Data Reduction in Sensor Networks: Performance Evaluation in a Real Environment

Efficient high-throughput (HT) compression algorithms are paramount to meet the stringent constraints of present and upcoming data storage, processing, and transmission systems. In particular, latency, bandwidth and energy requirements are critical for those systems. Most HT codecs are designed to maximize compression speed, and secondarily to minimize compressed lengths. On the other hand, decompression speed is often equally or more critical than compression speed, especially in scenarios where decompression is performed multiple times and/or at critical parts of a system. In this work, an algorithm to design variable-to-fixed (VF) codes is proposed that prioritizes decompression speed. Stationary Markov analysis is employed to generate multiple, jointly optimized codes (denoted code forests). Their average compression efficiency is on par with the state of the art in VF codes, e.g., within 1% of Yamamoto et al.'s algorithm. The proposed code forest structure enables the implementation of highly efficient codecs, with decompression speeds 3.8 times faster than other state-of-the-art HT entropy codecs with equal or better compression ratios for natural data sources. Compared to these HT codecs, the proposed forests yields similar compression efficiency and speeds. INDEX TERMS Data compression, high-throughput entropy coding, variable-to-fixed codes.

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“…. , 2 4 }, each with exactly |T | = 2 8 Included nodes. Note that for |F| = 1, Algorithm 7 is equivalent to Algorithm 4.…”

Section: ) Code Forest Efficiencymentioning

confidence: 99%

High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests

et al. 2020

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“…The scheme consists of two layers, the opportunistic routing with compression and the nonuniform random projection based estimation for reconstruction. The authors in [20] proposed a data aggregation technique called the Prefix-Frequency Filtering (PFF). This approach mainly consists of two aggregation layers, the first one is on the sensor level, and the second one is on the cluster head or the aggregator.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, in the "M " matrix, x 51 1 is Nan it will be set equal to the same value as x 50 1 , and x 50 2 and x 51 2 are set equal to the same value as x 49 2 , and so on. Algorithm 1 -line (17)(18)(19)(20)(21)(22) : Afterward, the linear dependency of each pair of vectors (v i ,v j ) ∈ M is calculated using the Pearson correlation coefficient. The latter is known as the best method of measuring the association between variables of interest because it is based on the method of covariance.…”

Section: The Proposed Approach (Stcsta)mentioning

confidence: 99%

“…Figure 12 shows the maximum memory size needed by the CH in function of the number of nodes belonging to the cluster. The maximum memory size required by the CH is 8 × M ax(N (SR max + 1 2 N + 4) + 1, 6N + 1) bytes, since the values stored in the first part of the Algorithm (1-17), could be cleared once the sensors have been matched (Algorithm 1, line [17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: F Scalability and Limitationsmentioning

confidence: 99%

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A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

et al. 2019

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In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the "nonsampled" parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60% less energy and can handle nonstationary data more effectively.

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“…A main application domain where wireless sensor networks are broadly used is environmental data collection and monitoring, where certain conditions or processes need to be monitored constantly, such as the temperature in a conditioned space or pressure in a process pipeline. In such applications, the common task of a sensor node is monitoring some phenomena, collecting periodically local measures of interest and relaying data toward the sink Makhoul and Harb, 2017;Harb et al, 2017). Periodic data collection offers great amount of collected data and thus enables complex data analysis which may not be possible with query processing.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient data collection in periodic sensor networks using spatio-temporal node correlation

Harb

Makhoul

Jaber

et al. 2019

IJSNET

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In wireless sensor networks (WSNs), the densely deployment and the dynamic phenomenon provide strong correlation between sensor nodes. This correlation is typically spatiotemporal. This paper proposes an efficient data collection technique, based on spatio-temporal correlation between sensor data, aiming to extend the network lifetime in periodic WSN applications. In the first step, our technique proposes a new model based on an adapted version of Euclidean distance which searches, in addition to the spatial correlation, the temporal correlation between neighbouring nodes. Based on this correlation and in a second step, a subset of sensors are selected for collecting and transmitting data based on a sleep/active algorithm. Our proposed technique is validated via experiments on real sensor data readings. Compared to other existing techniques, the results show the effectiveness of our technique in terms of reducing energy consumption and extending network lifetime while maintaining the coverage of the monitored area.

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Data Reduction in Sensor Networks: Performance Evaluation in a Real Environment

Cited by 23 publications

References 14 publications

High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests

High-Throughput Variable-to-Fixed Entropy Codec Using Selective, Stochastic Code Forests

A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

Energy efficient data collection in periodic sensor networks using spatio-temporal node correlation

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