Minimizing energy consumptions in wireless sensor networks via two-modal transmission

Abstract-In this paper we propose a new lossless compression algorithm suitable for Internet of Things (IoT). The proposed algorithm, named RAKE, is based only on elementary counting operations and has low memory requirements, and therefore it can be easily implemented in low-cost and low-speed microcontrollers as those used in IoT devices. Despite its simplicity, simulation results show that, in the case of sparse sequences, the proposed algorithm outperforms well-known lossless compression algorithms such as rar, gzip and bzip2. Moreover, in the case of real-world data, RAKE achieves higher compression ratios as even compared to IoT-specific lossless compression algorithms.

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“…outliers) as the original raw data. This approach is commonly known as Two-Modal (TM) transmission [18].…”

Section: Related Workmentioning

confidence: 99%

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

Campobello

Segreto

Zanafi

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…The sensor data accuracy is important for the understanding of the physical environment, as errors may affect research findings [26]. The available processing power and memory size limitations of the on-board computer (OBC) impose restrictions on the use of computationally intensive data processing algorithms, which have to be taken into account.…”

Section: Wsn Data Processing: Overview and Objectivesmentioning

confidence: 99%

Efficient Data-Processing Algorithms for Wireless-Sensor-Networks-Based Planetary Exploration

Zhai

Vladimirova

2016

Journal of Aerospace Information Systems

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Abstract:The SWIPE project (Space Wireless Sensor Networks for Planetary Exploration) aims to design a wireless sensor network (WSN) , which consists of small wireless sensor nodes dropped onto the Moon surface to collect scientific measurements. Data gathered from the sensors will be processed and aggregated for uploading to a lunar orbiter and subsequent transmission to Earth. In this paper efficient data processing/fusion algorithms are proposed, the purpose of which is to integrate the scientific sensor data collected by the WSN, reducing the data volume without sacrificing the data quality to satisfy energy constraints of WSN nodes operating in the extreme Moon environment. The results of an extensive simulation experiment targeted at the SWIPE lunar exploration mission is reported, which quantifies the performance efficiency of the data processing scheme. It is shown that the proposed data processing algorithms can reduce the WSN node energy consumption significantly, decreasing the data transmission energy up to 91%. In addition, it is shown that up to 99% of the accuracy of the original data can be preserved in the final reconstructed data.

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“…In our work we exploit the statistical features of the data, in order to compress the collected measurements either without a coding lexicon, or with a low memory space dictionary, as in the case in [6,12,10]. The work in [10] calculates the optimal number of low order bits to represent the group index in the Golomb-Rice encoding technique.…”

Section: Related Workmentioning

confidence: 99%

Compressed Data Acquisition from Water Tanks

Burdakis

Inglezakis

Deligiannakis

2015

Proceedings of the 1st ACM International Workshop on Cyber-Physical Systems for Smart Water Networks

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The Aquanet project builds a low-cost monitoring system for the water resources in the city of Chania in Greece. This monitoring process is performed by installing sensor nodes at distribution tanks and periodically transmitting measurements regarding the water level to a remote base station. In this paper we present the data management aspects of this network, focusing on how the transmitted data is compressed in order to minimize the energy consumption and how information concerning the water level at the distribution tanks can be used to derive a water management policy in the network.

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Minimizing energy consumptions in wireless sensor networks via two-modal transmission

Cited by 57 publications

References 9 publications

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

RAKE: A simple and efficient lossless compression algorithm for the Internet of Things

Efficient Data-Processing Algorithms for Wireless-Sensor-Networks-Based Planetary Exploration

Compressed Data Acquisition from Water Tanks

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