Abstract
Machine Learning brings intelligence services to IoT systems, with Edge Computing contributing for edge nodes to be part of these services, allowing data to be processed directly in the nodes in real time. This paper introduces a new way of creating a self-configurable IoT node, in terms of communications, supported by machine learning and edge computing, in order to achieve a better efficiency in terms of power consumption, as well as a comparison between regression models and between deploying them in edge or cloud fashions, with a real case implementation. The correct choice of protocol and configuration parameters can make the difference between a device battery lasting 100 times more. The proposed method predicts the energy consumption and quality of signal using regressions based on node location, distance and obstacles and the transmission power used. With an accuracy of 99.88% and a margin of error of 1.504 mA for energy consumption and 98.68% and a margin of error of 1.9558 dBm for link quality, allowing the node to use the best transmission power values for reliability and energy efficiency. With this it is possible to achieve a network that can reduce up to 68% the energy consumption of nodes while only compromising in 7% the quality of the network. Besides that, edge computing proves to be a better solution when energy efficient nodes are needed, as less messages are exchanged, and the reduced latency allows nodes to be configured in less time.
Highlights
The Internet of Things (IoT) relies on the devices ability to share information among them and with the cloud, for storage and data processing
This paper presents a methodology for an implementation of an autonomous configuration system for peer-to-peer communication in smart nodes supported by machine learning, that uses regressions to predict the energy consumption and link quality of a connection and chooses the best protocol and transmission power to use
The results show that when using Edge Computing alongside Efficiency Model (EFM) mode, the quality of the signal decreases while lower transmission power values are used, facing the Best Link Model (BLM) mode
Summary
The Internet of Things (IoT) relies on the devices ability to share information among them and with the cloud, for storage and data processing. With 50 billion devices expected to be connected by 2030, for the deployment of IoT system to be scalable, nodes need to be more autonomous. Communication is a major component in IoT but it is a power-hungry operation, mainly over large distances [1]. With the proliferation of Smart Cities, these networks of nodes need to be powered in a more efficient and green way [2]. Fafoutis said [3] ‘‘any data that is wrongly gathered, transmitted, stored or processed is a potential waste of energy’’. For the devices in higher numbers and powered by batteries, the end devices, a change
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