###### Abstract
Given the lower cost and ease of EEG implementation compared to other neuroimaging techniques, it offers a practical solution for enhancing early detection and comprehensive care in this high-risk population, ultimately alleviating pressure on healthcare systems while improving patient outcomes [8]. The EEG has proven to be a valuable tool for distinguishing between individuals with schizophrenia and HCs by analyzing electrical brain activity via scalp recordings, yielding highly accurate diagnostic results. Compared to other neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), the EEG offers several advantages, including lower cost and reduced need for specialized training for its implementation [9]. These features make the EEG a practical and accessible option for the study and diagnosis of schizophrenia. Given the chronic nature of schizophrenia and the high risk of comorbid physical conditions, there is a growing need for innovative approaches to patient care that go beyond traditional in-person medical visits. A remote monitoring system could play a pivotal role in the continuous management of both mental and physical health in individuals with schizophrenia. Remote monitoring tools could allow healthcare professionals to track key physiological and behavioral indicators in real time, enabling early intervention when symptoms of psychosis worsen or when signs of physical health deterioration, such as cardiovascular issues, arise [10]. In recent years, continuous monitoring has become crucial in improving healthcare outcomes, particularly for managing complex conditions, such as schizophrenia. By collecting real-time granular data, this approach enables more informed and timely decisions, enhancing the precision and personalization of interventions. However, it also introduces challenges related to data storage, coding efficiency, and energy management. IoT-based systems generate vast amounts of data from devices, like wearable EEGs, raising concerns about digital storage. Local storage offers control but lacks scalability and incurs high costs, while cloud storage provides scalability but requires stringent security to protect sensitive health information [11]. Additionally, the continuous operation of IoT systems demands significant energy resources. Advances in low-power computing and efficient data transmission have reduced energy consumption [12], but scaling these systems increases energy demands, necessitating a balance between real-time transmission and batch processing to conserve power [13]. While IoT-based continuous monitoring offers significant benefits for healthcare, particularly in the real-time management of mental health conditions, such as schizophrenia, these systems require careful planning to ad dress challenges related to data storage, energy efficiency, and scalability. Future research should focus on optimizing storage solutions and advancing energy-efficient technologies to ensure these systems are sustainable and scalable across broader healthcare applications. Moreover, integrating a remote monitoring system could significantly improve mental health management for people with schizophrenia [14]. Since access to mental health services can be limited due to geographic, financial, or logistical barriers, especially in low-resource settings, remote monitoring could ensure consistent oversight of patients, even in rural or underserved areas. Tools such as wearable devices, mobile apps, and telemedicine platforms could be used to monitor patients' mental states, medication adherence, sleep patterns, and stress levels. These data could then be shared with healthcare providers in real time, allowing for personalized and adaptive treatment plans. Continuous mental health monitoring would enable the early detection of symptom relapse or medication side effects, ensuring prompt adjustments to treatment regimens [15].