Compression In Wireless Sensor Networks Research Articles

Autoencoder-based image compression for wireless sensor networks

This work presents an image compression technique designed for wireless sensor networks (WSNs), leveraging autoencoders and incorporating an error-bound mechanism. The algorithm strategically reduces redundancies in image data, conserving energy by exploiting spatial and temporal correlations within sampled image data through autoencoder features. Precise control over the distortion level in reconstructed images is achieved via the error-bound mechanism, establishing equilibrium between compression rate and reconstruction error. Evaluation results demonstrate comparable image reconstruction fidelity to existing methods (JPEG, JPEG2000, HDPhoto, and an existing Rate-Distortion Balanced approach). The proposed algorithm achieves superior image reconstruction quality at compression ratio rates exceeding 70%, emphasizing a fundamental approach prioritizing heightened reconstructed image quality while balancing compression ratio, distortion, and energy efficiency. Notably, a substantial 50% reduction in overall energy consumption is realized at a compression rate of 38.6%.

Image Compression for Wireless Sensor Network: A Model Segmentation-Based Compressive Autoencoder

Aiming at the problems of image quality, compression performance, and transmission efficiency of image compression in wireless sensor networks (WSN), a model segmentation-based compressive autoencoder (MS-CAE) is proposed. In the proposed algorithm, we first divide each image in the dataset into pixel blocks and design a novel deep image compression network with a compressive autoencoder to form a compressed feature map by encoding pixel blocks. Then, the reconstructed image is obtained by using the quantized coefficients of the quantizer and splicing the decoded feature maps in order. Finally, the deep network model is segmented into two parts: the encoding network and the decoding network. The weight parameters of the encoding network are deployed to the edge device for the compressed image in the sensor network. For high-quality reconstructed images, the weight parameters of the decoding network are deployed to the cloud system. Experimental results demonstrate that the proposed MS-CAE obtains a high signal-to-noise ratio (PSNR) for the details of the image, and the compression ratio at the same bit per pixel (bpp) is significantly higher than that of the compared image compression algorithms. It also indicates that the MS-CAE not only greatly relieves the pressure of the hardware system in sensor network but also effectively improves image transmission efficiency and solves the deployment problem of image monitoring in remote and energy-poor areas.

Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison

There is continuous intensive research on image compression techniques in wireless sensor networks (WSNs) in the literature. Some of the image compression techniques in WSNs that exist in the literature include discrete cosine transform (DCT), discrete waveform transforms (DWT), set partitioning in a hierarchical tree (SPIHT), and embedded zero tree wavelet (EZW) coding. Research on image compression in WSNs is necessitated by the need to improve the energy efficiency of sensor nodes and WSNs&#x2019; lifetimes without compromising the quality of the reconstructed data. Several approaches have been developed centered around image compression and other factors in trying to limit the energy consumption of sensor nodes. Most of these approaches do not provide the error-bound mechanism that balances the rate of compression and distortion of the reconstructed image. Therefore, in this paper, a review and analysis of image compression techniques and approaches in WSNs are conducted. Available image compression approaches in WSNs in literature were then classified according to the image compression technique adopted, and their strengths and weaknesses were highlighted. In addition, a rate-distortion balanced data compression algorithm with error bound mechanism based on artificial neural networks (ANN) in the form of an autoencoder (AE) was coded and simulated in MATLAB before being evaluated and compared to the conventional approaches. The experimental results show that the simulated algorithm has less root mean square error (RMSE) and a higher coefficient of determination (<i>R</i>²) values on variable compression ratios as compared to the Principal Component Analysis (PCA), Discrete Cosine Transform, and Fast Fourier Transform (FFT) when using the Grand-St-Bernard metrological dataset. Furthermore, it presented less RMSE, and higher compression ratio values compared to the Lightweight Temporal Compression (LTC) algorithm on variable error bounds when using the LUCE metrological dataset. Therefore, it was found that the simulated algorithm presents better compression fidelity as compared to the conventional approaches without an error-bound mechanism. Moreover, the algorithm analyzed presents a significant approach to balancing the compression ratio and reconstructed data quality through its error-bound mechanism.

