Adaptive Self-Healing Urban IoT Network Management Using Hybrid Deep Learning–Machine Learning Models for Real-Time Infrastructure Failure Prediction and Mitigation

Abstract

The development of smart cities has brought along with it the integration of interlinked Internet of Things (IoT) systems for monitoring urban infrastructure facilities, including transport, energy grid, water supply, and security systems. However, problems such as vulnerability of the system, poor communication channels, and ineffective use of resources are prevalent in disrupting the functioning of the facilities. For the purpose of solving these problems, an adaptive self-healing IoT architecture is proposed in this paper with the aim of detecting and correcting infrastructure failure in advance through predictions. This adaptive architecture utilizes both machine learning algorithms, including random forest and SVM, along with deep learning models such as LSTM for pattern recognition and CNN for modeling spatial dependencies. IoT sensors are deployed on urban infrastructures in order to detect any anomalies and balance the flow of traffic, as well as redirect resources. Simulation findings indicate that the proposed model performs better than traditional approaches in terms of the following performance metrics: accuracy is 92%, precision is 93%, recall is 91%, system reliability is 98.4%, and downtime decreased by 41%. Moreover, response efficiency increased by 30%, and resource utilization became 83%. All the above findings clearly prove that the proposed system can respond effectively to changes in the urban environment and provide reliable and efficient urban services at reduced costs.