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

To address the issue of delay-sensitive data collection in the wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) offer a promising solution due to their flexibility and maneuverability. We investigate the UAVs-assisted WSN with delaysensitive data, where sensor nodes (SNs) are distributed in monitoring areas (MAs) to sense the environment and generate data, UAVs 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 UAVs, we minimize the total energy consumption of UAVs by jointly optimizing the grouping, transmit power, and bandwidth of SNs, the number, associated collection points (CPs), and flight trajectories of UAVs 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 UAVs subproblem. To tackle the first subproblem, we propose a hybrid FDMA and NOMA (HFN) protocol that incorporates the optimal grouping of SNs scheme, derive the optimal transmit power of SNs, and propose the low-complexity heap-based bandwidth optimization algorithm. To tackle the second subproblem, we propose a clustering-based trajectories and number of UAVs optimization (CTNO) algorithm that incorporates the low-complexity improved partitioning around medoids (IPAM) 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 UAVs.