Enabling Remote Surgery and Real-Time Patient Monitoring Through Edge-Enhanced Collaborative Computing in 6G Networks

The implementation of 6G networks supports low-latency communications of crucial healthcare applications like remotely controlled surgery and real-time patient monitoring in applications requiring high reliability and rapid reaction to changing network environments, prohibiting a cloud-only architecture. This paper proposes an improved collaborative computing system incorporating adaptive task offloading, edge-cloud coordination and latency-conscious resource allocation as applied to a tactile healthcare service. A model of end-to-end latency is designed, and an orchestration algorithm allocates dynamically the workloads between the edge and cloud layers. Experimental testing based on public healthcare datasets, including PhysioNet ECG and UCI Human Activity Recognition, demonstrates that the proposed framework achieves average end-to-end latency of 6.8 ms under moderate network load when latency-critical tasks are executed at edge nodes while delay-tolerant workloads are offloaded to the cloud, a task success rate of over 99% with up to 100 connected devices and a statistically significant improvement in performance (p < 0.01) based on repeated simulation runs and paired statistical comparison across baseline methods. Such findings confirm the usefulness of collaborative edge computing to make 6G healthcare systems scalable, reliable, and with low latencies.