Data Sorting-based Adaptive Spatial Compression in Wireless Sensor Networks

The major challenge in designing wireless sensor networks (WSNs) is to resolve the limitation of energy and transmission capacity. Data compression, as an effective solution, is attracted many attentions. In this paper, a survey is done on the strategies and techniques for data compression in WSNs. And the data compression schemes are divided into two classes: classical methods and the potential technique based on compressed sensing. The basic procedures of each method are further analyzed, and the existing problems are also highlighted. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3507 Full Text: PDF

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.

We study the problem of compression of videosurveillance sequences collected by a wireless sensor network. In particular, we propose a low-complexity coding framework based on change detection and JPEG-like compression of regions of interest, along with a suitable low-complexity change detection algorithm. We show that on typical videosurveillance sequences the performance of the proposed compression algorithm is similar to that of MPEG-2, at a much less computational cost. Energy profiling results on a TMS320VC5204 board validate the coder design for the proposed application.

Many wireless sensor network datasets suffer from the effects of acquisition noise, channel noise, fading, and fusion of different nodes with huge amounts of data. At the fusion center, where decisions relevant to these data are taken, any deviation from real values could affect the decisions made. We have developed computationally low power, low bandwidth, and low cost filters that will remove the noise and compress the data so that a decision can be made at the node level. This wavelet‐based method is guaranteed to converge to a stationary point for both uncorrelated and correlated sensor data. Presented here is the theoretical background with examples showing the performance and merits of this novel approach compared to other alternatives.

Signal Compression very important aspect in the field of Internet of Things (IoT). The existing compression algorithms present overhead to be implemented on low range devices used in Wireless Sensor Networks (WSNs). To overcome this issue some of the compressed sensing schemes have been developed. Few of the schemes include Basis Pursuit (BP), Orthogonal Least Squares (OLS), and Orthogonal Matching Pursuit (OMP) algorithms. This paper tries to provide a comparison of such algorithms by studying their characteristics and their performance with varying sparsity levels of data. The sparse signals are generated using a sparse-plex library, and the experiments are executed using MATLAB for some of the sparsity levels. The experiments show us that as the sparsity level in the data increases, the recovery error increases, and hence the performance of the schemes degrades.

This paper considers the in-system testing of wireless sensor network (WSN) nodes that operate with strict limits on energy and cost, and are prone to malicious interference. We show that while performing testing using software-based self-test (SBST) programs has clear advantages, node energy efficiency can be increased by compressing self-test programs before distributing them over the wireless interface. We demonstrate a node energy savings by using an adaptive test program compression scheme with a small memory footprint based upon the BSTW algorithm in conjunction with Golomb-Rice coding. As well, we assist in providing security during the testing process by offloading cryptographic functions to hardware for a measure of protection against malicious interference during testing.

High resolution digital medical images are stored in DICOM (Digital Imaging and Communications in Medicine) format that requires high storage space in database. Therefore reducing the image size while maintaining diagnostic quality can increase the memory usage efficiency in PACS. In this study, diagnostic regions of interest (ROI) of pelvis radiographs marked by the radiologist are segmented and adaptively compressed by using image processing algorithms. There are three ROIs marked by red, blue and green in every image. ROI contoured by red is defined as the most significant region in the image and compressed by lossless JPEG algorithm. Blue and green regions have less importance than the red region but still contain diagnostic data compared to the rest of the image. Therefore, these regions are compressed by lossy JPEG algorithm with higher quality factor than rest of the image. Non-contoured region is compressed by low quality factor which does not have any diagnostic information about the patient. Several compression ratios are used to determine sufficient quality and appropriate compression level. Compression ratio (CR), peak signal to noise ratio (PSNR), bits per pixel (BPP) and signal to noise ratio (SNR) values are calculated for objective evaluation of image quality. Experimental results show that original images can approximately be compressed six times without losing any diagnostic data. In pelvis radiographs marking multiple regions of interest and adaptive compression of more than one ROI is a new approach. It is believed that this method will improve database management efficiency of PACS while preserving diagnostic image content.

