## 6 Challenges
* Network scalability: Network scalability in biosensors integrated with multi-hop IoT systems for patient health monitoring introduces several challenges. The surge in health data from an increasing number of biosensors poses challenges for managing and processing large volumes efficiently. Scalability issues may lead to delays and inefficiencies in handling the growing data load. Scaling the network infrastructure requires careful planning to accommodate additional biosensors, involving the expansion of server capacity, storage resources, and computational capabilities [125]. Seamless integration of new nodes into the existing network presents challenges, particularly with diverse biosensor types. Maintaining reliable communications becomes challenging with a growing number of biosensors, increasing the risk of signal interference, packet collisions, and communication failures. Consistent and dependable communications pathways are crucial for effective patient monitoring. Scalability introduces security challenges, providing more entry points for potential cyber threats. Robust security measures that scale with network size are essential to safeguarding sensitive health data from unauthorized access. Addressing these challenges requires a comprehensive approach, incorporating advancements in network architectures, data management, and security protocols to ensure the scalability and sustainability of biosensor-integrated multi-hop IoT systems for patient health monitoring through cloud connectivity. * Security and privacy: Security and privacy pose critical challenges in biosensors integrated with multi-hop IoT systems. Ensuring the confidentiality and integrity of health data transmitted across the network is paramount. The distributed nature of multi-hop IoT systems introduces vulnerabilities, and unauthorized access to sensitive patient information is a significant concern. Implementing robust encryption mechanisms becomes crucial in order to secure data during transmission and storage in the cloud. Authentication and access-control measures must be stringent to prevent unauthorized users from accessing health data [126]. Privacy concerns arise owing to the nature of health information; ensuring compliance with regulations such as HIPAA is essential. Unauthorized disclosure of patient data can lead to severe consequences, making data encryption and access controls imperative. Balancing the need for data sharing among healthcare professionals with patient privacy rights poses a complex challenge. Consent mechanisms and transparent communication regarding data usage become crucial components of addressing privacy concerns [43]. The dynamic nature of IoT networks, with constant data exchange between biosensors and the cloud, requires continuous monitoring and updates to security protocols. Potential threats, such as malware or denial-of-service attacks, demand proactive measures to detect, prevent, and mitigate security breaches. Collaborative efforts between technology developers, healthcare providers, and policymakers are necessary to establish comprehensive security and privacy frameworks that address the unique challenges posed by biosensor-integrated multi-hop IoT systems in cloud-connected patient monitoring. * Data aggregation and fusion: Data aggregation and fusion in biosensors present several challenges. The complexity of aggregating heterogeneous data from diverse biosensors requires standardized formats and protocols to ensure interoperability. Integrating data seamlessly while maintaining accuracy and reliability is a fundamental challenge given the variations in sensor types, data rates, and formats [88]. Ensuring the timeliness of data aggregation is crucial for real-time patient monitoring. Delays in data fusion could impact the ability to respond promptly to critical health events. Coordinating the simultaneous transmission of data from multiple biosensors in a multi-hop IoT network introduces synchronization challenges.