Edge-Learning-Based Sensor Allocation Strategy in Internet of Things System

In the edge learning process of B5G Internet of Things (IoT) systems, the allocation strategy of edge sensors will directly affect the learning results of the system. This article proposes multiple methods to allocate edge devices that can learn and predict the usage of spectrum data, aiming to improve the efficiency and fairness of edge learning. We design an efficient edge device allocation strategy to enhance the edge learning efficiency and propose a metric called ineffective transmission parameter (ITP) to evaluate its performance. To solve the optimization problem, mathematical analysis is performed and closed-form expressions are obtained. The scenarios considered include: devices with different learning performance and the same learning performance on the same band. We propose three edge device allocation methods: 1) iterative hierarchical Hungarian allocation; 2) bow allocation; and 3) category-divided allocation to ensure the fairness of edge learning among sub-bands. The fairness of the system is measured by evaluating the lowest learning performance in the sub-band. To adapt to the actual scenario, we enhance fairness by introducing band attribute parameters (considering the priority, anti-interference ability, and congestion level of the main users of the sub-band). Simulation results show that the proposed strategy significantly improves the ITP of edge learning in IoT systems, especially in the case of varying band utilization. The fairness scheme improves the overall edge learning fairness of the system.