An Artificial Intelligence Approach for Time Series Next Generation Applications

Dridi, Aicha; Khedher, Hatem Ibn; Moungla, Hassine; Afifi, Hossam

doi:10.1109/icc40277.2020.9148931

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…We embarked an LSTM algorithm similar to the ones studied in [27] and [30] onto an electronic device as a way to test the experimental feasibility of these solutions. These articles propose to use LSTMs that are sufficiently simple to be implemented and embarked onto devices.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Embedding ML Algorithms onto LPWAN Sensors for Compressed Communications

Bernard

Dridi

Marot

et al. 2021

2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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LPWANs are networks characterized by the scarcity of their radio resources and their limited payload size. To extend the efficiency of the data transmission by decreasing the traffic sent from sensors, this paper proposes a lossy compression method using known ML techniques. We embedded a pre-trained neural network directly on constrained LoRaWAN devices and we tested the trade-off between compression ratio and accuracy of the compression algorithm. This paper studies multiple aspects of the system -energy consumption, error rate due to the lossy compression, compression ratio and the impact of LSTM parameter quantization -to measure the possible strengths and weaknesses of using a dual prediction system in order to reduce transmission costs. Surprisingly, machine learning used in this context does not consume a lot of energy and it even leads to energy saving in the very constrained devices which are the sensors.

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Section: Methodsmentioning

confidence: 99%

“…For this paper, the prediction algorithms used rely on [30]. We aim to have a reliable data prediction based on an LTSM neural network and run the predictor on both the sensor and the network infrastructure.…”

Section: Related Workmentioning

confidence: 99%