The Future of Healthcare: The Smart Health System

Such techniques also aim to achieve Hospital-to-Home (H2H) services, enabling patient-centric systems and improving the quality of life. Smart healthcare systems aim to achieve global accessibility, massive data analytics, low latency, high reliability, and high performance in real-time care systems. Ensuring security in healthcare systems involves verifying user identities by using strong cryptographic methods (authentication) and providing appropriate permissions (authorization). Non-repudiation ensures that messages are genuinely sent by verifying digital https://www.23ch.info/what-has-changed-recently-with-8/ signatures and timestamps (Khan et al. 2020). Additionally, maintaining data integrity and confidentiality ensures that patient information remains intact and secure throughout the system. These combined measures are essential for protecting sensitive patient data and maintaining robust security in healthcare environments.

The integration of smart grids for smart healthcare could represent shift toward advanced energy systems with healthcare infrastructure, aiming to enhance resilience, efficiency, and sustainability in the medical system. In the healthcare system, deploying smart https://elitecolumbia.com/beyond-aesthetics-how-top-product-design-agencies-drive-business-growth-in-2025.html grids could ensure a reliable power supply, which is crucial for continuous patient monitoring and the operation of critical medical equipment. These smart grids can integrate renewable energy sources, hence optimizing the energy usage and achieving energy efficiency solutions.

Has connected intelligence for resource-agnostic IoT arrived?

The cloud computing technology, with the help of IoT, provides access to various storage, computing resources, and applications from anywhere and at any time through connected IoT smart devices (Bera et al. 2014). Besides, due to the large amount of data produced in smart grids, traditional communication and information management approaches may not be sufficient, and cloud computing technologies can help deal with such massive data (Ghorbanian et al. 2019). Both public and private clouds can be efficiently utilized, where public clouds are accessible to the general public and private clouds are dedicated to a single organization, while hybrid solutions integrate both models.

Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review

A survey done during the research demonstrates that patient remote health monitoring (RHM) 61 systems are considerable and the key challenge in these systems is the services that it delivers in various environments. RHM systems are implemented to examine and monitor patients’ conditions remotely benefiting patients, hospital staff, and resources 62. Even though this system provides diverse services some challenges that need to be overcome are data privacy and data storage which can be solved by introducing cloud-based IoT healthcare systems. RHM system can be implemented to provide a diverse variety of services like BP checks, heart rate monitoring, temperature check, diabetes checks, rehabilitation, cancer detection, brain-related diseases, health monitoring, and so on. Therefore, these systems can be developed for different tasks, and they are closely related to the applications of equivalent-enabled technologies.

2.2 Current challenges in energy harvesting and future research directions

Consequently, feature engineering has been considered in ML, focusing on constructing features from raw data to improve the performance of ML. A major challenge of efficient digital health services on the fog network is that they typically span multiple computational activities, hence performing big data processing over sensible information, which must be protected (García-Valls et al. 2020). To this end, using the capabilities of current processors can improve the servicing of remote patient nodes.

• Healthcare systems around the world are burdened by inefficiencies in both administrative and clinical processes 5.
• These performance limitations may impact the responsiveness required in time-sensitive clinical scenarios.
• To this end, a semantic data model called the ubiquitous data accessing (UDA) method-based IoT has been proposed for improved data accessibility.
• Besides, due to the large amount of data produced in smart grids, traditional communication and information management approaches may not be sufficient, and cloud computing technologies can help deal with such massive data (Ghorbanian et al. 2019).

Important areas of focus include security and privacy aspects, standardization and regulatory efforts, and prototyping. Furthermore, exploring how electromagnetic nanonetworks can enhance existing applications and services, as well as enable new ones, will be crucial for future work. Furthermore, edge computing enables offline operation and resilience to network failures (Raeisi-Varzaneh et al. 2023).

The participation of many organizations in the healthcare network can lead to issues with large volumes of health data, frequent requests, and concerns about stability. Research should explore scheduling methods that can effectively handle the heterogeneity of edge environments, accommodating diverse end devices and varying resources. Mobility challenges, such as frequent link failures due to device movement, necessitate efficient task scheduling mechanisms that can dynamically adapt to changing conditions. Moreover, developing fault-tolerant scheduling strategies is essential to ensure reliability in dynamic edge environments prone to failures and faults, supporting robust 5 G and IoT applications (Raeisi-Varzaneh et al. 2023). Scaling edge computing capabilities to manage the increasing volume of data from diverse endpoints efficiently is also a critical area for future investigation. In this regard, blockchain technology, with its inherent decentralization and robust security features, holds significant promise for advancing big data services and applications (Deepa et al. 2022).

The proposed approach tracked the physiological data of elderly people to detect specific disorders, supporting early intervention. The work in Talal et al. (2019) provided a review of real-time RPM systems for triage and priority systems based on multi-layer architecture. The implementation of multi-layer architecture in the RPM system was also explained in Farahani et al. (2018).

NFV can improve network service deployment and management by enhancing reliability, scalability, and flexibility. NFV can be utilized to deploy and manage network functions, allowing for efficient resource utilization and rapid service deployment. With NFV, several mobile devices can access computing services from a single-edge computing server. This is accomplished by building various virtual computers with the capacity to perform multiple activities simultaneously. This integration helps in ensuring immutable, auditable, non-repudiable, consistent, and anonymous configuration (Dai et al. 2019). NFV enables flexible provisioning, deployment, and centralized management of virtual network functions.

The primary energy means is glucose and the pancreas secreted hormone, i.e., insulin regulates the metabolism of the body and controls the glucose level in the blood. If ample insulin is not produced in the body diabetes is caused as blood glucose cannot be regulated and remains in the blood. According to medical research, diabetes is not completely curable, but it is treatable and if it is left untreated it emerges into various diseases viz., heart stroke, brain stroke, kidney failure, blindness, and even may lead to death. Thus, it is requisite to detect diabetes at its early stage, thereby diagnosing and treating it to prevent disease advancement as it aids to death and further diseases. Vast medical data can be found through various resources like lab reports, medical images, Electronic Health Record (EHR), and clinical reports but the significant challenge is data understanding and interception.

It ensures low and predictable latency for service delivery during emergencies, reduces the volume of data transferred to the cloud by processing and storing vital data locally, and conserves bandwidth and battery resources (Kashani et al. 2021). Wireless power transmission could be a promising solution for energy harvesting, but it needs further investigation. Future research should focus on developing energy efficiency algorithms, resource management techniques, and hardware-software architectures that extend the lifespan of sensors and devices. In 45, the author developed a model (ML based, i.e., ANN and RF) to diagnose and identify bacteria intoxication among serious patients and recorded 90.8% accuracy. In 46, 47, and 48, a system was proposed by utilizing data mining techniques, ensemble learning approach, and SVM respectively to identify readmitting of ICU patients. In 49, a DL model was proposed by employing a CNN algorithm to diagnose glaucoma and obtained 81.6% accuracy on data acquired from Beijing Tongren Hospital.