Sensor Data Stream on-line Compression with Linearity-based Methods

Väänänen, Olli; Hämäläinen, Timo D.

doi:10.1109/smartcomp50058.2020.00049

Cited by 4 publications

(10 citation statements)

References 13 publications

(22 reference statements)

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“…If the difference between the calculated regression line and the raw value or values is larger than the error bound, then the first two raw values are retained (stored/sent), and a new regression line is calculated when the next two new values are available. The algorithm works if N = 3 or more [1], [2].…”

Section: A Linear Regression Based Temporal Compressionmentioning

confidence: 99%

“…The latency in this version is the N-1 measurement intervals when calculating the regression line. When the line parameters are sent, the receiver knows that the values follow the line with one measurement interval latency until the line-end parameters (ci and τi) are received [2].…”

Section: A Linear Regression Based Temporal Compressionmentioning

confidence: 99%

“…Compression RT-LRbTC was presented in [2] as a modification of M-LRbTC to achieve a predictable and shorter inherent latency. RT-LRbTC uses already available sensor values to calculate a new regression line.…”

Section: B Real-time Linear Regression Based Temporalmentioning

confidence: 99%

“…When the new line parameters with the timestamp are received, it is known that the previous line ended one measurement interval earlier. Thus, the inherent latency of the algorithm was described by the measurement frequency [2].…”

Section: B Real-time Linear Regression Based Temporalmentioning

confidence: 99%

“…The compression efficiency is dependent on the data characteristics, and in most cases, RT-LRbTC has a lower inherent compression ratio (CR) than M-LRbTC [2]. As RT-LRbTC generally has a lower CR, it means that there are more linear regression lines after compression with RT-LRbTC than with M-LRbTC.…”

Section: B Real-time Linear Regression Based Temporalmentioning

confidence: 99%

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Linearity-based Sensor Data Online Compression Methods for Environmental Applications

Väänänen

Hämäläinen

2023

2023 6th Conference on Cloud and Internet of Things (CIoT)

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Environmental monitoring is a typical Internet of Things (IoT) application. Environmental monitoring plays a significant role, for example, in smart farming and smart city applications. Environmental magnitudes are usually measured using wireless sensor nodes, which are often battery-powered, and the number of sensing nodes can be large. One effective method for reducing the energy consumption of a sensor node is to use data compression to reduce the amount of data required for transmission via a wireless connection. Compressing the sensor data means fewer transmission periods, and thus, lower energy consumption. Compression methods should be effective for compressing environmental magnitudes and be computationally light to be suitable for constrained sensor nodes. A compression algorithm should be able to compress an online data stream. In this paper, we review some compression algorithms suitable for environmental monitoring and present two new versions of those algorithms. The algorithms were evaluated, tested, and compared. The main parameters used for the comparisons were compression ratio, root mean square error, and inherent latency. The simulation results obtained using real datasets demonstrate that simple linearity-based compression algorithms are effective and suitable for compressing environmental data. Two new compression algorithm versions proved to be effective for compressing sensor data with reasonable compression quality and predictable inherent latency.

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Section: A Linear Regression Based Temporal Compressionmentioning

confidence: 99%

Section: A Linear Regression Based Temporal Compressionmentioning

confidence: 99%

Section: B Real-time Linear Regression Based Temporalmentioning

confidence: 99%

Section: B Real-time Linear Regression Based Temporalmentioning

confidence: 99%

Section: B Real-time Linear Regression Based Temporalmentioning

confidence: 99%

See 3 more Smart Citations

Linearity-based Sensor Data Online Compression Methods for Environmental Applications

Väänänen

Hämäläinen

2023

2023 6th Conference on Cloud and Internet of Things (CIoT)

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Toward fast theta‐join: A prefiltering and amalgamated partitioning approach

Wang

Fan

et al. 2022

Concurrency and Computation

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As one of the most useful online processing techniques, the theta‐join operation has been utilized by many applications to fully excavate the relationships between data streams in various scenarios. As such, constant research efforts have been put to optimize its performance in the distributed environment, which is typically characterized by reducing the number of Cartesian products as much as possible. In this article, we design and implement a novel fast theta‐join algorithm, called Prefap, by developing two distinct techniques—prefiltering and amalgamated partitioning—based on the state‐of‐the‐art FastThetaJoin algorithm to optimize the efficiency of the theta‐join operation. Firstly, we develop a prefiltering strategy before data streams are partitioned to reduce the amount of data to be involved and benefit a more fine‐grained partitioning. Secondly, to avoid the data streams being partitioned in a coarse‐grained isolated manner and improve the quality of the partition‐level filtering, we introduce an amalgamated partitioning mechanism that can amalgamate the partitioning boundaries of two data streams to assist a fine‐grained partitioning. With the integration of these two techniques into the existing FastThetaJoin algorithm, we design and implement a new framework to achieve a decreased number of Cartesian products and a higher theta‐join efficiency. By comparing with existing algorithms, FastThetaJoin in particular, we evaluate the performance of Prefap on both synthetic and real data streams from two‐way to multiway theta‐join to demonstrate its superiority.

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Efficiency of temporal sensor data compression methods to reduce LoRa-based sensor node energy consumption

Väänänen

Hämäläinen

2022

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Purpose Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node. Design/methodology/approach In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured. Findings Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods. Originality/value This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

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Sensor Data Stream on-line Compression with Linearity-based Methods

Cited by 4 publications

References 13 publications

Linearity-based Sensor Data Online Compression Methods for Environmental Applications

Linearity-based Sensor Data Online Compression Methods for Environmental Applications

Toward fast theta‐join: A prefiltering and amalgamated partitioning approach

Efficiency of temporal sensor data compression methods to reduce LoRa-based sensor node energy consumption

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