Maximizing data gathering and energy efficiency in UAV-assisted IoT: A multi-objective optimization approach

Abstract

The data volume and energy consumption are important design aspects of data collection in the Internet of Things (IoT). In this paper, the unmanned aerial vehicles (UAVs)-assisted data gathering (DG) in IoT is investigated, in which multiple rotary-wing UAVs are dispatched to gather data from multiple terrestrial IoT devices within specified coverage range. In order to ensure the freshness of the collected data, we must consider that each UAV should spend as little time as possible to collect data. Moreover, it is assumed that UAVs collect data from the devices only when they are hovering, and the transmission distance and on-board energy of the UAVs are limited. Under these practical hypotheses, we formulate a UAV-assisted data gathering multi-objective optimization problem (UAVDGMOP) to simultaneously maximize the total data volume collected by UAVs, minimize the maximum-time for UAVs collecting data and approaching the optimal positions, and minimize the total energy consumptions of UAVs via jointly optimizing the locations of UAVs, the transmission power of UAVs, and the scheduling of UAV–device pairs. Moreover, the formulated UAVDGMOP consists of the hybrid and complex solutions, which is difficult to be solved by using the conventional methods, and thus we solve it by proposing a hybrid update multi-objective evolutionary algorithm based on decomposition (HUMOEA/D). Simulation results demonstrate that the proposed approach achieves higher data amount and energy efficiency of the network, and it is more suitable for solving the formulated UAVDGMOP comparing to other comparative approaches, including the random, uniform and layering deployment approaches, as well as other multi-objective evolutionary algorithms (MOEAs). Moreover, the proposed HUMOEA/D still achieves the overall excellent performance in more use cases.