Compression In Wireless Sensor Networks Research Articles

Lightweight, latency‐aware routing for data compression in wireless sensor networks with heterogeneous traffics

AbstractIn many applications and scenarios, sensors have to regularly report what they monitor from the environment and quickly notify the sink node of event occurrence in the sensing field. An in‐network data reduction technique, such as data aggregation and data compression, can help diminish the amount of data sent from sensors, which not only saves the network bandwidth but also preserves sensors' energy. However, such technique does not consider packet latency because of the aggregation or compression operation. When some sensors generate regular reports in lower data rates, their packets have to spend longer time to be aggregated or compressed, resulting in higher packet delays. Besides, when events occur, the network could suffer from instant congestion due to the generation of numerous event notifications. Motivated with the aforementioned observations, the paper develops a lightweight, latency‐aware routing for data compression (L2DC) scheme to reduce packet latency when applying the compression technique, to reduce the amount of data generated from sensors. L2DC gives event notifications a higher priority over regular reports and eliminates unnecessary notifications to avoid bursty network congestion. In addition, L2DC facilitates the data compression process by allowing each sensor to determine whether to keep packets for compression locally or to send them to a neighbor to be compressed in a distributed manner. Our L2DC scheme can be easily built on most ad hoc and sensor routing protocols because it provides auxiliary redundant packet elimination and relay node selection mechanisms to reduce packet latency. By using the ad hoc on‐demand distance vector protocol as the example, simulation results demonstrate the effectiveness of the L2DC scheme. Copyright © 2015 John Wiley & Sons, Ltd.

Data Preprocessing Algorithm for Better Haar-Based Data Compression in Wireless Sensor Networks

Data Preprocessing Algorithm for Better Haar-Based Data Compression in Wireless Sensor Networks

Compressive Sensing Optimization for Signal Ensembles in WSNs

Compressive sensing (CS) is a new approach to simultaneous sensing and compressing that is highly promising for fully distributed compression in wireless sensor networks (WSNs). While a wide investigation has been performed about theory and practice of CS for individual signals, real and practical cases, in general, involve multiple signals, extending the problem of compression from 1-D single-sensor to 2-D multiple-sensors data. In this paper the two most prominent frameworks on sparsity and compressibility of multidimensional signals and signal ensembles, Distributed compressed sensing (DCS) and Kronecker compressive sensing (KCS), are investigated. In this paper we compare these two frameworks against a common set of artificial signals properly built to embody the main characteristics of natural signals. We further investigate how, in a real deployment, DCS can be used to reduce the power consumption and to prolong lifetime. In particular an extensive analysis is performed using real commercial off-the-shelf (COTS) hardware evaluating how different kind of compression matrices can affect the jointly reconstruction, trying to achieve the better tradeoff between quality and energy expenditure.

Data-Smoothness based Preprocessing Strategy for Wavelet Data Processing in Wireless Sensor Networks

Wavelet based data compression in wireless sensor networks can reduce in-network data transmissions and gain better data approximation. To improve the performance of algorithms based on wavelet data compression, data-smoothness based preprocessing strategy for wavelet data processing is proposed. The strategy can adjust the order of data to be processed for better smoothness through sample mean and control the frequency of data order adjustment by a threshold, achieving better data reconstruction precision with acceptable network control overhead, and higher data compression degree under a given requirement of data reconstruction precision. Theoretical analysis and experiments prove the effectiveness of the strategy.

Lightweight Data Compression in Wireless Sensor Networks Using Huffman Coding

This paper presents a lightweight data compression method for wireless sensor networks monitoring environmental parameters with low resolution sensors. Instead of attempting to devise novel ad hoc algorithms, we show that, given general knowledge of the parameters that must be monitored, it is possible to efficiently employ conventional Huffman coding to represent the same parameter when measured at different locations and time periods. When the data collected by the sensor nodes consists of integer measurements, the Huffman dictionary computed using statistics inferred from public datasets often approaches the entropy of the data. Results using temperature and relative humidity measurements show that even when the proposed method does not approach the theoretical limit, it outperforms popular compression mechanisms designed specifically for wireless sensor networks.

Compression in wireless sensor networks

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.

