The field of age-of-information (AoI) optimization is moving towards more complex and realistic scenarios, incorporating factors such as computational demands, energy consumption, and multi-source systems. Researchers are developing innovative methods to minimize AoI in various contexts, including CPU scheduling, piggyback networks, and multi-source systems with wake-up control and packet management. A key direction is the integration of AoI optimization with other performance metrics, such as energy consumption and reliability, to achieve a better trade-off between these competing objectives. Notable papers in this area include:

• A paper that formulates the CPU scheduling problem as a constrained semi-Markov decision process to minimize the long-term average age of information, achieving significant reductions in AoI and energy consumption.
• A study that proposes approximation approaches to design a patrolling route for data collection drones, minimizing the maximum age-of-information across the system.
• A paper that analyzes the age-energy trade-off in multi-source systems with wake-up control and packet management, identifying optimal sleep policies and packet preemption strategies.
• A work that proposes adaptive strategies for reliability and deadline-ensured workflow scheduling in cloud environments, minimizing energy consumption while ensuring strict reliability and deadline constraints.