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.

No abstract

Temperature perception is a highly nonlinear phenomenon with faster rates of change being perceived at much lower thresholds than slower rates. This paper presents a method that takes advantage of this nonlinear characteristic to generate a perception of continuous cooling even though the average temperature is not changing. The method uses multiple thermal actuators so that a few are cooling quickly while the rest of the actuators are heating slowly. The slowly-heating actuators are below the perceptual threshold temperature change and hence are not perceived, while the quickly-cooling actuators are above the perceptual temperature change, hence are perceived. As a result, a feeling of decreasing temperature was elicited, when in fact, there was no net change in the temperature of the skin. Three sets of judiciously designed experiments were conducted in this study, investigating the effects of actuator sizes, forearm measurement locations, patterns of actuator layout, and various heating/cooling time cycles. Our results showed that 19 out 21 participants perceived the continuous cooling effect as hypothesized. Our research indicates that the measurement location, heating/cooling cycle times, and arrangement of the actuators affect the perception of continuous cooling.

No abstract

No abstract