Error-Control Truncated SVD Technique for In-Network Data Compression in Wireless Sensor Networks

In-network data compression plays an important role in the elimination of redundant time-series data in a wireless sensor network (WSN). Inconsistency of data and high computational process in cluster formation remain to be challenging issues of in-network data compression particularly for energy-constraint WSNs. This paper develops a new data clustering technique for in-network data preprocessing and compression called Error-Control Truncated Singular Value Decomposition (ETSVD) to achieve online outlier detection and adaptive data compression. The ETSVD is divided into two modules which are Adaptive Recursive Outlier Detection and Smoothing (ARODS) and Adaptive Data Compression (DC). Firstly, the ARODS pre-processes the collected data for outlier detection and smoothing in order to improve the data quality. Secondly, the DC decomposes the pre-processed data into vector space to compress the spatio-temporal correlated data based on the predefined error threshold at the sending end. After the compression of correlated data, the distinct decomposed data are reconstructed at the receiver end which is performed offline. The simulation results show that the proposed technique is able to compress 91.49% of spatio-temporal environmental temperature data with reconstruction error having a minimum tolerance of $\pm 1.0^\circ $ C. The performance improvement of ETSVD in terms of error and accuracy compared to the performance of conventional SVD are 85.26% and 33.49%, respectively. Moreover, the ETSVD provides efficient error-control data preprocessing and compression solutions within the networks with minimum space and time complexities.

A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding

Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression techniques are generally preferable for sensors in commercial nodes than the lossless ones as they provide a better compression ratio at a lower computational cost. However, the traditional approaches for data compression in WSNs are sensitive to sensor accuracy. They are less efficient when there are abnormal and faulty measurements or missing data. This paper proposes a new lossy compression approach using the Bayesian predictive coding (BPC). Instead of the original signals, predictive coding transmits the error terms which are calculated by subtracting the predicted signals from the actual signals to the receiving node. Its compression performance depends on the accuracy of the adopted prediction technique. BPC combines the Bayesian inference with the predictive coding. Prediction is made by the Bayesian inference instead of regression models as in traditional predictive coding. In this way, it can utilize prior information and provide inferences that are conditional on the data without reliance on asymptotic approximation. Experimental tests show that the BPC is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution. More than that, the BPC is more robust toward occasionally erroneous or missing sensor data. The proposed approach is based on the physical knowledge of the phenomenon in applications. It can be considered as a complementary approach to the existing lossy compression family for WSNs.

A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks

In the recent years, wireless sensor networks (WSN) has been deployed in different real time applications. Energy efficiency is the critical issue in the design and deployment of WSN since the sensor nodes are powered by batteries with limited capacity. As data transmission is the main power consuming process in WSN, several energy efficient techniques have been proposed. Data compression is a popular energy efficient technique which helps to reduce the amount of data to be transmitted in the network resulting in significant power saving. This paper proposes a new algorithm called neighborhood indexing sequence (NIS) for data compression in WSN. The proposed NIS algorithm dynamically assigns shorter length codewords to each character in the input sequence by exploiting the occurrence of neighboring bits. Using the real world WSN dataset, it is shown that the compression performance of the NIS algorithm is superior to existing compression algorithms. Compared with existing methods, the proposed compression algorithm is not only efficient but also highly robust for different WSN dataset. The proposed algorithm attains a compression ratio of 89.13 with the bit rate of 1.74 per sample. Moreover, it achieved power savings up to 87.57% for the applied WSN dataset.

A new scheme for evaluating energy efficiency of data compression in wireless sensor networks

Data communication incurs the highest energy cost in wireless sensor networks, and restricts the application of wireless sensor networks. Data compression is a promising technique that can reduce the amount of data exchanged between nodes and results in energy saving. However, there is a lack of effective methods to evaluate the efficiency of data compression algorithms and to increase nodes’ energy efficiency. The energy saving of nodes is related to both hardware and software, this article proposes a new scheme for evaluating energy efficiency of data compression in wireless sensor networks according to the node’s hardware and software. The relationship between the energy efficiency and the hardware and software factors is expressed by a formula. In this formula, energy efficiency can be improved by increasing the compression ratio and decreasing the ratio of s/ k, in which k represents the node’s hardware factor related to energy consumption of processor, wireless module, and so on and s represents the software factor that reflects the energy consumption of the algorithm. Based on the scheme, a mechanism is proposed to improve the node’s energy efficiency by selecting effective algorithms in accordance with the node’s radio frequency power. The feasibility of the scheme is demonstrated with lossless data compression algorithms on the MSP430F2618 processor.

