Distributed compression in a dense microsensor network

Pradhan, S. Sandeep; Kusuma, Julius; Ramchandran, Kannan

doi:10.1109/79.985684

Cited by 434 publications

(254 citation statements)

References 23 publications

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“…First, by simply reducing the traffic transported by the network, e.g., through distributed source coding [5], [6], [7] and/or data aggregation/header compression [8], [9]. Second, by making the transport of traffic on a sensor network energy efficient, e.g., through energy-aware routing [10], [11] and/or distributed medium access control [12].…”

Section: Introductionmentioning

confidence: 99%

Minimizing Energy Consumption in Large-Scale Sensor Networks Through Distributed Data Compression and Hierarchical Aggregation

Baek

Veciana

2004

IEEE J. Select. Areas Commun.

155

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Abstract-In this paper we study how to reduce energy consumption in large-scale sensor networks which systematically sample a spatio-temporal field. We begin by formulating a distributed compression problem subject to aggregation (energy) costs to a single sink. We show that the optimal solution is greedy and based on ordering sensors according to their aggregation costs-typically related to proximity-and, perhaps surprisingly, it is independent of the distribution of data sources. Next we consider a simplified hierarchical model for a sensor network including multiple sinks, compressors/aggregation nodes and sensors. Using a reasonable metric for energy cost, we show that the optimal organization of devices is associated with a Johnson-Mehl tessellation induced by their locations. Drawing on techniques from stochastic geometry, we analyze the energy savings that optimal hierarchies provide relative to previously proposed organizations based on proximity, i.e., associated Voronoi tessellations. Our analysis and simulations show that an optimal organization of aggregation/compression can yield 8-28% energy savings depending on the compression ratio.

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Section: Introductionmentioning

confidence: 99%

Minimizing Energy Consumption in Large-Scale Sensor Networks Through Distributed Data Compression and Hierarchical Aggregation

Baek

Veciana

2004

IEEE J. Select. Areas Commun.

155

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“…Energy efficiency for sensor networks has been an intense area of research in recent years [7], [8], [9], [10], [5], [11], [12], [13], [14], [15], [16], [17], [18]. Among these techniques, various data collection approaches have been proposed such as event detection [19], in-network aggregation [5], [6], distributed compression [13], model-based apporaches [17], [8], and data sampling [10], [18].…”

Section: Related Workmentioning

confidence: 99%

“…Among these techniques, various data collection approaches have been proposed such as event detection [19], in-network aggregation [5], [6], distributed compression [13], model-based apporaches [17], [8], and data sampling [10], [18].…”

Section: Related Workmentioning

confidence: 99%

Region Sampling: Continuous Adaptive Sampling on Sensor Networks

Lin

Arai

Gunopulos

et al. 2008

2008 IEEE 24th International Conference on Data Engineering

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Abstract-Satisfying energy constraints while meeting performance requirements is a primary concern when a sensor network is being deployed. Many recent proposed techniques offer error bounding solutions for aggregate approximation but cannot guarantee energy spending. Inversely, our goal is to bound the energy consumption while minimizing the approximation error. In this paper, we propose an online algorithm, Region Sampling, for computing approximate aggregates while satisfying a pre-defined energy budget. Our algorithm is distinguished by segmenting a sensor network into partitions of non-overlapping regions and performing sampling and local aggregation for each region. The sampling energy cost rate and sampling statistics are collected and analyzed to predict the optimal sampling plan. Comprehensive experiments on real-world data sets indicate that our approach is at a minimum of 10% more accurate compared with the previously proposed solutions.

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“…The algorithms Low-Complexity Video Compression [14] and Distributed Compression [19] deal with data compression of visual data. the first algorithm is based on block changing and JPEG data compression.…”

Section: Related Workmentioning

confidence: 99%

Gathering Sensor Data in Home Networks with IPFIX

Kothmayr

Schmitt

Braun

et al. 2010

Lecture Notes in Computer Science

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Abstract. New developments in military, health and home areas call for new approaches for data acquisition in real-time. Such application areas frequently include challenging requirements for collection, processing and analysis of environmental data. Wireless Sensor Networks can collect such environmental data efficiently. Collected sensor node data needs to be transmitted in an efficient way due to limitations of sensor node resources in battery power and available bandwidth. In this paper, we present a method for efficient transmission of sensor measurement data using the IETF standard IPFIX. We show that its template based design is suitable for efficient transmission of senor data with low bandwidth consumption. In this paper, we present the protocol and its implementation in Wireless Sensor Networks (WSNs). Additionally, a header compression scheme is introduced which further reduces communication cost during data transmission.

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Distributed compression in a dense microsensor network

Cited by 434 publications

References 23 publications

Minimizing Energy Consumption in Large-Scale Sensor Networks Through Distributed Data Compression and Hierarchical Aggregation

Minimizing Energy Consumption in Large-Scale Sensor Networks Through Distributed Data Compression and Hierarchical Aggregation

Region Sampling: Continuous Adaptive Sampling on Sensor Networks

Gathering Sensor Data in Home Networks with IPFIX

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