Optimised Value and Quantity Balancing for Data Collection in Resource-Constrained UAV-Aided IoT

Abstract

Unmanned aerial vehicles (UAVs) play a pivotal role in disaster response by enabling rapid information gathering from widely distributed sensor nodes. In resource-constrained scenarios, limitations in the number of UAVs and their onboard energy pose significant challenges in swiftly collecting and analysing high-priority data. In this paper, we tackle the crucial challenge of efficient multi-UAV-aided data collection in resource-constrained Internet of Things (IoT) environments where UAVs function as mobile data collectors. Given the inherent heterogeneity in data value and spatial distribution of IoT devices, a sophisticated approach is necessary to maximise data collection value while optimising energy consumption and coverage. This paper introduces a novel UAV-assisted IoT data collection model tailored for resource-constrained environments. By constructing a virtual backbone network and employing the NSGA-II algorithm for multi-objective optimisation, our model effectively routes data and schedules UAVs to maximise both data value and coverage. Extensive simulations demonstrate that our proposed algorithm achieves a superior balance between value and quantity in resource-constrained scenarios compared to existing data collection schemes.