<p>Digitalization is the process by which the efficiency, benefits, and availability of healthcare services are improved through the adoption of various technologies and data. Routing Protocol for Low-Power and Lossy Networks (RPL) protocol is well-known for its use in Low-power and Lossy Networks (LLNs). However, it encounters various obstacles such as scalability challenges, energy limitations, and vulnerabilities to breach, including, but not limited to Sybil and rank attacks. Existing solutions have often been inadequate in providing a balance between the accuracy of detection and resource overhead, creating a gap for more effective trust-based systems that are robust and more efficient. Malicious nodes could masquerade as legitimate medical devices to manipulate network rankings involving patients’ information, diagnosis, and emergency response, which may be fatal if delayed. This paper presents a decentralized, trust-based framework using fuzzy logic to detect and isolate Sybil and rank attacks in smart healthcare cyber-physical systems (CPS). In order to improve the scalability, fault tolerance, and energy efficiency in a smart healthcare environment, the proposed FLBT-RPL framework incorporates fog-based trust monitoring and assessment. It reduces the risk of a single point of failure, node energy drainage, and processing overheads. The FLBT-RPL is tested for its effectiveness and performance. It outperforms the state-of-the-art mechanisms, THC-RPL and LETM-IoT. The proposed FLBT-RPL reduces energy consumption by 20% to optimize resource utilization and sustainability compared to THC-RPL and consumes 26.6% less energy than LETM-IoT. It increases residual energy by 15%, thus extending network node lifespan. It also reduces end-to-end delay by 12%, hence improving data transmission efficiency, and detects 10% more attacks for improved system security. It cuts down attack detection time by 15% and message overhead by 25% for quick threat response and efficient communication.</p>