Data compression issues arise in a sensor network when designing protocols for efficiently collecting all data observed by the sensor nodes at an internetconnected base station. In this research, we used a combination of huffman encoding and variable length encoding. Pervasive computing 1 data compression techniques in wireless. While some good compression algorithms exist specific to sensor networks. Data compression techniques have extensive applications in powerconstrained digital communication systems, such as in the rapidlydeveloping domain of wireless sensor network applications. In any data compression and transfer framework, the user must be able to derive the desired information from the data received. This book is intended for researchers starting work in the field and for practitioners seeking a comprehensive overview of the various aspects of building a sensor network. The proposed preprocessing techniques consider the characteristics of sensed multimedia data. The innetwork data reduction mechanisms proposed in, combine data compression and routing techniques. The effective use of data fusion in sensor networks is not new. Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications author links open overlay panel mou wu a liansheng tan a naixue xiong b c show more. The individual nodes in a wireless sensor network wsn are inherently. Communication technologies and intelligent applications explores the latest sensor and sensor networks techniques and applications, showing how networked wireless sensors are used to monitor and gather intelligence from our surrounding environment. The data collected by sensors are of a huge amount.
Wsn nodes have less power, computation and communication compared to manet nodes. The sensor nodes in wireless sensor network sense the physical environment and the sensed information is transferred to the destination. Sep 25, 2009 an evaluation of compression techniques for wireless sensor networks abstract. Ieee 8th international conference on intelligent sensors, sensor networks and information processing, 20, pp. Recent advances of data compression in wireless sensor network.
Practical data compression in wireless sensor networks. In this chapter, we discuss the data compression techniques in wsns, which can be classified. The advancement in the wireless technologies and digital integrated circuits led to the development of wireless sensor networks wsn. We show how wireless sensor networks can establish an important identity property of ensuring code integrity through the process of remote attestation. Among those proposed techniques, the data compression scheme is one that can be used to reduce transmitted data over wireless channels. Distributed signal processing techniques for wireless. Data fusion methods data compression wireless sensor. Sep 06, 2012 since wireless multimedia sensor networks wmsns are based on transmission of audio and video data, performing compression on the sensor nodes plays a key role. Fowler in order for wireless sensor networks to exploit signal, signal data must. Cs based compression does not require intensive computation at sensor nodes and complicated transmission control compared to conventional compression techniques. An adaptive lossless data compression scheme for wireless. Hardware components, operating systems, protocols, and algorithms that make up the anatomy of a sensor node are described in chapter two.
Part of the lecture notes in computer science book series lncs, volume 7425. Introduction to wireless sensor networks begins with an introduction to wireless sensor networks and their fundamental concepts. Data compression approaches in iot real world application future aspects. Wireless sensor networks wsns are achieving importance with the passage of time. Pervasive computing 1 data compression techniques in wireless sensor networks youchiun wang abstract wireless sensor networks wsns open a new research. As the number and variety of communication services grow, so do the challenges of designing costeffective networks that meet the requirements of emerging technologies in wireless, sensor, and mesh networks.
Jul 01, 2005 included is coverage of lowcost sensor devices equipped with wireless interfaces, sensor network protocols for large scale sensor networks, data storage and compression techniques, security architectures and mechanisms, and many practical applications that relate to use in environmental, military, medical, industrial and home networks. Jul 20, 2004 designing, implementing, and operating a wireless sensor network involves a wide range of disciplines and many applicationspecific constraints. A few of the compression techniques for wireless sensor networks are, delta encoding. Case studies, based on the authors direct experience of implementing wireless sensor networks, describe the design methodology and the type of measurements used, together with samples of the performance measurements attained. A survey on data compression in wireless sensor networks ieee. These networks are used to monitor physical or environmental conditions like sound, pressure, temperature and cooperatively pass data through the network to a main location as shown in the figure. Wireless sensor networks possess significant limitations in storage, bandwidth, and power. To make sense of and take advantage of these systems, a holistic approach is neededand this is precisely what wireless sensor networks delivers. Mouftah, a data mining approach to energy efficiency in wireless sensor networks, ieee 24thinternational symposium on personal, indoor and mobile radio communications. Pdf data compression techniques in wireless sensor networks.
Research article preprocessing techniques for high. The routingdriven compression rdc fulfills the task of data compression. A survey of network lifetime maximization techniques in. Most existing neural network compression methods focus on improving the compression and reconstruction accuracy i. A wireless sensor network wsn is made up of a set of. A survey on data compression techniques in wireless sensor. Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. Distributed signal processing techniques for wireless sensor networks ringbased optimallevel distributed wavelet transform with arbitrary filter length for wireless sensor networks we propose an optimallevel distributed transform for waveletbased spatiotemporal data compression in wireless sensor networks. The common encoders are huffman encoder, arithmetic encoder and simple runlength encoder. Realtime data compression in wireless sensor networks. In the context of the use of wireless sensor network technology for environmental. A novel data compression using hybrid jpeg in wireless sensor network chihchung lin, chicheng chuang, chienwen chiang, rayi chang department of engineering science, national. Wireless sensor networks wsns are resource constraint. Lossless data compression for wireless sensor networks based on modified bitlevel rle.
