are able to deliver and execute codes for flexible application re-tasking, local processing, and collaborative signal and information processing. In contrast to the conventional wireless sensor network operations based on the client---server computing model, recent research has shown the efficiency of agent-based data collection and aggregation in collaborative and ubiquitous environments. In this paper, we consider the problem of calculating multiple itineraries for MAs to visit source nodes in parallel. Our algorithm iteratively partitions a directional sector zone where the source nodes are included in an itinerary. The length of an itinerary is controlled by the angle of the directional sector zone in such a way that near-optimal routes for MAs can be obtained by selecting the angle efficiently in an adaptive fashion. Simulation results confirm the effectiveness of the proposed algorithm as well as its performance gain over alternative approaches.

Fusion-based volcanic earthquake detection and timing in wireless sensor networks

Volcano monitoring is of great interest to public safety and scientific explorations. However, traditional volcanic instrumentation such as broadband seismometers are expensive, power hungry, bulky, and difficult to install. Wireless sensor networks (WSNs) offer the potential to monitor volcanoes on unprecedented spatial and temporal scales. However, current volcanic WSN systems often yield poor monitoring quality due to the limited sensing capability of low-cost sensors and unpredictable dynamics of volcanic activities. In this article, we propose a novel quality-driven approach to achieving real-time, distributed, and long-lived volcanic earthquake detection and timing. By employing novel in-network collaborative signal processing algorithms, our approach can meet stringent requirements on sensing quality (i.e., low false alarm/missing rate, short detection delay, and precise earthquake onset time) at low power consumption. We have implemented our algorithms in TinyOS and conducted extensive evaluation on a testbed of 24 TelosB motes as well as simulations based on real data traces collected during 5.5 months on an active volcano. We show that our approach yields near-zero false alarm/missing rate, less than one second of detection delay, and millisecond precision earthquake onset time while achieving up to six-fold energy reduction over the current data collection approach.

Collaborative Signal and Information Processing for Target Detection with Heterogeneous Sensor Networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce highperformance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behavior and capacity, target behavior and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield.

Adaptive calibration for fusion-based cyber-physical systems

Many Cyber-Physical Systems (CPS) are composed of low-cost devices that are deeply integrated with physical environments. As a result, the performance of a CPS system is inevitably undermined by various physical uncertainties , which include stochastic noises, hardware biases, unpredictable environment changes, and dynamics of the physical process of interest. Traditional solutions to these issues (e.g., device calibration and collaborative signal processing) work in an open-loop fashion and hence often fail to adapt to the uncertainties after system deployment. In this article, we propose an adaptive system-level calibration approach for a class of CPS systems whose primary objective is to detect events or targets of interest. Through collaborative data fusion, our calibration approach features a feedback control loop that exploits system heterogeneity to mitigate the impact of aforementioned uncertainties on the system performance. In contrast to existing heuristic-based solutions, our control-theoretical calibration algorithm can ensure provable system stability and convergence. We also develop a routing algorithm for fusion-based multihop CPS systems that is robust to communication unreliability and delay. Our approach is evaluated by both experiments on a testbed of Tmotes as well as extensive simulations based on data traces gathered from a real vehicle detection experiment. The results demonstrate that our calibration algorithm enables a CPS system to maintain the optimal sensing performance in the presence of various system and environmental dynamics.

Exploiting Data Fusion to Improve the Coverage of Wireless Sensor Networks

Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. An important performance measure of such applications is sensing coverage that characterizes how well a sensing field is monitored by a network. Although advanced collaborative signal processing algorithms have been adopted by many existing WSNs, most previous analytical studies on sensing coverage are conducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sensing. In this paper, we attempt to bridge this gap by exploring the fundamental limits of coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between coverage, network density, and signal-to-noise ratio (SNR). We show that data fusion can significantly improve sensing coverage by exploiting the collaboration among sensors when several physical properties of the target signal are known. In particular, for signal path loss exponent of (typically between 2.0 and 5.0), ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> = O(ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-1/k</sup> , where ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> and ρ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> are the densities of uniformly deployed sensors that achieve full coverage under the fusion and disc models, respectively. Moreover, data fusion can also reduce network density for regularly deployed networks and mobile networks where mobile sensors can relocate to fill coverage holes. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.

