DASHES-Autonomous and Secure Framework for Connected Healthcare with Consumer Electronics

The integration of consumer electronics into connected healthcare requires secure, explainable, and low-latency data management. Existing solutions often fail to scale or adapt under variable network conditions, exposing weaknesses in data integrity, processing, and fault recovery for next-generation systems. This paper presents DASHES, an autonomous framework that secures and optimizes end-to-end data handling while integrating consumer devices into healthcare networks. The framework combines: (i) a quantum-resistant blockchain using lattice-based cryptography for decentralized validation and integrity; (ii) bio-signal quantum encryption to protect physiological data against quantum-capable adversaries; (iii) an adaptive neuro-fuzzy inference system with evolutionary optimization for explainable, real-time decision-making; (iv) a self-healing mesh for fault-tolerant connectivity; and (v) a holographic data transmission pipeline for bandwidth-efficient, low-latency transport. Simulation studies on the MIMIC-III dataset under diverse stressors report an encryption success rate of 98.5%, fault detection accuracy of 98.5%, and mean recovery time of 3.2 s. End-to-end latency spans 12-29 ms across load and failure scenarios, and throughput reaches 1.2 GB/s under ideal conditions (1.0 GB/s during scaling; 0.7 GB/s with node failures).