Energy-Aware Smart Routing for Biomedical Waste Management via Secure Edge Intelligence in 6G-Enabled CPS

With the advancement of biomedical technologies, the effective and timely management of biomedical waste (BMW) has become increasingly critical to ensure public health and environmental sustainability. Among the key challenges in this domain is the optimization of collection and transportation routes under strict safety, time, and cost constraints. This research focuses on the application of secure and energy -aware edge intelligence to solve the route optimization problem for BMW transportation, modeled as a variant of the classical Traveling Salesman Problem (TSP), within 6G-enabled Cyber-Physical Systems (CPS). We propose a comparative analysis of smart routing algorithms namely Dijkstra's algorithm, Ant Colony Optimization (ACO), and Cohort Intelligence (CI) customized for biomedical waste scenarios that include constraints such as traffic congestion, vehicle reliability, risk exposure, and energy consumption. The study evaluates these algorithms based on key performance metrics including distance travel, execution time, energy efficiency, and delay. Our contributions are twofold: (1) adaptation and integration of TSP-based algorithms with edge-intelligent decision-making for real-time routing, and (2) comparative performance assessment under realistic risk-aware and sustainability-driven constraints. The results highlight the potential of energy-aware, trustworthy edge intelligence in enhancing the safety, efficiency, and environmental compliance of biomedical waste management in future 6G-connected smart cities.