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.

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Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

We present a jaw, face, or head movement (face-related movement) recognition system called CanalSense. It recognizes face-related movements using barometers embedded in earphones. We find that face-related movements change air pressure inside the ear canals, which shows characteristic changes depending on the type and degree of the movement. We also find that such characteristic changes can be used to recognize face-related movements. We conduct an experiment to measure the accuracy of recognition. As a result, random forest shows per-user recognition accuracies of 87.6% for eleven face-related movements and 87.5% for four OpenMouth levels.

CanalSense: Face-Related Movement Recognition System based on Sensing Air Pressure in Ear Canals

Barometers are among the oldest engineered sensors. Historically, they have been primarily used either as environmental sensors to measure the atmospheric pressure for weather forecasts or as altimeters for aircrafts. With the advent of microelectromechanical system (MEMS)-based barometers and their systematic embedding in smartphones and wearable devices, a vast breadth of new applications for the use of barometers has emerged. For instance, it is now possible to use barometers in conjunction with other sensors to track and identify a wide range of human activity classes. However, the effectiveness of barometers in the growing field of human activity recognition critically hinges on our understanding of the numerous factors affecting the atmospheric pressure, as well as on the properties of the sensor itself-sensitivity, accuracy, variability, etc. This review article thoroughly details all these factors and presents a comprehensive report of the numerous studies dealing with one or more of these factors in the particular framework of human activity tracking and recognition. In addition, we specifically collected some experimental data to illustrate the effects of these factors, which we observed to be in good agreement with the findings in the literature. We conclude this review with some suggestions on some possible future uses of barometric sensors for the specific purpose of tracking human activities.

https://hal.archives-ouvertes.fr/hal-03031690/document

On the Challenges and Potential of Using Barometric Sensors to Track Human Activity

Rail transit systems are important parts of modern cities such as the urban subways and intercity trains. During the travelling time, many passengers access the networks via cellular communications for working or entertainment. However, intermittent connection of LTE signals usually decreases the user experiences, especially due to the poor LTE link quality of underground subways. On the other hand, we observe that there are usually multiple cellular operators covering a city. For example, AT&T and T-Mobile in American cities, China Mobile and China Unicom in Chinese cities. Based on the different eNodeB distributions of different operators, we propose to study a new problem cross-operator switch . Unlike the handover between eNodeBs from one operator, cross-operator switch can switch to an eNodeB belonging to other operators. Although current mobile phones support dual SIM cards and Android supports the quick swap between two cards, it is still challenging to determine which eNodeB to switch because of no information exchange between different operators. To address this challenge, we propose a robust Crowdsensing based Switch (CrowdSwitch) solution dedicated for rail transit systems. CrowdSwitch utilizes mass mobile devices of passengers to sense and collect LTE signals along subway tracks, builds the signal heat map (S-Map) in the cloud servers, and finally recommends the optimal switch taking advantages of the known tracks. We implement CrowdSwitch in off-the-shelf mobile phones and conduct extensive experiments on Shanghai Metro. The results show that CrowdSwitch can depict the accurate LTE distribution and always recommend the optimal operator for users to connect.

Crowdsensing-Based Cross-Operator Switch in Rail Transit Systems

While satellite-based global navigation systems have become essential tools in our daily lives, their effectiveness is often hampered by the fact that the signals cannot be accessed in underground, indoor, or underwater environments. Recently, a novel navigation system has been invented to address this issue by utilizing the characteristics of the ubiquitous and highly penetrative cosmic-ray muons. This technique, muometric navigation, does not require active signal generation and enables positioning in the aforementioned environments within a reference coordinate defined by the three-dimensional positions of multiple detectors. In its first phase of development, these reference detectors had to be connected to the receivers via a wired configuration to guarantee precise time synchronization. This work describes more versatile, wireless muometric navigation system (MuWNS), which was designed in conjunction with a cost-effective, crystal-oscillator-based grandmaster clock and a performance evaluation is reported for shallow underground/indoor, deep underground and undersea environments. It was confirmed that MuWNS offers a navigation quality almost equivalent to aboveground GPS-based handheld navigation by determining the distance between the reference frame and the receivers within a precision range between 1 and 10 m. 

https://www.nature.com/articles/s41598-022-13280-4.pdf

Wireless muometric navigation system

Knowing the location of a train is necessary to develop a useful service for train passengers. However, popular localization methods such as GPS and Wi-Fi are not accurate especially on a subway. As an alternative method, estimation of motion state and stop station by using sensors on a smartphone is being studied for subway passengers. This paper proposes a localization method that uses only a barometer on a smartphone. We estimate motion state from the change of elevation, and also estimate latest stop station by the similarity of a series of elevations recorded when the train stopped and actual elevations of stations. By estimation of the motion state and the latest stop station, development of various context-aware services for subway passengers becomes possible. Through experiments in four lines of subway in Tokyo, we demonstrated that the accuracy of estimation of the motion state is 86%, and estimation of the stop station is 58%.

Estimate a user's location using smartphone's barometer on a subway

Knowing the location of a train is necessary for the development of useful services for train passengers. However, popular localization methods such as GPS and Wi-Fi are not accurate, especially on a subway. This paper proposes an online algorithm for localization on a subway using only a barometer. We estimate the motion state from the change of elevation, then estimate the last station stopped at using the similarity of a series of elevations recorded when the train stopped to the actual elevations of the stations. We evaluated the proposed method using data from the subway in Tokyo. We also developed a mobile application to demonstrate the proposed method.

Satoshi Hyuga

Papers

Estimate a user's location using smartphone's barometer on a subway

An online localization method for a subway train utilizing the barometer on a smartphone