Securing the Edge of Things for the Internet of Medical Things: A Survey of Threats, Privacy, and Trust

Abstract

The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling continuous monitoring, remote diagnosis, and data-driven decision-making. However, conventional cloud-centric IoMT architectures face significant limitations, including high latency, bandwidth overhead, privacy leakage, and increased exposure to cyberattacks. To address these issues, healthcare systems are increasingly adopting the Edge of Things (EoT) paradigm, which enables localized data processing, real-time intelligence, and improved quality of service. Despite these benefits, the decentralization and heterogeneity of EoT environments introduce new security, privacy, and trust challenges, particularly under stringent resource constraints. This survey provides a comprehensive analysis of the evolution from IoMT to EoT, with a particular emphasis on security threats across the device, network, edge, and application layers, and systematically reviews key attack vectors, including data tampering, identity spoofing, insider threats, adversarial learning, and distributed denial-of-service attacks. The paper further examines state-of-the-art countermeasures, such as lightweight cryptographic mechanisms, blockchain-based trust and integrity management, federated learning (FL) for privacy-preserving intelligence, and immune-inspired intrusion detection at the edge. Finally, open research issues and future directions are identified, including scalable trust models, resilient FL, energy-aware security, standardized datasets, and regulatory-compliant decentralized architectures. This survey aims to guide the development of secure, intelligent, and resilient edge-enabled healthcare systems.