Increasing the lifetime of a wireless sensor network through data aggregation techniques

Săcăleanu, Dragoş Ioan; Stoian, Rodica; Perişoară, Lucian Andrei; Lazarescu, V.

doi:10.1109/isscs.2013.6651231

Cited by 5 publications

(8 citation statements)

References 8 publications

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“…The results of our experiments verified the validity of the proposed energy consumption bounds. It was mentioned that data transmission consumes approximate 80% energy of the sensor node (Sacaleanu et al, 2011), therefore, the bounds derived in this paper could be applied as a measurement criterion to evaluate the energy efficiency of data transmission. In order to illustrate the application of the derived bound, we investigated multiple sources issue and proposed a topology combination approach to reduce energy consumption.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to the routing efficiency, data aggregation is helpful to decrease the data volume and, therefore, reduce the transmission energy. However, the energy required for data aggregation is proportional to the compression ratio (Sacaleanu et al, 2011). Alternatively, the lifetime elongation of the sensor network can be achieved by minimizing the energy consumption if a specific packet loss rate is tolerable.…”

Section: Discussionmentioning

confidence: 99%

“…It is vital for sensor nodes to minimize energy consumption during data transmission. And data aggregation is helpful to extend the network lifetime (He et al, 2004;Sacaleanu et al, 2011;Kimura and Latifi 2005). The aggregation of data reduced the total volume of data thanks to the gain in compression.…”

Section: Grid Construction and Forwarding Schemesmentioning

confidence: 99%

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Energy consumption bounds analysis and its applications for grid based wireless sensor networks

Peng

Chen

2013

Journal of Network and Computer Applications

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Energy consumption bounds analysis and its applications for grid based wireless sensor networks

Peng

Chen

2013

Journal of Network and Computer Applications

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“…To prevent CH battery depletion, the CH changes periodically based on energy criteria [31], [32]. When the residual energy of the CH turns low, another CH is elected among the peripheral nodes.…”

Section: A Network Modelmentioning

confidence: 99%

Distributed Joint Source-Channel Coding With Copula-Function-Based Correlation Modeling for Wireless Sensors Measuring Temperature

Deligiannis

Zimos

Ofrim³

et al. 2015

IEEE Sensors J.

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Wireless sensor networks (WSNs) deployed for temperature monitoring in indoor environments call for systems that perform efficient compression and reliable transmission of the measurements. This is known to be a challenging problem in such deployments, as highly-efficient compression mechanisms impose a high computational cost at the encoder. In this paper, we propose a new distributed joint source-channel coding (DJSCC) solution for this problem. Our design allows for efficient compression and error-resilient transmission, with low computational complexity at the sensor. A new Slepian-Wolf code construction, based on non-systematic Raptor codes, is devised that achieves good performance at short code lengths, which are appropriate for temperature monitoring applications. A key contribution of the work is a novel Copula-function-based modeling approach that accurately expresses the correlation amongst the temperature readings from co-located sensors. Experimental results using a WSN deployment reveal that, for lossless compression, the proposed Copula-function-based model leads to a notable encoding rate reduction (of up to 17.56%) compared to the stateof-the-art model in the literature. Using the proposed model, our DJSCC system achieves significant rate savings (up to 41.81%) against a baseline system that performs arithmetic entropy encoding of the measurements. Moreover, under channel losses, the transmission rate reduction against the state-of-the-art model reaches 19.64%, which leads to energy savings between 18.68% to 24.36% with respect to the baseline system.

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“…This type of scheduling approach demands that the nodes cooperate with each other to complete the tasks, and massive factors should be taken into account comprehensively to determine which nodes are redundant, for example, the coverage ratio, nodes’ location information, the number of neighbor nodes, the distance between nodes, etc. [11,12]. In this way, the sharing of state information between nodes will increase communication overhead, but the advantage is that the distribution of active nodes in the target area is more balanced.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy Efficient Topology Control Scheme Based on a Coverage-Preserving and Sleep Scheduling Model for Sensor Networks

Bin-bin

Wei

Wang

et al. 2016

Sensors

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In high-density sensor networks, scheduling some sensor nodes to be in the sleep mode while other sensor nodes remain active for monitoring or forwarding packets is an effective control scheme to conserve energy. In this paper, a Coverage-Preserving Control Scheduling Scheme (CPCSS) based on a cloud model and redundancy degree in sensor networks is proposed. Firstly, the normal cloud model is adopted for calculating the similarity degree between the sensor nodes in terms of their historical data, and then all nodes in each grid of the target area can be classified into several categories. Secondly, the redundancy degree of a node is calculated according to its sensing area being covered by the neighboring sensors. Finally, a centralized approximation algorithm based on the partition of the target area is designed to obtain the approximate minimum set of nodes, which can retain the sufficient coverage of the target region and ensure the connectivity of the network at the same time. The simulation results show that the proposed CPCSS can balance the energy consumption and optimize the coverage performance of the sensor network.

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Increasing the lifetime of a wireless sensor network through data aggregation techniques

Cited by 5 publications

References 8 publications

Energy consumption bounds analysis and its applications for grid based wireless sensor networks

Energy consumption bounds analysis and its applications for grid based wireless sensor networks

Distributed Joint Source-Channel Coding With Copula-Function-Based Correlation Modeling for Wireless Sensors Measuring Temperature

A Novel Energy Efficient Topology Control Scheme Based on a Coverage-Preserving and Sleep Scheduling Model for Sensor Networks

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