Fault tolerant spatio-temporal fusion for moving vehicle classification in wireless sensor networks

Wireless sensor networks (WSNs) can implement complicated tasks through collaboration among multiple sensor nodes. The low-cost sensors in WSNs often generate noisy and even faulty measurements, which will degrade the network performance. Therefore developing collaborative signal processing (CSP) algorithms that has high fault tolerance ability is necessary for the increasingly deployed WSNs. In this study, the authors propose a novel fault tolerant fusion scheme to implement reliable vehicle classification by integrating fault detection and correction with a spatio-temporal fusion structure. Sensor faults are detected at the fusion centre and then the fault detection results are fed back to the local sensors to update subsequent classification results. A Dempster-Shafer theory based fault correction strategy is devised to utilise the fusion centre feedback. Simulation results demonstrate that the proposed scheme ensures more than 95- of the classification results to be correct when no larger than 30- of the sensors are faulty and the scheme achieves improved fault detection rate and false alarm rate than the optimum threshold Bayesian fault detection scheme.

Nonlinear filtering in target tracking using cooperative mobile sensors

Collaborative signal processing and sensor deployment have been among the most important research tasks in target tracking using networked sensors. In this paper, the mathematical model is formulated for single target tracking using mobile nonlinear scalar range sensors. Then a sensor deployment strategy is proposed for the mobile sensors and a nonlinear convergent filter is built to estimate the trajectory of the target.

LWT Based Sensor Node Signal Processing in Vehicle Surveillance Distributed Sensor Network

Previous vehicle surveillance researches on distributed sensor network focused on overcoming power limitation and communication bandwidth constraints in sensor node. In spite of this constraints, vehicle surveillance sensor node must have signal compression, feature extraction, target localization, noise cancellation and collaborative signal processing with low computation and communication energy dissipation. In this paper, we introduce an algorithm for light-weight wireless sensor node signal processing based on lifting scheme wavelet analysis feature extraction in distributed sensor network.

Energy Efficient Hidden Markov ModelBased Target Tracking Mechanism in Wireless Sensor Networks

Problem statement: Target tracking is a challenging application in Wireless Sensor Networks (WSNs) because it is computation-intensive and requires real-time location processing. This study proposes a practical target tracking system based on the Hidden Markov Model in a distributed signal processing framework. In this framework, wireless sensor nodes perform target detection and tracking, whereas target localization requires the collaborative signal processing between wireless sensor nodes for improving the location accuracy and robustness. Approach: For carrying out target tracking under the constraints imposed by the limited transmission capabilities of the wireless sensor nodes, the HMM model and the particle filter approach are adopted in single wireless sensor node due to their outstanding performance and light computational calculations. Furthermore, a progressive multi sensor localization algorithm is proposed in distributed wireless sensor network considering the tradeoff between the localization accuracy of the target and the resource constraints of sensor nodes. Results: Finally, a real world target tracking experiment had been illustrated for static and mobile targets. Here blind child is considered as the target to be tracked within the sensor network. Conclusion: Mathematical analysis and the real world results showed that the target tracking system based on a distributed WSN make efficient use of the communication resources and achieve accurate target tracking successfully.

An Energy-Efficient Target Tracking Framework in Wireless Sensor Networks

This study devises and evaluates an energy-efficient distributed collaborative signal and information processing framework for acoustic target tracking in wireless sensor networks. The distributed processing algorithm is based on mobile agent computing paradigm and sequential Bayesian estimation. At each time step, the short detection reports of cluster members will be collected by cluster head, and a sensor node with the highest signal-to-noise ratio (SNR) is chosen there as reference node for time difference of arrive (TDOA) calculation. During the mobile agent migration, the target state belief is transmitted among nodes and updated using the TDOA measurement of these fusion nodes one by one. The computing and processing burden is evenly distributed in the sensor network. To decrease the wireless communications, we propose to represent the belief by parameterized methods such as Gaussian approximation or Gaussian mixture model approximation. Furthermore, we present an attraction force function to handle the mobile agent migration planning problem, which is a combination of the node residual energy, useful information, and communication cost. Simulation examples demonstrate the estimation effectiveness and energy efficiency of the proposed distributed collaborative target tracking framework.

Experiments With Sensor Motes and Java-DSP

Distributed wireless sensor networks (WSNs) are being proposed for various applications including defense, security, and smart stages. The introduction of hardware wireless sensors in a signal processing education setting can serve as a paradigm for data acquisition, collaborative signal processing, or simply as a platform for obtaining, processing, and analyzing real-life real-time data. In this paper, a software interface that enables the Java-digital signal processing (J-DSP) visual programming environment to communicate in a two-way manner with a wireless sensor network is presented. This interface was developed by writing nesC (an extension to the C programming language for sensors) code that enables J-DSP to issue commands to multiple wireless sensor motes, activate specific transducers, and analyze data using any of the existing J-DSP signal processing functions in real time. A series of exercises were developed and disseminated to provide hardware experiences to signals and systems and signal processing undergraduate students. The hardware with the J-DSP software has been used for two semesters in the senior level digital signal processing (DSP) course at Arizona State University. The interface, the exercises, and their assessment (instruments and results) are described in the paper.

