Data Missing Problem in Smart Surveillance Environment

Peixoto, Maycon L. M.; Souza, Igo; Barbosa, Matheus; Lecomte, Gabriel; Batista, Bruno C.; Kuehne, Bruno Tardiole; Filho, Dionisio Machado Leite

doi:10.1109/hpcs.2018.00152

Cited by 5 publications

(2 citation statements)

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“…Finally, the singular spectrum analysis (SSA) to maximize the accuracy of data imputation in IoT-based surveillance environments is advanced in [ 36 ], where a non-parametric spectral estimation along with spatial–temporal correlations of time-series data from IoT devices are exploited together.…”

Section: Related Workmentioning

confidence: 99%

“…While all the works discussed earlier present interesting and innovative techniques or frameworks to manage the problem of missing data, some important differences arise with respect to our proposal. First, in many works (see, e.g., [ 25 , 26 , 27 , 28 , 30 , 31 , 32 , 33 , 35 , 36 ]), despite the data coming from the IoT, the analysed imputation algorithms actually run on standard computer architectures (e.g., PCs, laptops, etc.). Moreover, the importance of tackling the missing data problem as close as possible to the devices is evident (e.g., at the Edge of the network [ 37 ]); thus, our assessments are carried out straight on the board of the devices.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

Erhan

Mauro

Anjum

et al. 2021

Sensors

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Recent developments in cloud computing and the Internet of Things have enabled smart environments, in terms of both monitoring and actuation. Unfortunately, this often results in unsustainable cloud-based solutions, whereby, in the interest of simplicity, a wealth of raw (unprocessed) data are pushed from sensor nodes to the cloud. Herein, we advocate the use of machine learning at sensor nodes to perform essential data-cleaning operations, to avoid the transmission of corrupted (often unusable) data to the cloud. Starting from a public pollution dataset, we investigate how two machine learning techniques (kNN and missForest) may be embedded on Raspberry Pi to perform data imputation, without impacting the data collection process. Our experimental results demonstrate the accuracy and computational efficiency of edge-learning methods for filling in missing data values in corrupted data series. We find that kNN and missForest correctly impute up to 40% of randomly distributed missing values, with a density distribution of values that is indistinguishable from the benchmark. We also show a trade-off analysis for the case of bursty missing values, with recoverable blocks of up to 100 samples. Computation times are shorter than sampling periods, allowing for data imputation at the edge in a timely manner.

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Section: Related Workmentioning

confidence: 99%