Fairness-aware data offloading of IoT applications enabled by heterogeneous UAVs

Abstract

With the widespread application of IoT devices, some new scenarios with insufficient coverage of communication infrastructure have arisen, such as wildlife habit monitoring. For these IoT applications, how to efficiently backhaul data collected by IoT devices has become a key technology. As a new data collecting mode for environments lacking communication infrastructure, UAV-enabled data offloading has attracted increasing research interest. Nevertheless, the fairness of data offloading is rarely studied, which directly influences the quality of service. Therefore, we investigate the fairness-aware task scheduling for data offloading enabled by heterogeneous UAVs. To reduce the uncertainty of user status information caused by poor communication, a service time related data arrival rate estimation model is proposed. The fairness-aware heterogeneous UAV-enabled data offloading model is proposed to ensure that all users share system resources fairly. Then, we creatively propose a dynamic incentive-based coordinate descent optimization framework-DISCO, which can progressively improve resource utilization while maximizing service fairness. Besides, we theoretically analyze and experimentally demonstrate its convergence and low time complexity. The performance of DISCO is verified by a series of simulation experiments. Compared with four baseline algorithms, DISCO shows distinct advantages in terms of service fairness, energy efficiency, adaptability and data throughput.