## 4 Integration of Biosensors
### Architecture and Design Considerations  
The integration of biosensors with multi-hop IoT systems in healthcare herals a transformative era in remote patient monitoring and health data acquisition. The architecture and design of such systems require careful consideration to ensure seamless functionality, real-time data transmission, and effective utilization of biosensor-generated information. This section delves into the key architectural and design considerations vital to successful integration of biosensors with multi-hop IoT systems in healthcare applications. A foundational aspect of designing multi-hop IoT systems for biosensor integration is the adoption of a scalable and modular architecture. Scalability ensures that the system can efficiently handle the increasing number of connected biosensors, accommodating the growing demands of healthcare applications [46]. A modular architecture allows for flexibility, enabling the integration of diverse biosensors with varying functionalities without compromising overall system coherence. In the landscape of multi-hop IoT systems, edge computing and fog computing are instrumental in optimizing data processing and decision making. Edge computing, situated at each intermediate node in the multi-hop path, enables localized data processing. This occurs at the periphery of the network, reducing reliance on a central server [47]. Each intermediate node is treated as an _edge_, facilitating distributed processing and potential latency reduction. Fog computing, a strategic extension of edge computing, operates through strategically placed fog nodes within a multi-hop network. These nodes perform advanced processing and data aggregation, contributing refined data for transmission to a central server [48]. The central server, located at the end of the multi-hop path, conducts the final analysis and decision making. The combined utilization of edge and fog computing in multi-hop IoT systems minimizes the burden on central servers, enhances real-time capabilities, and optimizes data-processing efficiency. This distributed processing model strategically leverages intermediate and fog nodes, ultimately improving the overall efficiency and responsiveness of patient monitoring systems. Dynamic routing algorithms play a crucial role in the functionality of multi-hop IoT networks, which are inherently dynamic due to the changing nature of the network topology. These algorithms are designed to optimize data transmission paths, ensuring reliable and efficient communication between biosensors and the central data collection point. One prominent dynamic routing algorithm is Ad hoc On-Demand Distance Vector (AODV). _On-demand_ means establishing a route between nodes only when necessary. When a node requires a route to another node, AODV initiates a route-discovery process. It maintains routing tables at each node to store information about the discovered routes, and these tables are updated dynamically based on the changing network conditions. AODV is particularly suitable for multi-hop environments because it adapts to variations in the network topology. Another noteworthy algorithm is Dynamic Source Routing (DSR), which is an on-demand algorithm that discovers routes as needed. However, it distinguishes itself by utilizing source routing, where the complete route is included in the packet header. This eliminates the need for intermediate nodes to maintain routing tables, simplifying the process and making it suitable for dynamic multi-hop environments [49]. Real-time data analytics is a critical aspect in the design of biosensor-integrated multi-hop IoT systems. This capability allows the immediate processing of biosensor data, ensuring timely detection of anomalies and facilitating prompt decision making, particularly in healthcare applications. To enable real-time analytics, these systems implement analytics engines that can process biosensor data on the fly [50]. This means that data are analyzed as they are generated, without the need to store large volumes of data before analysis.