The swift evolution of Internet of Things (IoT) technology has promoted the innovation in many fields such as smart city, smart transportation, etc. Accurate positioning technology is one of the key links to achieve the IoT intelligent services, in the actual deployment, due to the complexity and diversity of the environment, the selection of communication links and data allocation between IoT devices face many challenges, to enhance the accuracy of IoT localization systems, this study introduces an optimized ant colony optimization approach for optimising link selection and resource allocation in IoT, this improves the traditional ant colony algorithm’s overall search capacity by introducing new heuristic pheromone updating rules and dynamic adjustment strategies, thus improving the positioning accuracy and reducing the network latency, and further reduces the energy consumption among nodes by optimising the algorithm for energy management. The experimental data indicates that the new algorithm surpasses the traditional one across various scenarios, especially in the large-scale IoT environment, its advantages in positioning accuracy, communication efficiency and energy consumption control are more significant, and this research result provides new ideas and technical support for the design of future IoT positioning systems.