Data funneling: routing with aggregation and compression for wireless sensor networks

Petrović, Dragan; Shah, Rahul; Ramchandran, Kannan; Rabaey, Jan M.

doi:10.1109/snpa.2003.1203366

Cited by 182 publications

(94 citation statements)

References 10 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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“…The sources send their compressed output to central unit for joint decoding. In another method called Coding by Ordering [73], every node in a region of interest sends its data to a border node, which is responsible for grouping all packets into a super-packet which is then sent to the sink node. The important property that is extracted here is that border nodes can suppress some packets and sort the remainder (when order is not important), such that the values of the suppressed packets can be automatically inferred.…”

Section: Pr(b|a)mentioning

confidence: 99%

Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

Borges

2016

Open Computer Science

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Internet-of-Things (IoT) is the latest buzzword, havings its origins in the erstwhile Sensor Networks. Sensor Networks produce a large amount of data. According to the needs this data requires to be processed, delivered and accessed. This processed data when made available with the physical device location, user preferences, time constraints; generically called as context-awareness; is widely referred to as the core function for ubiquitous systems. To our best knowledge there is lack of analysis of context information fusion for ubiquitous sensor networks. Adopting appropriate information fusion techniques can help in screening noisy measurements, control data in the network and take necessary inferences that can help in contextual computing. In this paper we try and explore di erent context information fusion techniques by comparing a large number of solutions, their methods, architectures and models. All the surveyed techniques can be adapted to the IoT framework.

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“…The basic idea of the algorithm Coding by Ordering [18] is to drop data at the aggregation node. This can happen if the transmitted data is unique, and the order is irrelevant for the application.…”

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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Data funneling: routing with aggregation and compression for wireless sensor networks

Cited by 182 publications

References 10 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

Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

Gathering Sensor Data in Home Networks with IPFIX

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