Strategies and Techniques for Data 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

Hardware compression scheme based on low complexity arithmetic encoding for low power image transmission over WSNs

Software implementation costs of most algorithms, designed for image compression in wireless sensor networks, do not justify their use to reduce the energy consumption and delay transmission of images. Even though the hardware solution looks to be very attractive for this problem, a specific care should be paid when designing a low power algorithm for image compression and transmission over these systems. The aim of this paper is to present and evaluate a hardware implementation for user-driven image compression scheme designed to respect the energy constraints of image transmission over wireless sensor networks (WSNs). The proposed encoder will be considered as a co-processor for tasks related with image compression and data packetization. In this paper, we discuss both of the hardware architecture and the features of this encoder circuit when prototyped on FPGA (field-programmable gate array) and ASIC (application-specific integrated circuit) circuits.

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

Demand for low-power sensing devices with integrated image processing capabilities is increasing, especially for resource-constrained systems such as WSNs. CMOS technology enables the integration of image sensing and image processing, which improves the overall system performance. The aim of this paper is to present and evaluate a smart image sensor integrating a user-driven video compression scheme designed to respect the energy constraints of image processing and transmission over WSNs. The interest of our solution is twofold. First, compression settings can be changed at run time depending on video characteristics. Second, compression is applied only on blocks that change significantly over time. This paper presents in details the internal hardware architecture of the proposed system and describes its performance, with relevant comparisons to some related works.

Cluster head extraction for data compression in wireless sensor networks

Douglas-Peucker(DP) compression algorithm of vector data compression algorithm was introduced to wireless sensor networks,at the same time for the number of scans of the data compression process,the paper put forward an improved cluster head extraction for data compression algorithm,and the cluster head was called data cluster head.Cluster head extraction compression algorithm reduced the number of data scan in compression process by setting step,and used the optimum curve fitting method for monitoring data point to do linear optimization fitting,according to the attachment relationship of the data,and extracted the cluster head data that reflected the overall characteristics;meanwhile,the subgroups of non-cluster head data subgroups were divided.The simulation results show that,the process of cluster head extraction compression algorithm is simpler;for the large fluctuation data it has a better cluster head extraction effect;besides,it reduces the amount of network data transmission,and effectively saves the energy consumption across the network.

Guest editorial: special issue on mobile data management

The term Mobile Data Management has been used in a broad sense to include various aspects on the intersection of mobility and data management. The new powerful smart phones, the abundance of large rich mobile data and the coming of age of the cloud computing paradigm is opening up new avenues for research and exploration that impact larger and larger communities of humans. In this new era, the problems of seamless execution over the new computing infrastructure that encompasses smart phones, mobile sensors and cell phones, of scalable data management, of access to services, of intelligent and autonomic management of architectures, systems, algorithms and security threats, are becoming of paramount importance. In this special issue, we have made an open call for papers for research contributions related to mobile data management. In addition, we have invited the top-three papers from the proceeding of the Mobile Data Management conference, 2011, to submit an extended version to this special issue. As a result, we are pleased to present this special that includes eight excellent papers covering a wide range of topics from searching in smart phones contents to data compression in wireless sensor networks, transactions on mobile ad-hoc networks, trajectory clustering, k-nearest-neighbor queries, elastic cloud environment, data acquisition in smart phones, and security in a smart phone environment.

A new data aggregation scheme via adaptive compression for wireless sensor networks

Data aggregation is necessary for extending the network lifetime of wireless sensor nodes with limited processing and power capabilities, since energy expended in transmitting a single data bit would be at least several orders of magnitude higher when compared to that needed for a 32-bit computation. Therefore, in this article, a novel nonlinear adaptive pulse coded modulation-based compression (NADPCMC) scheme is proposed for data aggregation in a wireless sensor network (WSN). The NADPCMC comprises of two estimators—one at the source or transmitter and the second one at the destination node. The estimator at the source node approximates the data value for each sample. The difference between the data sample and its estimate is quantized and transmitted to the next hop node instead of the actual data sample, thus reducing the amount of data transmission and rending energy savings. A similar estimator at the next hop node or base station reconstructs the original data. It is demonstrated that repeated application of the NADPCMC scheme along the route in a WSN results in data aggregation. Satisfactory performance of the proposed scheme in terms of distortion, compression ratio, and energy efficiency and in the presence of estimation and quantization errors for data aggregation is demonstrated using the Lyapunov approach. Then the performance of the proposed scheme is contrasted with the available compression schemes in an NS-2 environment through several benchmarking datasets. Simulation and hardware results demonstrate that almost 50% energy savings with low distortion levels below 5% and low overhead are observed when compared to no compression. Iteratively applying the proposed compression scheme at the cluster head nodes along the routes over the network yields an additional improvement of 20% in energy savings per aggregation with an overall distortion below 8%.

