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Title	An Online Model To Minimize Energy Consumption Of Iot Sensors In Smart Cities
ID_Doc	8796
Authors	Al-Hawawreh M.; Elgendi I.; Munasinghe K.
Year	2022
Published	IEEE Sensors Journal, 22, 20
DOI	http://dx.doi.org/10.1109/JSEN.2022.3199590
Abstract	The Internet of Things (IoT), which allows systems of billions or trillions of 'things,' such as sensors, to communicate with each other over the Internet, is encountering several technical and application challenges. One of these challenges is that IoT sensors send redundant and self-similar data to the edge gateways consuming a large amount of energy and making it extremely difficult to obtain an appropriate network lifetime, which has become a bottleneck in scaling such applications. To address these issues, we propose a new solution based on powering sensors using artificial intelligence to make smart decisions about transmitting collected readings. We take advantage of autocorrelation (AC) to detect self-similarity and propose updating mechanism that employs deep reinforcement learning (RL). Our proposed model is a real-time model that can determine the redundant data and self-similarity and then make the smart decision about transmitting data. We evaluate our proposed solution using measurements obtained from Queanbeyan smart city, Australia, and available-public dataset and show that our proposed model can reduce the amount of transmitted data and minimize the power consumption of sensors. © 2001-2012 IEEE.
Author Keywords	Autocorrelation (AC); energy consumption; Internet of Things (IoT); reinforcement learning (RL); smart city

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