## 6 Challenges
Managing temporal misalignments in data streams becomes necessary to derive meaningful insights. The sheer volume of data generated by biosensors necessitates efficient storage and processing strategies in the cloud. Aggregating and fusing large datasets for long-term analysis requires a scalable cloud infrastructure and optimized algorithms. Striking a balance between real-time processing and long-term storage for trend analysis is a key challenge in designing effective data aggregation and fusion mechanisms. Moreover, maintaining data integrity during the aggregation process is critical for accurate health assessments. Addressing issues such as data duplication, noise, and outliers is essential to ensuring the reliability of aggregated information. Collaborative efforts across the healthcare and technology sectors are essential to developing standardized protocols, scalable cloud solutions, and sophisticated algorithms that effectively tackle the challenges associated with data aggregation and fusion in cloud-connected patient monitoring systems [11]. * Mobility and dynamic topology: Mobility and dynamic topologies pose significant challenges in biosensors integrated with multi-hop IoT systems. In the healthcare context, patients move between different locations, leading to dynamic changes in the network topology. Managing the seamless handover of data transmission from one sensor to another becomes critical to ensuring continuous monitoring without disruptions. The dynamic nature of patient movement introduces challenges related to network reconfiguration. A multi-hop IoT system must adapt to changes in the network topology caused by the mobility of patients or devices. This requires robust protocols for efficient routing, addressing, and re-establishing connections to maintain data flow. Additionally, the mobility of biosensors and patients can result in unpredictable variations in signal strength and quality. Ensuring reliable communication between biosensors and the IoT gateway, even in the presence of mobility-induced signal fluctuations, is a key challenge. Effective mechanisms for signal compensation and error correction have become essential to maintaining the integrity and accuracy of health data [127]. A dynamic topology introduces complexities in managing network resources, including bandwidth allocation and energy consumption [128]. Optimizing these resources to accommodate a changing topology while minimizing disruptions and energy consumption is a crucial challenge. Collaborative research is vital to developing adaptive algorithms, communications protocols, and energy-efficient solutions that address the challenges associated with mobility and dynamic topologies in cloud-connected patient monitoring systems. * QoS-based performance, costs, and resource constraints: Addressing quality-of-service challenges in biosensors integrated with multi-hop IoT systems for patient health monitoring involves navigating complex considerations related to performance, cost, and resource constraints. Ensuring optimal performance is a multifaceted challenge. The diverse nature of health data, including real-time vital signs and sensor readings, demands a balance between timely data transmission and acceptable accuracy. Striking this balance is critical for providing healthcare professionals with reliable and up-to-date information for effective decision making. Cost constraints pose challenges in implementing and maintaining robust QoS in the H-IoT. Balancing the need for advanced technologies with budgetary constraints requires innovative solutions. Cost-effective sensor deployments, energy-efficient communications protocols, and scalable cloud resources are essential components in addressing this challenge [129]. Resource constraints encompass various aspects, including bandwidth limitations, storage capacities, and computational capabilities. Designing QoS-aware algorithms and protocols that optimize resource utilization while meeting the demands of patient monitoring applications is a complex task.