## 4 Integration of Biosensors
### Architecture and Design Considerations
This approach reduces latency and enables a swift response to emerging health-related insights [51]. ML algorithms play a pivotal role in enhancing the capabilities of these systems. By incorporating ML into the analytics process, the system gains the ability to derive meaningful and actionable insights from biosensor data [11]. ML algorithms can identify patterns, trends, and potential anomalies in real time, contributing to personalized and predictive healthcare. The significance of real-time data analytics in biosensor-integrated multi-hop IoT systems lies in the capacity to provide healthcare professionals with immediate and relevant information. This rapid analysis of biosensor data not only supports timely decision making but also opens avenues to personalized healthcare solutions [52]. The combination of real-time analytics and ML contributes to a more efficient and effective healthcare ecosystem by harnessing the full potential of biosensor data. The IoT employs various architectures, including three-layer, four-layer, and five-layer structures [53], but structures vary depending on the optimum design for the IoT. In multi-hop IoT system design, the five-layer structure shown in Figure 7 is suitable, comprising the physical layer, protocol layer, transport layer, application layer, and business layer [54]. The physical layer, also known as the perception layer, involves biosensors connected to multihop nodes as microcontrollers for collecting data. The protocol layer facilitates sensor-data transfer between the sending node and the receiving node via multiple intermediary nodes using communications protocols such as Mesh Networking, Wi-Fi, Zigbee, Thread, and BLE Mesh. The transport layer assumes responsibility for providing connection-oriented and reliable communications. It ensures accurate delivery of data to the destination by employing the Transmission Control Protocol (TCP), commonly used when prioritizing reliability and data integrity, guaranteeing error-free transmission of all data. Internet Protocol (IP) plays a critical role in device addressing, enabling routers to appropriately route data packets to their intended destinations. On the other hand, TLS and its precursor, SSL, are accountable for securing communications channels. These protocols employ encryption algorithms, safeguarding data confidentiality and integrity throughout transmission. The application layer provides specific IoT services like smart healthcare under various application protocols like Advanced MQTT, which works for efficient and scalable communications through a publish-subscribe model, and CoAP, which is responsible for facilitating resource-constrained device interactions using a lightweight, RESTful approach. HTTP serves as the foundation for web-based communications, with Hypertext Transfer Protocol Secure (HTTPS) encrypting the data for transfer in IoT applications. The business layer in the IoT, in collaboration with cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), IBM Cloud, and Oracle Cloud, manages overall IoT application and service operations. It oversees high-level analysis reports, creates flowcharts and graphs, analyzes results, and suggests improvements for devices and services deployed in the IoT ecosystem. Integration with these cloud platforms allows robust management, scalability, and advanced analytics, contributing to the efficient functioning and optimization of IoT applications and services.