Abstract
Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.
Highlights
Internet of Everything (IoE) is an information technological term that combines sensing, computation, information extraction, and communication functionalities together in a device
They reported that deep-learning artificial neural network (ANN) exceeded a 95% accuracy compared with other algorithms
In Indoor Localization, we found that it is possible to locate a user in a closed area utilizing only the accelerometer and gyroscope
Summary
Internet of Everything (IoE) is an information technological term that combines sensing, computation, information extraction, and communication functionalities together in a device. Smartphones, tablets, laptops, home appliances, and even cars are examples of nodes in IoE These nodes can sense the environment utilizing their different sensors and process data, retrieve useful information, communicate over the Internet, and control their behavior adaptively. Sensor data are leveraged in new indirect ways to predict and estimate new features not directly designed to be assessed by these sensors This new usage paradigm of smartphone sensors reveals privacy and security issues since smartphone users are willing to upload their harvested data without any awareness of the information that can be mined from them [8]. This issue was referred to as big data accident [9].
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