Cluster‐Based Arithmetic Coding for Data Provenance Compression in Wireless Sensor Networks

In wireless sensor networks (WSNs), data provenance records the data source and the forwarding and the aggregating information of a packet on its way to the base station (BS). To conserve the energy and wireless communication bandwidth, the provenances are compressed at each node along the packet path. To perform the provenances compression in resource‐tightened WSNs, we present a cluster‐based arithmetic coding method which not only has a higher compression rate but also can encode and decode the provenance in an incremental manner; i.e., the provenance can be zoomed in and out like Google Maps. Such a decoding method raises the efficiencies of the provenance decoding and the data trust assessment. Furthermore, the relationship between the clustering size and the provenance size is formally analyzed, and then the optimal clustering size is derived as a mathematical function of the WSN’s size. Both the simulation and the test‐bed experimental results show that our scheme outperforms the known arithmetic coding based provenance compression schemes with respect to the average provenance size, the energy consumption, and the communication bandwidth consumption.

Distributed data aggregation algorithm based on lifting wavelet compression in wireless sensor networks

Compressive sensing (CS) is one of the most promising recoverable data aggregation schemes, which can considerably reduce the amount of data transmitted. However, CS technique brings heavy aggregation burden on sensor nodes, which challenges their restricted available energy and computation capacity. In this paper, we focus on the energy-efficient data compression with the objective of recovering the original dataset. We first propose a dynamic clustering algorithm based on data spatial correlation (CDSC) to balance aggregation load. Furthermore, we propose a faster data compression approach based on eliminable lifting wavelet, which can eliminate spatial and temporal data redundancy. Also, it offers high fidelity recovery for the raw data. Extensive experiments demonstrate that our CDSC algorithm outperforms other methods on prolonging network lifetime and reducing the amount of data transmitted.

Distributed data aggregation algorithm based on lifting wavelet compression in wireless sensor networks

Distributed data aggregation algorithm based on lifting wavelet compression in wireless sensor networks

Increasing network lifetime using data compression in wireless sensor networks with energy harvesting

In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is derived. We then propose a data compression algorithm that determines the compression level at each sensor node to decrease the total energy consumption depending on the average energy level of neighboring sensor nodes while maximizing the lifetime of multihop wireless sensor networks with energy harvesting. Numerical simulations show that the proposed algorithm achieves a reduced average energy consumption while extending the entire network lifetime.

Zeroing for HW-efficient compressed sensing architectures targeting data compression in wireless sensor networks

The design of ultra-low cost wireless body sensor networks for wearable biomedical monitors has been made possible by today technology scaling. In these systems, a typically multi-channel biosignal sensor takes care of the operations of acquisition, data compression and final output transmission or storage. Furthermore, since these sensors are usually battery powered, the achievement of minimal energy operation is a fundamental issue. To this aim, several aspects must be considered, ranging from signal processing to architectural optimization. In this paper we consider the recently proposed rakeness-based compressed sensing (CS) paradigm along with its zeroing companion. With respect to a standard CS base sensor, the first approach allows us to further increase compression rate without sensible signal quality degradation by exploiting localization of input signal energy. The latter paradigm is here formalized and applied to further reduce the energy consumption of the sensing node. The application of both rakeness and zeroing allows for trading off energy from the compression stage to the transmission or storage one. Different cases are taken into account, by considering a realistic model of an ultra-low-power multicore DSP system.

Temporal Lossless and Lossy Compression in Wireless Sensor Networks

Energy efficiency is one of the most critical issues in the design and deployment of Wireless Sensor Networks (WSNs). Data compression is an important approach to reducing energy consumption of data gathering in multihop sensor networks. Existing compression algorithms only apply to either lossless or lossy data compression, but not to both. This article presents a generalized predictive coding framework for unified lossless and lossy data compression. In addition, we devise a novel algorithm for lossless compression to significantly improve data compression performance for various data collections and applications in WSNs. Rigorous simulations show our proposed framework and compression algorithm outperform several recent popular compression algorithms for WSNs such as Lossless Entropy Compression (LEC), S-Lempel-Ziv-Welch (LZW), and Lightweight Temporal Compression (LTC) using various real-world sensor datasets, demonstrating the merit of the proposed framework for unified temporal lossless and lossy data compression in WSNs.

Data Sorting-based Adaptive Spatial Compression in Wireless Sensor Networks

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes.