Image Compression in Wireless sensor networks- A survey

Wireless Sensor networks are battery powered due to which their lifetime is precisely limited. In this type of network the nodes commonly have very limited resources in terms of processing power, bandwidth and energy. Efficient coding of the multimedia content is therefore important. One possible way of achieve maximum utilization of those resource is applying compression on sensor event Usually, processing data consumes much less power than transmitting data in wireless medium, so it is effective to apply compression before transmitting data for reducing total power consumption by a sensor node. In this paper various energy efficient image compression techniques Collaborative image transmission using Sobal edge-detection, JPEG2000 image compression, Image Subtraction with Quantization of image; Adaptive Compression and Spatial Correlation-Based Image Compression are discussed.

Image Compression in Wireless sensor networks- A survey

Image Compression in Wireless sensor networks- A survey

Signal compression in wireless sensor networks

Signal compression is an important tool for reducing communication costs and increasing the lifetime of wireless sensor network deployments. In this paper, we overview and classify an array of proposed compression methods, with an emphasis on illustrating the differences between the various approaches.

CCS-MAC: Exploiting the overheard data for compression in wireless sensor networks

Both the overhearing and overhearing avoidance in a densely distributed sensor network may inevitably incur considerable power consumption. In this paper we propose a so-called CCS-MAC (collaborative compression strategy-based MAC) MAC protocol which facilitates to exploit those overheard data that is treated useless in traditional MAC protocols for the purpose of cost and energy savings. Particularly the CCS-MAC enables different sensor nodes to perform data compression cooperatively with regard to those overheard data, so that the redundancy of data prepared for the link layer transmission can be totally eliminated at the earliest. The problem of collaborative compression is analyzed and discussed along with a corresponding linear programming model formulated. Based on it a heuristic node-selection algorithm with a time complexity of ( O( N 2)) is proposed to the solve the linear programming problem. The node-selection algorithm is implemented in CCS-MAC at each sensor node in a distributed manner. The experiment results verify that the proposed CCS-MAC scheme can achieve a significant energy savings so as to prolong the lifetime of the sensor networks so far.

Practical data compression in wireless sensor networks: A survey

Power consumption is a critical problem affecting the lifetime of wireless sensor networks. A number of techniques have been proposed to solve this issue, such as energy-efficient medium access control or routing protocols. Among those proposed techniques, the data compression scheme is one that can be used to reduce transmitted data over wireless channels. This technique leads to a reduction in the required inter-node communication, which is the main power consumer in wireless sensor networks. In this article, a comprehensive review of existing data compression approaches in wireless sensor networks is provided. First, suitable sets of criteria are defined to classify existing techniques as well as to determine what practical data compression in wireless sensor networks should be. Next, the details of each classified compression category are described. Finally, their performance, open issues, limitations and suitable applications are analyzed and compared based on the criteria of practical data compression in wireless sensor networks.

Combining Encryption and Compression in Wireless Sensor Networks

More and more, wireless sensor networks (WSNs) are making their way out of the laboratory and into real-world deployments where they must be concerned with issues of security. These networks of motes are also being tasked with sensing and monitoring duties that often require high-fidelity data to be delivered from individual nodes, a situation which precludes the use of data aggregation and other techniques to reduce the volume of sensing data by condensing it within the network. This paper makes a case for why continued research in the area of joint compression and encryption is relevant to the field of wireless sensor networks and presents an approach based on the LZW algorithm combined with cryptographically strong pseudo-random number generators.

Improved Adaptive Compression Arbitration System for Wireless Sensor Networks

Previous studies have indicated that data compression in wireless sensor networks is not always beneficial to energy conservation due to the additional computational energy costs. This work gives an energy-efficient arbitration mechanism that enhances the performance of compression algorithms by avoiding unnecessary energy losses. The adaptive compression arbitration system uses a new prediction modeling and adaptation. Tests show that the modeling method gives better predictions with the adaptive mechanism needing less data for modeling, which allows on-line prediction modeling instead of an off-line training process. Moreover, adaptive adjustments of the model parameters enable the system to keep good arbitration precision even as the data characteristics or applications change.

Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity.