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With the everyday growth of the Internet of Things (IoT), the number of connected sensor devices increases as well, where each sensor consumes energy while being constantly online. During that time, they collect large amounts of data in short intervals leading to the collection of redundant and perhaps irrelevant data. Moreover, being commonly battery powered, sensor batteries need to be frequently replaced or recharged. The former requires smarter and less frequent data collection, while the latter being complementary to the former requires putting them to sleep while not being used in order to save energy. The focus of this article is low-cost gas sensors as they need to preheat for several minutes to reliably collect gas concentration. However, instead of waiting for a sensor to heat up, a transient, i.e., a data trend that the sensor collects while heating up is analyzed. It is shown that long short-term memory (LSTM) neural network can be used to learn and later predict the actual gas level from a part of the transient. This way, instead of being constantly online or fully preheating, the sensor needs to be turned on for only 20 s and then sleep for 120 s. With high accuracy, our approach decreases energy consumption by up to 85% compared to a system where sensors are constantly online, and more than 50% compared to a system where a sensor collects actual values instead of a part of the transient.

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With the increase in number and size of Internet of Things systems, there is an ever-growing risk of (meta)data loss, as well as the maintenance overhead to mitigate such risks. The experts recognize three main challenges in this area that need to be tackled, namely (1) downsizing the manual work required for configuring sensor networks, (2) recovering metadata, such as sensor type, in case of connection issues, malfunctions or malicious actions in sensor networks, (3) rebuilding metadata lost due to unexpected problems within a data storage. Fortunately, all three challenges can be tackled with a uniform solution, namely the signal type classification approach, which is able to match raw signal to an appropriate data type. In this research, we evaluate and compare different approaches for signal type classification that can be used to recognize a signal type being read from an IoT sensor. This is done by using machine learning methods for modelling a signal represented as raw time series data. Three machine learning classification approaches are taken into a consideration, namely one class, two class and multi-class. According to the results of the evaluation, the most accurate multi-class random forest algorithm can correctly classify unknown signals in $$\sim {75}\%$$ ∼ 75 % of the cases based on only 20 consecutive sensor readings. Moreover, multi-class random forest can detect two most probable classes of monitored signal with the accuracy of 95%.