Rate-Distortion Balanced Data Compression for Wireless Sensor Networks

This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes data congestion and reduces energy consumption by exploring spatio-temporal correlations among data samples. The adaptive rate-distortion feature balances the compressed data size (data rate) with the required error bound guarantee (distortion level). This compression relieves the strain on energy and bandwidth resources while collecting WSN data within tolerable error margins, thereby increasing the scale of WSNs. The algorithm is evaluated using real-world datasets and compared with conventional methods for temporal and spatial data compression. The experimental validation reveals that the proposed algorithm outperforms several existing WSN data compression methods in terms of compression efficiency and signal reconstruction. Moreover, an energy analysis shows that compressing the data can reduce the energy expenditure, and hence expand the service lifespan by several folds.

Cubic B–spline fuzzy transforms for an efficient and secure compression in wireless sensor networks

Joining data compression and encryption is a way to keep secure data, as discussed by the current literature. While data compression responds to the great demand on data storage and transmission techniques, the encryption allows to handle some important parameters in a secure way. In wireless sensor networks the usual transform–based compression is the Discrete Wavelet Transform. In a previous paper we showed the good perfomance of the fuzzy transform (or F–trasform for short) based compression with respect to it. In this work, we propose a cubic B–spline F–transform in order to have a higher accuracy, even when data are not correlated, and a lower computational cost. Besides, in order to show the efficiency of the proposed approach, we compare it with the most recent lossless compression scheme in the field. We discuss these issues formally and numerically by using publicly available real–world data sets. The parameters required to decompress data are encrypted by means of a suitable existing encryption algorithm. We show that even if an illegal user had access to one of these parameters, our scheme would be still secure.

Energy-efficient adaptive data compression in wireless sensor networks

Energy-efficient adaptive data compression in wireless sensor networks

Energy-efficient adaptive data compression in wireless sensor networks

In wireless sensor networks WSNs, a large number of tiny, inexpensive and computable sensor nodes are usually deployed randomly to monitor one or more physical phenomena. The sensor nodes collect and process the sensed data and send the data to the sink wirelessly. Energy consumption is however a serious problem affecting WSNs lifetime. Radio communication is often the major cause of energy consumption in wireless sensor nodes. Thus, applying data compression before transmission can significantly help in reducing the total power consumption of a sensor node. In this paper, we propose an efficient and robust adaptive data compression scheme ADCS. The proposed scheme independently compresses each block of source data losslessly or lossily on local nodes based on the given application. Simulation results show the merits of the proposed compression scheme in comparison with other recently proposed compression algorithms for WSNs including S-LZW, LEC, MPDC, Two-modal GPC and LTC.

An energy‐efficient cooperative MIMO transmission with data compression in wireless sensor networks

In this letter, a novel metric for energy‐efficient cooperative transmission with data compression in wireless sensor networks (WSNs) is proposed. Under the guidance of the new metric, energy consumption is optimized by considering the correlation between data generated by the sensor nodes. The distance between sensor nodes inside the cluster affecting the data correlation as a key factor is analyzed, and the corresponding optimal value is used to reduce the energy consumption. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Adaptive Hybrid Compression for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are often deployed to sample the desired environmental attributes and deliver the acquired samples to a central station, termed as the sink, for processing as needed by the application. Many applications stipulate high granularity and data accuracy that results in high data volumes. However, sensor nodes are battery powered, and sending the requested large amounts of data rapidly depletes their energy. Fortunately, environmental attributes (e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific analysis and simulations tolerate high latency for sensor data collection. Hence, we exploit the spatiotemporal correlation of sensor readings while benefiting from possible data delivery latency tolerance to minimize the amount of data to be transported to the sink. Accordingly, we develop a fully distributed adaptive hybrid compression scheme that exploits both spatial and temporal data redundancies and fuses both temporal and spatial compression for maximal data compression with accuracy guarantees. We present two main contributions: (i) an adaptive modeling technique that allows frugal and maximized temporal compression on resource-constraint sensor nodes by exploiting the data collection latency, and (ii) a novel model-based hierarchical clustering technique that allows for maximized spatial compression resulting into a hybrid compression scheme. Compared to the existing spatiotemporal compression approaches, our approach is fully decentralized and the proposed clustering scheme is based on sensor data models rather than instantaneous sensor data values, which allows merging nearby nodes with similar models into large clusters over a longer period of time rather than specific time instances. The analysis for computation and message overheads, the analysis for theoretical compressibility, and simulations using real-world data demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data.