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This environment has been utilized in a smart home monitoring app [ ], a fitness app [ ], a health monitoring app [ ], and smart lamp design utilizing smartphone sensors [ ]. The hybrid tool category shows that side-channel attacks are easy to implement. To start implementing apps, Figure 9 shows the required steps.

Firstly, data dumps from smart devices are required. These dumps as shown are available over the web. Any of these dumps can be downloaded.

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Secondly, a feature extraction process should be implemented. As mentioned, time-, frequency-, and wavelet-domain and row data features can be extracted from the data.

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Sometimes adopting the easiest one in implementation dominated over the accurate one. Finally, the mathematical model is ready for deployment. To deploy the trained algorithm in the real world, a smart device app is required. MAI simplifies this task. The algorithm should be embedded in any type of apps. The deployment steps are shown in Figure The deployment process consists of two main parts: client side and server side. The client side is the smart device app. This app should at least contain four different modules. The first module is the timer module which will record sensor reading over preconfigured periods.

Moreover, the time stamp of sensor data harvesting has been utilized as a feature in different algorithms as was shown. The second module is sensor modules. What type of sensor data has been utilized in the training process should be harvested in this step. All sensors, except for the fingerprint sensor, are implemented in MAI. The third module is the data saving module. This module is required to reduce network usage, and any data processing modules required in the smart device app. MAI allows the programmer to save the app data in an internal unique database.

The final step is data transmission over the Internet module. The HTTP protocol can be adopted for this step. In the server side, a web application should be written and hosted over the web. The application should extract any received data from the data transmission process. The Internet protocol IP address of the sender should be recorded to distinguish app users. Moreover, it should record time stamps. The second step in this application is feature extraction from the received data. Finally, the trained mathematical module is employed in the harvested features to obtain the hidden information.

Other choices can be made. Another method may be utilized to reduce network usage. All the steps are moved to the smart device app. In this method, the network usage will be reduced to the minimum since the device will only send the hidden information. However, the computational load will increase. To reduce the computational load of the app, the timer module can be configured to employ the feature extraction and the MLA mathematical module in very long periods.

These two implementation scenarios show how easy it is to breach the security of smart device users in the IoE era. Smart devices are everywhere. The IoE era has arrived. The advantages, applications, and usability of this paradigm have been introduced in many research papers.

The privacy and the security of smart devices in IoE have attracted researchers over the years to construct secure systems.

Nevertheless, machine learning and big data complicated the story. It has been shown how smart device sensors, which are utilized to enhance the usability of the devices, may be leveraged in useful applications on the one hand and in hacking and attacking issues on the other hand. Moreover, it has been shown how these threats and attacks can be implemented and deployed in a simple method utilizing event-driven programming without deep programming skills.

Unfortunately, there is no hidden data protection manual that can be downloaded and followed to solve the accuracy, privacy, and security issues. However, many techniques can be utilized from app designers and users to reduce these issues as much as possible. User awareness is the most important step to prevent hidden data issues.

Users should be aware of what to upload to the Internet.

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App permissions should be read carefully before installing new apps. Users should not install apps from unknown sources or developers. Users should not grant any permission required from any app until they think why such an app requires such permissions. For example, different games on the Android market require access to the smartphone media and files, why? Users should be smarter than their smart devices.

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Smart device operating system developers should increase and enhance the permissions on smart device sensor access. More control should be granted to the users. More warning messages should be shown all the time. Do not show this again message should not be used. More research and development in this field are required. For the accuracy enhancement, more data should be harvested from different ages, genders, and countries to reduce the impact of different variables on the concluded output. The developed applications should be tested in real life through different users for a period of time before announcing the validity of its conclusions.

Social networks are a fertilized environment for this step. Finally, we believe that the static design of smart devices is one of the main issues in the area of hidden data threats. For example, many of the smart device users do not know what sensors they have and how to use them. Moreover, many sensors are useless for these users. If smart device users have the ability to design and configure their devices with only the necessary sensors and parts, a part of this issue will be solved.

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This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Journal overview. Special Issues. Academic Editor: Matthew Brodie. Received 12 Nov Revised 12 Jan Accepted 11 Feb Published 15 May Abstract Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices.

Introduction Internet of Everything IoE is an information technological term that combines sensing, computation, information extraction, and communication functionalities together in a device. Smart Device Architecture Smart devices in this work are defined as the hand-held devices.

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Figure 1. Figure 2. Figure 3. Figure 4. Feature Citation Definition Mean [ 18 , 74 , 75 , 92 , 95 , — ] The summation of data points divided by their number Std deviation [ 18 , 58 , 74 , 75 , , , ] It is the square root of variance Average deviation [ 18 , 58 , 74 , , ] The average separation of data points from their mean or average value Skewness [ 18 , , ] Measures the asymmetry from the mean value.

It utilizes the mean and the variance Kurtosis [ 18 , , ] Estimates the frequency of extreme values. It utilizes the mean value in its formula RMS amplitude [ 18 , , , ] It is leveraged to calculate the power of a signal. Table 1. Feature Citation Spectral centroid [ 18 , , , ] Spectral Std deviation [ , ] Spectral kurtosis [ 18 , , ] Spectral skewness [ 18 , , ] Spectral crest [ 18 , , ] Irregularity-J [ 18 , , ] Smoothness [ 18 , , ] Flatness [ 18 , , ] Roll off [ 18 , , ] Entropy [ 18 , ] Brightness [ 18 , ] Roughness [ 18 , ].