**1. Introduction**

Neurological and mental ailments regularly present with side effects that are mind boggling, abnormal, fluctuant in illness evolution, and show high fluctuation between patients. Current diagnostic and efficacy evaluation methods frequently rely on inclinic visits and patients', caregivers', or clinicians'subjective evaluations. Most of the time, in-clinic evaluation methods are expensive, take a long time, and only allow for

a limited number and quality of observations. They also frequently exhibit high interand intra-rater variability. In the early stages of the disease, when there is a lag between the onset of the pathological process and the onset of symptoms, the diagnostic process may be affected by the aforementioned issues with traditional methods of diagnosis [1].

Neurological and psychiatric illnesses typically last a long time and cause significant changes in symptoms over time. As a result, the primary challenges in evaluating distinct diseases during periodic in-clinic visits are recall and reporting biases. Sensorbased smart technologies are rapidly developing remote monitoring of patients in their daily lives, which may assist clinicians in facilitating early diagnosis and evaluating and adjusting interventions. Use of recently developed smart sensor technologies for patient monitoring has become increasingly popular [1].

Parkinson's disease is a degenerative disorder of slow progress of the nervous system caused by the lack of levels of dopamine, which can provoke uncontrolled involuntary movements of the body and psychological affections [2].

An incremental number of sensors, such as motion (acceleration and gyroscope), location (the Global Positioning System, or GPS), environment (barometer, temperature, and light), and health (heart rate) sensors, are included in the modern smartphones and wearables. Smartphones have the potential to replace in-clinic evaluations for a variety of valuations due to their extensive array of sensors, ability to collect ecological momentary assessments (EMA), and information about social interaction (such as social media, messaging, and phone calls). Digital biomarkers (DBs) are terms used to describe the health-related data gathered during clinical trials. In order to gain a deeper comprehension of particular diseases, DBs can provide information that is useful, objective, and ecologically valid. Additionally, DBs make it possible to conduct frequent assessments of larger target populations over longer time periods, which may provide an in-depth understanding of the variation in daily life between and within individuals due to disease [1].

A few commitments empowering the utilization of cell phones as a valuation device have been as of late presented. Commercial devices make up the first set. By displaying notifications about a user's heart rate, number of steps taken, and type of activity, these apps primarily aim to provide feedback on the user's daily activities. However, the majority of these devices do not support high-frequency data collection and only provide limited access to the raw data. Applications and platforms created by investigators are the second category. The primary goals of these mostly open-source platforms are to make it possible to share and reproduce data as well as collect data for investigation applications. However, these software packages are much of the time restricted by a frequently constricted concentration to a few explicit clinical signs or concerning protection perspectives. Additionally, these periodically updated platforms render some unstable for the rapidly expanding smartphone ecosystem [1].

An example is a System developed by [3]. It builds on the fact that the medication treats Parkinson's disease gait abnormalities. It works by putting a smartphone in the pocket without requiring any special skills—a common occurrence in our daily lives. The information about a person's gait can be continuously detected by a smartphone without the user's active participation. The system makes passive sensing possible in this way. The system has two ends: a smartphone end and a cloud-server end. The smartphone sends the raw gait data to the cloud server from the smartphone end. After that, it is the job of the cloud server to look at the data and send the results to the smartphone. The smartphone notifies the user of the next medication time or reminds them to take their medication according to a drug schedule set by the healthcare

*A Simulation Model of a Blockchain-Based Decentralized Patient Information Exchange… DOI: http://dx.doi.org/10.5772/intechopen.109591*

provider. The system assists patients in avoiding missed, anticipatory, or additional doses through this approach. Although several platforms can collect context-driven data, the trade-off between privacy, optimization, stability, and research-grade data quality is not finding an optimal solution [1].

This paper objects to the solution of the abovementioned problems, to propose a decentralized authentication system utilizing blockchain technology and fog computing. With the characteristics and features of blockchain technology such as smart contracts, it addresses authentication using a decentralized database and communication between fog devices (nodes). The proposed system achieves authentication and communication without a central authority typical for this technology.

The main contributions of this paper can be summarized as follows:


The rest of this paper is organized as follows: Section 2 introduces authentication systems, fog computing, and blockchain technology in Smart Healthcare Systems. Section 3 discusses the related works.

Section 4 presents the proposed authentication system. Section 5 provides the implementation details of the proposed system, followed by details of the experimental setup using simulation for validation of the proposed system.

Section 6 concludes the paper along with future directions.
