Energy Consumption Minimization for Delay-Sensitive Data Collection in AAV-Assisted WSN

Abstract

To address the issue of delay-sensitive data collection in wireless sensor networks (WSNs), autonomous aerial vehicles (AAVs) offer a promising solution due to their flexibility and maneuverability. We investigate the AAV-assisted WSN with delay-sensitive data, where sensor nodes (SNs) are distributed in monitoring areas (MAs) to sense the environment and generate data, AAVs are dispatched to collect the generated data from SNs and deliver it to a data center within a predetermined delay. Constrained by the limited onboard energy of AAVs, we minimize the total energy consumption of AAVs by jointly optimizing the grouping, transmit power, and bandwidth of SNs, the number, associated collection points (CPs), and flight trajectories of AAVs subject to the predetermined delay constraint. As the formulated minimization problem is NP-hard, we decompose it into two subproblems, i.e., the grouping, transmit power, and bandwidth of SNs subproblem, and the number, associated CPs, and flight trajectories of AAVs subproblem. To tackle the first subproblem, we propose a hybrid FDMA and NOMA (HFN) protocol that incorporates the optimal grouping of SNs scheme, derives the optimal transmit power of SNs, and proposes the low-complexity heap-based bandwidth optimization algorithm. To tackle the second subproblem, we propose a clustering-based trajectory and number of AAVs optimization (CTNO) algorithm that incorporates the low-complexity improved partitioning around medoids (IPAMs) algorithm and the high-efficiency improved tabu search (ITS) algorithm. Numerical results show the superior performance of the HFN protocol and CTNO algorithm in terms of the energy consumption of AAVs.