Security Enhancement Scheduling Model for IoT-Based Smart Cities Through Machine Learning Method

Abstract

The Internet of Things (IoT) is rapidly evolving, and this has supported the adoption of a new computing paradigm that moves processing power to the network's edge. The job must be assigned to the computer nodes, where their associated data is available, to minimize overheads generated by data transmissions in the network, because the edge nodes have limited processing power and are vulnerable to security. Hence, the paper introduces a novel security-enhanced scheduling model for IoT-based smart cities utilizing machine learning techniques. Initially, nodes are initialized using the LHK-Means algorithm. Subsequently, tasks, representing requests from multiple users to access IoT data, are scheduled. Anomaly detection tasks are then identified using an L²-Norm-based fuzzy model. Normal tasks are processed by the BN-CNN model, which schedules data collection tasks through the initialized IoT device nodes. Comparative analysis with existing models illustrates the effectiveness of the proposed approach in terms of accuracy, precision, recall, sensitivity, specificity, and f-measure.