Open Source AI Project


This project proposes an innovative Quantum-Informed Ant Colony Optimization (ACO) algorithm tailored for the Industrial Internet of Things (IIoT).


The GitHub project introduces a cutting-edge algorithm called Quantum-Informed Ant Colony Optimization (ACO), specifically designed for use in the Industrial Internet of Things (IIoT). The primary goal of this algorithm is to enhance energy efficiency in routing processes, which is a critical concern in IIoT networks.

To achieve this, the algorithm incorporates a unique encoding scheme for selecting cluster heads within the network. Cluster heads are crucial components in network architecture as they are responsible for aggregating and forwarding data from other nodes, thereby reducing overall energy consumption.

Additionally, the algorithm uses a novel approach to derive information heuristics, which are essential for guiding the ants (simulated agents) in the ACO algorithm to find optimal paths for data transmission. This helps in further improving energy efficiency and network performance.

The effectiveness of this Quantum-Informed ACO algorithm has been validated through extensive testing across various network scenarios. The results demonstrate its capability to optimize key network performance metrics, such as the remaining energy of nodes, the overall lifespan of the network, and the real-time count of IoT nodes in operation. These improvements are vital for the sustainable and efficient functioning of IIoT systems.

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