Strategic Approaches to Efficient Data Aggregation and Malicious Node Detection in Internet of Things-Enabled Healthcare Systems

Internet of Things (IoT) technology adoption in the healthcare sector has accelerated, transforming patient management and handling in the following ways. However, inherent threats in IoT systems, particularly in their interaction with healthcare data, can pose challenges to data protection, data integrity, and energy consumption. This chapter aims to offer insights into the specific staking strategies that facilitate optimal data gathering and the identification of adversarial nodes in IoT-medical applications. This is preceded by a review of the architecture and potential of a range of IoT healthcare applications, with specific focus on the need for effective and scalable data aggregation to minimize the overhead costs and enhance the performance of the otherwise complex implementations. The chapter also defines other security threats that include compromised nodes, packet modification, etc., and analyzes a new detection paradigm that includes trust management, machine learning (ML), and state-of-the-art blockchain. The cases and real-world deployment of cryptographic schemes in this chapter revealed that lightweight encryption approaches, low-energy consumption protocols, and artificial intelligence (AI)-based anomaly detection systems were key solutions to the security and energy utility dilemma. Future directions involve combining edge computing, blockchain, and adaptive algorithms to create robust, large-scale, and reliable IoT healthcare systems.