An Intelligent Robust Networking Mechanism for the Internet of Things

In smart cities, the Internet of Things (IoT) consists of many low-power smart nodes. Its robustness is essential for protection of communication in data science against node failures caused by energy shortage or cyber-attacks. Scale-free networking topology, widely applied in IoT, is effectively resilient to random attacks but is vulnerable to malicious ones in which high-degree nodes are made to fail. The prohibitively high computational cost of existing robustness optimization algorithms is an obstacle to efficient topology self-optimization. To solve this problem, a novel robust networking model based on artificial intelligence is proposed to improve IoT topology robustness to protect its communication. Using the Back-Propagation neural network learning algorithm, the model extracts topology features from a dataset by supervised training. The experimental results show that the model achieves better prediction accuracy, thereby optimizing the topology with minimal computation overhead.