Energy Distribution Optimization in Heterogeneous Networks with Min-Max and Local Constraints as Support of Ambient...

Area of Science:

• Computer Science
• Electrical Engineering
• Ubiquitous Computing

Background:

• Ambient intelligence (AmI) systems enhance user comfort and efficiency by creating adaptive environments.
• Internet of Things (IoT) frameworks, utilizing sensors and actuators, are crucial for AmI system operation.
• Robust wireless networks are essential for integrating diverse devices in AmI/IoT ecosystems, but face challenges in protocol compatibility, power, and computation.

Purpose of the Study:

• To design a heterogeneous wireless network for AmI systems within the IoT ecosystem using a novel multi-interface network approach.
• To optimize energy efficiency for battery-powered devices by selecting appropriate communication protocols (e.g., Wi-Fi, Bluetooth, 5G).
• To improve overall network performance and extend operational lifespan by balancing power consumption and efficiency.

Main Methods:

• Development of a new model within multi-interface networks specifically for reducing battery consumption and maximizing AmI system performance.
• Analysis of the computational complexity associated with the proposed model.
• Proposal of two solution algorithms based on fixed-parameter tractability theory for specific network classes.

Main Results:

• A novel model for multi-interface networks that effectively reduces battery consumption in AmI/IoT systems.
• Demonstrated improvement in network performance through optimized interface selection.
• Identification of computational complexity and development of tractable algorithms for practical implementation.

Conclusions:

• The proposed multi-interface network approach offers a viable solution for enhancing AmI systems within the IoT.
• Optimizing communication interface selection is critical for energy efficiency and network longevity.
• The developed algorithms provide efficient methods for deploying such networks in real-world scenarios.