Energy-Efficient Information-Driven Coverage for Physical Movement Monitoring in Body Sensor Networks

Advances in technology have led to the development of various light-weight sensor devices that can be woven into the physical environment of our daily lives. Such systems enable on-body and mobile health-care monitoring. Our interest particularly lies in the area of movement-monitoring platforms that operate with inertial sensors. In this paper, we introduce the notion of compatibility graphs and describe how they can be utilized for power optimization. We first formulate an action coverage problem that will consider the sensing coverage from a collaborative signal processing perspective. Our solution is capable of eliminating redundant sensor nodes while maintaining the quality of service. The problem we outline can be transformed into an NP-hard problem. Therefore, we propose an ILP formulation to attain a lower bound on the solution and a fast greedy technique. Moreover, we present a system for dynamically activating and deactivating sensor nodes in real time. We then use our graph representation to develop an efficient formulation for maximum lifetime. This formulation provides sufficient information for finding activation duties for each sensor node. Finally, we demonstrate the effectiveness of our techniques on data collected from several subjects.

Virtual field strategy for collaborative signal and information processing in wireless heterogeneous sensor networks

A novel collaborative signal and information processing (CSIP) method, which is based on virtual fields excited by sensor nodes, is proposed for wireless heterogeneous sensor networks. These virtual fields influence states and operations in sensor nodes located in their regions of influence (ROIs) and thus collaboration is implemented through interactions between surrounding virtual fields and sensor nodes. Described by a group of radial basis functions (RBFs), virtual fields have different magnitudes and ROIs due to different initial energy, communication ranges, sensing ranges and information processing capabilities in heterogeneous sensor nodes. Dynamic mobile agent itinerary decision and adaptive node active probability updating are studied with virtual field strategies in a heterogeneous sensor network using mobile-agent-based computing paradigm. Simulation results demonstrate that this approach can reduce energy consumption in sensor nodes. Information gain efficiency and network lifetime are also increased.

Underwater acoustic sensor networks: Target size detection and performance analysis

Underwater acoustic sensor network consists of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area. Scalability concern suggests a hierarchical organization of underwater sensor networks with the lowest level in the hierarchy being a cluster. In this paper, we show that an ultra-wide band (UWB) channel can be used for underwater channel modeling and propose a maximum-likelihood (ML) estimation algorithm for underwater target size detection using collaborative signal processing within a cluster in underwater acoustic sensor networks. Theoretical analysis demonstrates that our underwater sensor network can tremendously reduce the variance of target size estimation. We show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer–Rao lower bound. Simulations further validate these theoretical results.

Secure Time Synchronization in Sensor Networks

Time synchronization is critical in sensor networks at many layers of their design. It enables better duty-cycling of the radio, accurate and secure localization, beamforming, and other collaborative signal processing tasks. These benefits make time-synchronization protocols a prime target of malicious adversaries who want to disrupt the normal operation of a sensor network. In this article, we analyze attacks on existing time synchronization protocols for wireless sensor networks and we propose a secure time synchronization toolbox to counter these attacks. This toolbox includes protocols for secure pairwise and group synchronization of nodes that either lie in the neighborhood of each other or are separated by multiple hops. We provide an in-depth analysis of the security and the energy overhead of the proposed protocols. The efficiency of these protocols has been tested through an experimental study on Mica2 motes.

Applications and design issues for mobile agents in wireless sensor networks

Recently, research interest has increased in the design, development, and deployment of mobile agent systems for high-level inference and surveillance in a wireless sensor network (WSN). Mobile agent systems employ migrating codes to facilitate flexible application re-tasking, local processing, and collaborative signal and information processing. This provides extra flexibility, as well as new capabilities to WSNs in contrast to the conventional WSN operations based on the client-server computing model. In this article we survey the potential applications of mobile agents in WSNs and discuss the key design issues for such applications. We decompose the agent design functionality into four components, that is, architecture, itinerary planning, middleware system design, and agent cooperation. This taxonomy covers low-level to high-level design issues and facilitates the creation of a component-based and efficient mobile agent system for a wide range of applications. With a different realization for each design component, it is expected that flexible trade-offs (e.g., between energy and delay) can be achieved according to specific application requirements.