Research article preprocessing techniques for highefficiency data compression in wireless multimedia sensor networks junhopark 1 andjaesooyoo 2 agency for defense development. Aug 29, 2014 we have proposed preprocessing techniques for highefficiency data compression in wireless multimedia sensor networks. Many wireless sensor networks today play an important role in collecting big amounts of realtime sensing data over permission to make digital or hard copies of all or part of this work for. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input. Energyaware data compression for wireless sensor networks. Usually, processing data consumes much less power than transmitting data in wireless medium, so it is effective to apply data. Out of massive usage of wireless sensor networks, few applications demand quick data transfer including minimum possible interruption. Energy efficient compression approach for wireless. These techniques have the potential to limit the power supply nodes in order to increase the life time of wsn. To analyze the effects of different kinds of data compression in wireless sensor networks a survey has been conducted and discussed in tabular form.
Data compression and dimensionality reduction in wireless sensor networks wsns refer to the problem of encoding the data collected from sensor nodes using fewer bits. Data prediction, compression, and recovery in clustered. Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks wsns. Ahadul imam, justin chi and mohammad mozumdar, data compression and visualization for wireless sensor networks. Manet wireless sensor networks may be considered a subset of mobile adhoc networks manet. Computer and communication networks is the first book to offer balanced coverage of all these topics using extensive case studies and.
It also helps to cut communication cost and computation cost. Special issue data collection in wireless sensor networks. These networks capture, process and transmit audio and video signals and operate on traffic enforcement, health care and ambient monitoring applications among others. Data compression in sensor network free download as powerpoint presentation. Communication technologies and intelligent applications explores the latest sensor and sensor networks techniques and applications, showing how networked wireless sensors are used to monitor and gather intelligence from our surrounding environment it provides a systematic look at the unique characteristics of wireless sensor networks. Data centric routing and storage in sensor network r. Additionally, realtime sensor networks cannot tolerate high latency. Several applications give importance to throughput and they have not much to do with delay. Compression techniques for wireless sensor networks caimu tang, c. Data compression for inference tasks in wireless sensor networks by mo chen chair. Survey on compressive data collection techniques for. Lossy compression with error bound guarantee we propose to apply the autoencoder to the data com. Therefore, network lifetime nl maximization techniques. Included is coverage of lowcost sensor devices equipped with wireless interfaces, sensor network protocols for large scale sensor networks, data storage and compression techniques, security architectures and mechanisms, and many practical applications that relate to use in environmental, military, medical, industrial and home networks.
Ratedistortion balanced data compression for wireless sensor. In these largescale sensing networks, data compression is required for encoding the data collected from sensors into fewer bits, and hence reducing energy and bandwidth consumption. To do this, we analyzed the characteristics of multimedia data under the environment of wireless multimedia sensor networks. A data compression model with efficient convolutional. Proceedings of the 8th international conference on wireless communications networking and. We study the problem of data gathering in wireless sensor networks and compare several approaches belonging to different research fields. Wsn is a wireless network that consists of base stations and numbers of nodes wireless sensors. It includes the encoding information at data generating nodes and decoding it at sink node. One possible way of achieve maximum utilization of those resource is applying data compression on sensor data. Sensor systems based on fpgas and their applications. A data compression application for wireless sensor networks using. Wireless sensor networks wsns open a new research field for pervasive computing and contextaware monitoring of the physical environments. Wireless sensor and actuator networks will enable you to answer vital questions such as. Data compression is used to reduce the amount of information or data transmitted by source nodes.
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. Routing protocols for wireless sensor networks wsns. Energy conservation is a critical issue in wireless sensor networks since sensor nodes are powered by battery. A comparison of these data compression techniques is also given in this chapter. Ahadul imam, justin chi and mohammad mozumdar, data compression and visualization for wireless sensor networks 2084.
Data gathering techniques for wireless sensor networks. Manets have high degree of mobility, while sensor networks are mostly stationary. A data compression technique for sensor networks with dynamic. Data collection is a crucial function of a sensor node, as it requires more energy from the network. Hence, wireless sensor networks wsns have been considered as one of the big data domains. Wireless sensor networks wsn typically consist of compact, low energy devices capable of sustaining a variety of adhoc topologies and operate for many years from a single, nonrenewable, finite energy source such as a battery. Efficient data compression with error bound guarantee in. Preprocessing techniques for highefficiency data compression. Introduction to wireless sensor networks wiley desktop. Data compression for inf erence t asks in wireless sensor networks by mo chen chair. However, existing compression algorithms are not applicable for sensor nodes because of their limited resource.
