In-network aggregation techniques for wireless sensor networks: a survey

Fasolo, E.; Rossi, Michele; Widmer, Joerg; Zorzi, Michele

doi:10.1109/mwc.2007.358967

Cited by 635 publications

(378 citation statements)

References 60 publications

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“…Indeed, depending on the aggregation function, original data may not be recovered by the sink, thus information precision can be lost. Data aggregation techniques dedicated to wireless sensor networks are surveyed in detail by Rajagopalan and Varshney in [3] and by Fasolo et al in [83].…”

Section: Data Reductionmentioning

confidence: 99%

Energy efficiency in wireless sensor networks: A top-down survey

Rault¹,

Bouabdallah²,

Challal³

2014

Computer Networks

782

452

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The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the one hand, energyconstrained sensors are expected to run autonomously for long periods. However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile environments. On the other hand, unlike other networks, WSNs are designed for specific applications which range from small-size healthcare surveillance systems to large-scale environmental monitoring. Thus, any WSN deployment has to satisfy a set of requirements that differs from one application to another. In this context, a host of research work has been conducted in order to propose a wide range of solutions to the energysaving problem. This research covers several areas going from physical layer optimization to network layer solutions. Therefore, it is not easy for the WSN designer to select the efficient solutions that should be considered in the design of application-specific WSN architecture.We present a top-down survey of the trade-offs between application requirements and lifetime extension that arise when designing wireless sensor networks. We first identify the main categories of applications and their specific requirements. Then we present a new classification of energy-conservation schemes found in the recent literature, followed by a systematic discussion as to how these schemes conflict with the specific requirements. Finally, we survey the techniques applied in WSNs to achieve trade-off between multiple requirements, such as multi-objective optimisation.

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Section: Data Reductionmentioning

confidence: 99%

Energy efficiency in wireless sensor networks: A top-down survey

Rault¹,

Bouabdallah²,

Challal³

2014

Computer Networks

782

452

View full text Add to dashboard Cite

show abstract

“…Therefore, the energy consumed by the network to transmit one bit and receive it at the next hop (one hop transmission) over the transceiver unit is approximately equal to the energy used by the micro-controller in 125, 945 CPU clock cycles 4 . Using these design components, we formulate the computational complexity (in number of clock cycles) for compressing the input vector using the AE network C AE (L, K) as: Finally, using (13), we find that the energy consumed to transmit the data with compression can be formulated as: …”

Section: Energy Conservation By Data Compressionmentioning

confidence: 99%

“…Data compression enhances the functionality of WSNs in three main ways. Firstly, compression at cluster heads, gateways, or even within sensor nodes is one key ingredient in prolonging network lifetime [3], [4]. Secondly, archiving the sensing raw data over several years requires a tremendous amount of A preliminary version of this paper has appeared in the proceedings of the 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, see [1].…”

Section: Introductionmentioning

confidence: 99%

Rate-Distortion Balanced Data Compression for Wireless Sensor Networks

et al. 2016

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Abstract-This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes data congestion and reduces energy consumption by exploring spatio-temporal correlations among data samples. The adaptive rate-distortion feature balances the compressed data size (data rate) with the required error bound guarantee (distortion level). This compression relieves the strain on energy and bandwidth resources while collecting WSN data within tolerable error margins, thereby increasing the scale of WSNs. The algorithm is evaluated using real-world datasets and compared with conventional methods for temporal and spatial data compression. The experimental validation reveals that the proposed algorithm outperforms several existing WSN data compression methods in terms of compression efficiency and signal reconstruction. Moreover, an energy analysis shows that compressing the data can reduce the energy expenditure, and hence expand the service lifespan by several folds.Index Terms-Lossy data compression, error bound guarantee, compressing neural networks, Internet of things.

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“…During the past years, the research community on sensor networks has mainly focused on the establishment of efficient and scalable networks by enhancing routing [2], addressing [3] and aggregation [4] mechanisms in WSN. However, issues like gathering, intercepting, storing, transforming and indexing of measurements collected from sensor networks have not been well treated, causing the lack of a standard middleware.…”

Section: Related Workmentioning

confidence: 99%

Design and Performance Evaluation of a System for Storing and Visualizing Data from a Sensor Network

Drira¹,

Renault²,

Zeghlache³

2013

JACN

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Abstract-This paper presents architecture design and performance evaluation of a back end system used to store, index, manage and visualize water quality measurements collected by sensor networks deployed in rivers, lakes and coastal regions. The embedded communications systems in each sensor node enable ad hoc network operation to relay the measurements to the back end system in charge of managing, processing and providing data to end users and large communities through web interfaces. The focus is on the back end system architecture description and on the evaluation of its performance.

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In-network aggregation techniques for wireless sensor networks: a survey

Cited by 635 publications

References 60 publications

Energy efficiency in wireless sensor networks: A top-down survey

Energy efficiency in wireless sensor networks: A top-down survey

Rate-Distortion Balanced Data Compression for Wireless Sensor Networks

Design and Performance Evaluation of a System for Storing and Visualizing Data from a Sensor Network

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