Integrating Machine Learning-IoT Technologies Integration for Building Sustainable Digital Ecosystems
Revathi S A, Afroze Ansari, S. Jacophine Susmi, R. Madhavi, M. A. Gunavathie, M. Sudhakar · Advances in computational intelligence and robotics book series · 2024
The integration of ML, IoT, and NSA promotes significant opportunities for promoting sustainability in various industries. Actionable insights, while IoT devices collect data for real-time monitoring and control of environmental parameters, have been provided by ML algorithms that analyze vast datasets. IoT deployments improve resource efficiency and resilience, while the NSA dynamically allocates network resources based on application requirements. These architectures prioritize traffic, optimize bandwidth, and ensure QoS to facilitate IoT and ML applications due to minimizing energy consumption and carbon emissions. However, challenges (data security, interoperability, and ethical) have been considered to persist, necessitating holistic sustainability approaches. Network slicing architectures provide a flexible network architecture for the efficient coexistence of diverse services and applications on a shared infrastructure.