Inference over Wireless IoT Links with Importance-Filtered Updates
| Autor: | Nikola Zlatanov, Anders E. Kalør, Ivana Nikoloska, Josefine Holm, Petar Popovski |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
Signal Processing (eess.SP) FOS: Computer and information sciences Computational complexity theory Computer Networks and Communications Computer science Machine vision Distributed computing Computer Science - Information Theory Internet of Things Inference Artificial Intelligence 11. Sustainability Machine learning FOS: Electrical engineering electronic engineering information engineering Wireless Electrical Engineering and Systems Science - Signal Processing computer.programming_language Artificial neural network business.industry Information Theory (cs.IT) Rate reduction Filter (signal processing) Distributed importance filtering Hardware and Architecture Benchmark (computing) business computer |
| Zdroj: | Nikoloska, I, Holm, J, Kalør, A E, Popovski, P & Zlatanov, N 2021, ' Inference over Wireless IoT Links with Importance-Filtered Updates ', IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 4, 9512263, pp. 1089-1098 . https://doi.org/10.1109/TCCN.2021.3104287 |
| Popis: | We consider a communication cell comprised of Internet-of-Things (IoT) nodes transmitting to a common Access Point (AP). The nodes in the cell are assumed to generate data samples periodically, which are to be transmitted to the AP. The AP hosts a machine learning model, such as a neural network, which is trained on the received data samples to make accurate inferences. We address the following tradeoff: The more often the IoT nodes transmit, the higher the accuracy of the inference made by the AP, but also the higher the energy expenditure at the IoT nodes. We propose a data filtering scheme employed by the IoT nodes, which we refer to as distributed importance filtering in order to filter out redundant data samples already at the IoT nodes. The IoT nodes do not have large on-device machine learning models and the data filtering scheme operates under periodic instructions from the model placed at the AP. The proposed scheme is evaluated using neural networks on a benchmark machine vision dataset, as well as in two practical scenarios: leakage detection in water distribution networks and air-pollution detection in urban areas. The results show that the proposed scheme offers significant benefits in terms of network longevity as it preserves the devices' resources, whilst maintaining high inference accuracy. Our approach reduces the the computational complexity for training the model and obviates the need for data pre-processing, which makes it highly applicable in practical IoT scenarios. |
| Databáze: | OpenAIRE |
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