A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks

Chou, J.; Petrović, Dragan; Kannan, Ravindran

doi:10.1109/infcom.2003.1208942

Cited by 232 publications

(169 citation statements)

References 11 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%

“…There has been much related work in this area. In particular we will draw on a substantial body of work studying the scaling and possible implementation of distributed compression mechanisms for sensor networks, e.g., see [5], [6], [7]. Our main contribution to this literature is to explicitly introduce aggregation costs in the distributed compression problem.…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…Jim chou etal [4] have demonstrated a new approach to decrease energy consumption in sensor networks using a distributed adaptive signal processing framework. Here the sensor nodes compress the readings with respect to one another without the requirement of explicit and energy expensive to achieve the compression.…”

Section: Previous Workmentioning

confidence: 99%

An Energy based Data Gathering Approach for Wireless Sensor Networks

Kaleeswari¹,

Baskaran²

2012

IJCA

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In Wireless Sensor Networks (WSNs), sensor nodes are organized in a random manner. These sensor nodes are mainly deployed in physical environment to gather data and deliver the data to the destination wherever requires. Sensor nodes have limited computational capability, limited bandwidth, energy source and memory. The main challenge in data gathering is to maximize the network lifetime and the given energy constraints of sensor nodes. If energy consumption per node can be balanced as well, a near optimal data gathering and routing scheme can be achieved in terms of network lifetime. We have proposed the algorithm called Energy based Data Gathering Approach (EDGA) to make the correct balance between the three factors like residual energy, distance and data gathering ratio. By simulation results we have shown that the EDGA achieves good throughput, high network lifetime, high residual energy while attaining low delay than the existing schemes LEACH and EBRP.

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“…Several of the proposed methods of energy conservation in sensor networks attempt to take advantage of this spatial redundancy through distributed signal processing. For example, Intanagonwiwat et al use wavelet based compression [7], while Chou et al use distributed source coding, based on the work of Slepian and Wolf [17], to reduce the spatio-temporal redundancy of the data to be transmitted to the sink [4]. In essence, both of these approaches increase network lifetime by using compression to reduce the amount and accuracy of the data received by the sink node.…”

Section: Introductionmentioning

confidence: 99%

infer: A Bayesian Inference Approach towards Energy Efficient Data Collection in Dense Sensor Networks

Hartl¹,

Li²

25th IEEE International Conference on Distributed Computing Systems (ICDCS'05)

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A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks

Cited by 232 publications

References 11 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

An Energy based Data Gathering Approach for Wireless Sensor Networks

infer: A Bayesian Inference Approach towards Energy Efficient Data Collection in Dense Sensor Networks

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