**3. Results**

#### **3.1 Related works**

Fog computing and blockchain technology have been used in IoT, sales, cloud computing, and other systems, primarily in decentralized ways, in a number of works proposed for authentication. In order to comprehend the proposed scheme, a review of schemes including cloud computing, fog computing, blockchain technology, IoT, and authentication has been provided in this section.

#### **3.2 JTrack**

JTrack [1] was developed as an online server-side dashboard and an Android-based smartphone application. The main components of the JTrack application fall into the following categories: Human Activity Recognition (HAR), location data, sensor data, smartphone, and application usage monitoring, and both active (with user interaction) and passive (without user interaction) monitoring options are available for each component. The dashboard side is a web-based platform for study creation and management that incorporates DataLad infrastructures to make data management and sharing easier. Because JTrack is a modular open source with a high level of optimization, it is a practical solution for clinicians and researchers to collect, manage, and share digital biomarker data, particularly for Parkinson's disease patients.

The main components of the JTrack platform are shown in **Figure 3**.

In [1], the authors proposed a solution with the aim of QR-Code Authentication to provide a secure way of activation. Additionally, the JTrack platform was developed in accordance with Google Developer Policies and GDPR. At no stage is any sensitive information, such as a person's name, phone number, contacts, or actual location, recorded. Using the MD5 stability checksum, all of the collected data were transferred using the Hypertext Transfer Protocol Secure (HTTPS) protocol.

Regarding patient privacy, all JTrack users receive clear explanations of what was recorded and why. During installation and activation, permission requests for each module must be approved. All members may likewise pause and leave a review whenever straightforwardly from the application. Additionally, clinicians can maximize control over the collected data with remote configuration and one-step recording without having to collect any identifying information [1].

Along with Firebase integration for performance and crash reports, automatic restarting is implemented to reduce data loss caused by crashes or reboots. The optional recorded data, which are not active by default, include information about the phone's manufacturer, model, and operating system version. This information can be used to analyze and deal with cross-sensor variability [1].
