Efficient Resource Management in IoT Network through ACOGA Algorithm

Internet of things networks often suffer from early node failures and short lifespan due to energy limits. Traditional routing methods are not enough. This work proposes a new hybrid algorithm called ACOGA. It combines Ant Colony Optimization (ACO) and the Greedy Algorithm (GA). ACO finds smart paths while Greedy makes quick decisions. This improves energy use and performance. ACOGA outperforms Hybrid Energy-Efficient (HEE) and Adaptive Lossless Data Compression (ALDC) algorithms. After 500 rounds, only 5% of ACOGA’s nodes are dead, compared to 15% for HEE and 20% for ALDC. The network using ACOGA runs for 1200 rounds before the first nodes fail. HEE lasts 900 rounds and ALDC only 850. ACOGA saves at least 15% more energy by better distributing the load. It also achieves a 98% packet delivery rate. The method works well in mixed IoT networks like Smart Water Management Systems (SWMS). These systems have different power levels and communication ranges. The simulation of proposed model has been done in MATLAB simulator. The results show that that the proposed model outperform then the existing models.