A survey on data compression in wireless sensor networks. Aziz, an energy efficient image compression scheme for wireless sensor networks, in. Data compression techniques in wireless sensor networks youchiun wang abstractwireless sensor networks wsns open a new research. This chapter investigates sensor network node identity relative to the functional properties reflected in executing code found on small computing sensors. Thus, any data compression scheme proposed for wsns must be lightweight. Various compression technique for wireless sensor networks compression techniques are predominately used to increase the energy efficiency and the life time of sensors. A novel data compression using hybrid jpeg in wireless. Energy efficient data compression in wireless sensor networks. Wireless sensor networks data compression energy efficiency bit rate. Introduction to wireless sensor networks types and applications. Data compression techniques in wireless sensor networks future.
However, the computational burdens of the intended compression algorithms must be. Manets have high degree of mobility, while sensor networks. Ltc algorithm is used to minimize the amount of error in each reading. As radio communications is the main source of energy consumption, reducing transmission overhead would be extended the sensor node lifetime. Many wsn applications aim at longterm environmental monitoring. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Data compression techniques for wireless sensor network. Wireless sensor networks may be considered a subset of mobile adhoc networks manet. This essential reference begins by providing a solid foundation in tcpip schemes, wireless networking, internet applications, and network. Computer and communication networks is the first book to offer balanced coverage of all these topics using extensive case studies and examples.
Data compression techniques in wireless sensor networks. Reliable transport for sensor networks chiehyih wan, a. The objective of the proposed work is to minimize the total number of bits required to be transmitted from the sensor node to reduce the energy consumed by the sensor node. Data compression in sensor network internet of things. Lastly, data packets are created for transmission over the wireless sensor network. However, this paper focuses only on data compression techniques. Pervasive computing 1 data compression techniques in. However, the computational burdens of the intended compression.
Technique for wireless sensor networks compression techniques are predominately used to increase the energy efficiency and the life time of sensors. Out of massive usage of wireless sensor networks, few applications demand quick data transfer. We then present a new data compression scheme, called espiht, that exploits this spatiotemporal correlation present in sensor networks to reduce the amount of data bits transmitted in a collaborative signal. In order for wireless sensor networks to exploit signal, signal data must be collected at a multitude. An evaluation of compression techniques for wireless sensor. Sensor fusion is also known as multi sensor data fusion and is a subset of information fusion. Gailly, the data compression book, mis press, 1996. Although existing data compression techniques for wireless sensor networks have been surveyed in the literature such as kimura and latifi 2005, the survey was not uptodate and contained algorithms that were not practical in wsns. Fowler in order for wireless sensor networks to exploit signal, signal data must be collected at a multitude of sensors and must be shared among the sensors. This paper explores energy consumption tradeoffs associated with data compression, particularly in the context of lossless compression. Sensor network localization systems and network management techniques are covered in part v.
Wsn consists of various sensor nodes and relays capable of computing, sensing, and communicating wirelessly. Youchiun wang, data compression techniques in wireless sensor networks, pervasive computing. It all rest on the applications desires that which parameter is more favourite. A wireless sensor node is equipped with sensing and computing devices, radio transceivers and power components. Combining compression and clustering techniques to handle big. Compression at cluster heads, gateways, or even within a sensor node with multiple sensing units, is one key ingredient in prolonging network lifetime 2. We have proposed preprocessing techniques for highefficiency data compression in wireless multimedia sensor networks. Ratedistortion balanced data compression for wireless. Data compression technique for wireless sensor networks.
When we return to the literature 2, we can distinguish between several data compression techniques used in particular around wireless sensor networks. The role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things iot, ehealth and industry 4. The final part focuses on target detection and habitat monitoring applications of sensor networks. Therefore, current research has been focused on data compression and data clustering as efficient techniques to reduce big data collection in wsns thus enhancing their lifetime. Sensor networks, data aggregation, data compression, event detection, redundancy elimination. In this paper, we present an adaptive lossless data compression aldc algorithm for wireless sensor networks. A few of the compression techniques for wireless sensor networks. Advancement in wireless communication technologies and lowcost wireless devices for networking has initiated the emergence of a new type of wireless network, called wireless sensor network. The data compression algorithms are needed for these networks to reduce the amount of data communicated to the sink. In this article, we propose and evaluate a new compression approach. Wang, data compression techniques in wireless sensor networks, pervasive comput. Wireless sensor networks wsns can be defined as a selfconfigured and infrastructureless wireless networks to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants and to cooperatively pass their data through the network to a main location or sink where the data can be observed and analysed. Compression techniques for wireless sensor networks.
1118 890 490 821 151 771 1101 149 13 21 830 1495 423 209 701 107 634 890 574 277 61 327 987 224 1041 645 1335 468 750 459 1032 62 985 575 1489 830 446 384 222 1364 716 374 270 1010