Contribution to the design of a CMOS image sensor with low-complexity video compression for wireless sensor networks

One of the most successful natural language compression methods is word-based Huffman. However, such a two-pass semi-static compressor is not well suited to many interesting real-time transmission scenarios. A one-pass adaptive variant of Huffman exists, but it is character-oriented and rather complex. In this paper we implement word-based adaptive Huffman compression, showing that it obtains very competitive compression ratios. Then, we show how End-Tagged Dense Code, an alternative to word-based Huffman, can be turned into a faster and much simpler adaptive compression method which obtains almost the same compression ratios.

Characterizing unpredictable patterns in Wireless Sensor Network data

Reliable real-time sensing plays a vital role in ensuring the reliability and safety of industrial cyber-physical systems (CPSs) such as wireless sensor and actuator networks. For many reasons, such as harsh industrial environments, fault-prone sensors, or malicious attacks, sensor readings may be abnormal or faulty. This could lead to serious system performance degradation or even catastrophic failure. Current anomaly detection approaches are either centralized and complicated or restricted due to strict assumptions, which are not suitable for practical large-scale networked industrial sensing systems (NISSs), where sensing devices are connected via digital communications, such as wireless sensor networks or smart grid systems. In this paper, we introduce a fully distributed general anomaly detection (GAD) scheme, which uses graph theory and exploits spatiotemporal correlations of physical processes to carry out real-time anomaly detection for general large-scale NISSs. We formally prove the scalability of our GAD approach and evaluate the performance of GAD for two industrial applications: building structure monitoring and smart grids. Extensive trace-driven simulations validate our theoretical analysis and demonstrate that our approach can significantly outperform state-of-the-art approaches in terms of detection accuracy and efficiency.

Based on digitized TV frames a computer simulation study was conducted on a motion -adaptive TV data compression scheme, defined as one in which a 2- dimensional transformapplied to subblocks is followed by the transmission to the receiving site of only changedsubblocks. Performance was measured both by calculation of Normalized Mean Square Errorand by subjective viewing of reconstructed frames. Two systems designs leading to 512x512resolution operation (one based on the Hadamard and one on the Discrete Cosine Transform)were prepared.IntroductionThe motion -adaptive TV data compression described herein is more effective than conven-tional intraframe techniques because it encompasses them and furthermore takes advantageof the correlation between frames by updating only those subblocks which have changed.Following a 2 -D transform performed on small (e.g. 8x8) subblocks, intraframe compressionis achieved by assigning a small number of bits (or none) to each transform coefficient.This bit allocation is made adaptive by assigning fewer bits to those subblocks within aframe which exhibit less busyness or activity. Next, motion -adaptivity, a form of inter -frame compression, is provided by having the amount of change in coefficients between thecurrent subblock and the corresponding subblock of the previous frame determine which trans-form subblocks to transmit. This process has been simulated and an approach to hardwareimplementation derived.BackgroundTwo -dimensional transforms combined with frame to frame motion -adaptivity achieve highcompression ratios because they exploit pixel correlation in all three dimensions.

Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement

Environmental monitoring is a crucial application of wireless sensor networks (WSNs). This kind of application generally requires a certain geographical granularity of sensing. In addition, some applications simultaneously gathers sensor readings with different attributes. In this paper, we propose a topology construction method that prolongs the lifetime of a WSN which gathers multi-attribute sensor readings. In our proposed method, a minimum number of sensor nodes are selected to acquire sensor readings for each attribute that satisfies the application requirement. For gathering the acquired readings, our method constructs a tree-shaped topology considering the sum of residual energies of nodes and the granularity of sensing for each attribute. By doing so, our method avoids exhausting batteries of nodes which can acquire a specific attribute. We confirmed the effectiveness of our proposed method through simulation experiments.

Wireless sensor networks (WSNs) are vulnerable, that is, they can be physically or remotely exposed to destructive effects. In other words, security is critical in many wireless sensor network applications. Sensor readings are used to locate events such as fires, floods, etc. Because wireless sensor networks are vulnerable, there is a need to protect the network by detecting the introduction of false data. We have developed an algorithm to identify and fix malicious data in the network. The developed optimized swarm intelligence algorithm is applied to different datasets to analyze its performance. A simulator is used to test the algorithm. The analysis and simulation results show the detection and correction of malicious data in wireless sensor networks. In this paper we test simulation of data injection attack in WSN and reach the accuracy of 95% using Matlab.