Energy Efficient Routing Frame Work for Cloud based IoT Networks using AI
Anusha Kondam, K. Suganyadevi · 2024
The explosion of Internet of Things (IoT) technology has led to an unprecedented surge in the number of devices connected. As a result, it has significantly increased energy consumption and system operation costs of the cloud environment in IoT networks. Optimal routing is critical to reduce energy consumption and improve the play of these networks. This paper proposes a novel AI-based energy-efficient routing framework for cloud-assisted IoT networks. The framework uses AI methods to determine which path the data traffic can move more efficiently, depending on how congested the network is at that moment. It includes the vs, bandwidth and energy for routing decision_TypeInfo. In addition, energy-efficient routing protocols are also integrated with the framework as part of optimization measures to conserve energy. To alleviate network congestion and improve overall efficiency, our framework ensures innovative management of resources among connected IoT devices by efficiently load balancing. It is equipped to detect & automatically rectify faulty or jammed network nodes for smooth data transmission. Experimental results demonstrate that the proposed framework significantly reduced energy consumption, delay and packet loss compared to conventional routing approaches. It helps solve the energy efficiency problem in cloud-based IoT networks and provides a scalable, immune approach that uses AI techniques. Ultimately, our framework could lead to significant performance and sustainability gains for cloud-based IoT networks.