Compressed Sensing of Audio Signals in a Wireless Sensor Network

Compressed sensing is an attractive compression scheme due to its universality and lack of complexity on the sensor side. In this paper we present a study on compressed sensing of real, non-sparse, audio signals. We investigate the performance of different bases and reconstruction algorithms. We then explore the performance of multi-sensor compressed sensing of audio signals and present a novel scheme to provide improved performance over standard reconstruction algorithms. We then present simulations and measured results of a new algorithm to perform efficient detection and estimation in a sensor network that is used to track the location of a subject wearing a tracking device, which periodically transmits a very sparse audio signal. We show that our algorithm can dramatically reduce the number of transmissions in such a sensor network.

Signal Processing, 2015

A microelectronic system for monitoring areas of environmental interest through the automated creation of soundmaps, based on data from a wireless acoustic sensor network (WASN) has been recently proposed. In this context, it has been demonstrated that compression algorithms need to be employed at sensor node level due to the increasing demand in bandwidth as the number of sensors and events to be logged increases. Motivated by this finding, the effect of data compression on signal complexity is studied in this paper by employing four widely used audio compression algorithms in combination to different entropic / information measures. Several entropic / information measures are calculated for both compressed data streams and the original audio, leading to a comparison on the effect of the compression on the complexity characteristics of WASN signals. Numerical results imply that in a realistic WASN for environmental monitoring it is possible to locally compress audio data at node level prior to network transmission while maintaining the complexity characteristics of the sound signal in terms of preserving the precision of specific entropic / information metrics. However, this is not possible for all the studied "complexity metric-compression algorithm" combinations.

International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, 2014

A Wireless Sensor Network (WSN) consists of several sensor nodes deployed in inaccessible areas for monitoring temperature, pressure, vibration, sound, motion etc. A WSN is used for variety of applications such as military, civil, industrial automation, medical, home automation, fleet monitoring, habitat monitoring, preventing theft etc. The availability of inexpensive hardware such as CMOS cameras and microphones has led to the development of Wireless Multimedia Sensor Networks (WMSN) which is used for image and video applications. In case of video applications the captured data will be too large if transmitted as such so it has to be compressed before transmission. Compression in traditional video encoding makes use of motion estimation and motion compensation techniques which requires intensive operations that lead to significant energy consumption and also the storage required is high. This drawback can be addressed by Compressed sensing, an emerging technique that directly obtains the desired samples, thereby reducing the energy consumption, storage capacity and bandwidth used in the network. It is used for reconstructing a signal from the M<<N measurements obtained from sparse or compressible signals, where N is the number of samples required for Nyquist sampling. Compressed sensing can overcome the drawbacks of traditional video encoders by simultaneously sensing and compressing the data at low complexity. The original signal can be recovered from measurements using basis pursuit and greedy algorithms. The objective of this paper is to implement a video compressed sensing framework using Gaussian measurement matrix and reconstruct it using Orthogonal Matching Pursuit algorithm and further transmission energy is analysed for the video compressed sensing framework.

ACM Transactions on Sensor Networks, 2013

Wireless sensor networks (WSNs) are highly resource constrained in terms of power supply, memory capacity, communication bandwidth, and processor performance. Compression of sampling, sensor data, and communications can significantly improve the efficiency of utilization of three of these resources, namely, power supply, memory and bandwidth. Recently, there have been a large number of proposals describing compression algorithms for WSNs. These proposals are diverse and involve different compression approaches. It is high time that these individual efforts are put into perspective and a more holistic view taken. In this article, we take a step in that direction by presenting a survey of the literature in the area of compression and compression frameworks in WSNs. A comparative study of the various approaches is also provided. In addition, open research issues, challenges and future research directions are highlighted.

International Journal of Computer Applications, 2021

Over the last two decades, the Wireless Multimedia Sensors Networks (WMSN) technology have become increasingly popular by both actual industrial users and research community, they are used for recording speech and then sending it to a base station. However, their limited amount of resources (power, low capacity of radio waves, bandwidth, memory, processing, storage, etc.) makes it important to save resources in order to extend the life of the sensor as long as possible. This paper aims to propose and evaluate an adaptive lifting wavelet encoding hardware solution for audio data compression in WMSN, with require low memory, low computation and low energy consumption. The simulation results show that the proposed approach is efficient and satisfactory compared to the Discrete Cosine Transform (DCT) approach, since it allows 32.6% storage savings and 47.84% energy savings were achieved.

2009 Information Theory and Applications Workshop, 2009

Compressive Sensing (CS) shows high promise for fully distributed compression in wireless sensor networks (WSNs). In theory, CS allows the approximation of the readings from a sensor field with excellent accuracy, while collecting only a small fraction of them at a data gathering point. However, the conditions under which CS performs well are not necessarily met in practice. CS requires a suitable transformation that makes the signal sparse in its domain. Also, the transformation of the data given by the routing protocol and network topology and the sparse representation of the signal have to be incoherent, which is not straightforward to achieve in real networks. In this work we address the data gathering problem in WSNs, where routing is used in conjunction with CS to transport random projections of the data. We analyze synthetic and real data sets and compare the results against those of random sampling. In doing so, we consider a number of popular transformations and we find that, with real data sets, none of them are able to sparsify the data while being at the same time incoherent with respect to the routing matrix. The obtained performance is thus not as good as expected and finding a suitable transformation with good sparsification and incoherence properties remains an open problem for data gathering in static WSNs.

IEEE Signal Processing Magazine, 2000

A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advanced signal processing tasks with unprecedented robustness and versatility, thus making WSN an attractive low-cost technology for a wide range of remote sensing and environmental monitoring applications [1], .

2016

The thesis focuses on collecting data from wireless sensors which are deployed randomly in a region. These sensors are widely used in applications ranging from tracking to the monitoring of environment, traffic and health among others. These energy constrained sensors, once deployed may receive little or no maintenance. Hence gathering data in the most energy efficient manner becomes critical for the longevity of wireless sensor networks (WSNs). Recently, Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in WSN; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the forwarding load across the network. This is particularly true due to the benefits obtained from in-network data compression. With CDG, the central unit, instead of receiving data from all sensors in the network, it may receive very few compre...

dei.unipd.it

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples (sub-sampling) at the Data Collection Point (DCP). To this end, we propose a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is very general as it does not require ad-hoc parameter tuning by the user and is able to self-adapt to unpredictable changes in the ...

The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric 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 on a sparse basis that provides a promising approach to compress 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. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.