ENADL: Towards Performance Improvement of IoT Networks Using Deep Learning-Based Node Fault Prediction

The Internet of Things (IoT) has grown explosively with wireless technology integration. Several IoT applications require high data throughput, low data transmission latency, and high data gathering reliability. Since, the IoT network (IoTN) is generally dynamic and utilizes a multi-hop data transmission scheme for such applications, the throughput, latency, and network lifetime tend to degrade as the hops increase. Moreover, IoT devices (IoD) are low-cost, less computationally capable, and battery-limited, further impacting performance. A faulty IoD worsens network lifetime and throughput. Predicting faulty nodes and re-routing data can significantly enhance performance. This work proposes a node fault prediction framework to enhance data routing in dynamic IoTN, maximizing throughput and lifetime. The network is represented as a graph in which the IoD are the nodes. Then a novel deep learning model is proposed utilizing various node and edge features to predict the faulty IoDs. Particularly, the proposed edge and node features-accumulation deep learning (ENADL) method exploits features, such as Euclidean distance between nodes, residual energy level of nodes, and type and number of messages passed between edges to predict the forthcoming faulty IoD. Thereafter, data routing is performed over the updated network topology. Furthermore, to improve the network lifetime, the node's degree and betweenness centrality measures-based energy allocation method is also proposed. Finally, numerical results on simulated and real-field testbeds demonstrate the ENADL method.s effectiveness in predicting faulty nodes and re-routing data packets. This results in maximized network throughput and lifetime as compared to several existing methods.