Recently, Unmanned Aerial Vehicles (UAVs) have shown impressive performance in many critical applications, such as surveillance, search and rescue operations, environmental monitoring, etc. In many of these applications, the UAVs capture images as well as other sensory data and then send the data processing requests to remote servers. Nevertheless, this approach is not always practical in real-time-based applications due to unstable connections, limited bandwidth, limited energy, and strict end-to-end latency. One promising solution is to divide the inference requests into subtasks that can be distributed among UAVs in a swarm based on the available resources. Moreover, these tasks create intermediate results that need to be transmitted reliably as the swarm moves to cover the area. Our system model deals with real-time requests, aiming to find the optimal transmission power that guarantees higher reliability and low latency. We formulate the Low Latency and High-Reliability (LLHR) distributed inference as an optimization problem, and due to the complexity of the problem, we divide it into three subproblems. In the first subproblem, we find the optimal transmit power of the connected UAVs with guaranteed transmission reliability. The second subproblem aims to find the optimal positions of the UAVs in the grid, while the last subproblem finds the optimal placement of the CNN layers in the available UAVs. We conduct extensive simulations and compare our work to two baseline models demonstrating that our model outperforms the competing models.

翻译：近年来，无人机在监控、搜索救援、环境监测等诸多关键应用中展现出卓越性能。在这些应用中，无人机通常采集图像及其他传感数据，并将数据处理请求发送至远程服务器。然而，由于连接不稳定、带宽有限、能量受限以及严格的端到端延迟要求，这种方案在实时应用中并不总是可行。一种有前景的解决方案是将推理请求分解为子任务，根据集群中无人机的可用资源进行分布式处理。此外，这些任务会产生中间结果，需要在集群移动覆盖区域时可靠传输。我们的系统模型处理实时请求，旨在寻找能同时保证高可靠性和低延迟的最优传输功率。我们将低延迟高可靠性分布式推理问题形式化为一个优化问题，由于问题复杂性，我们将其分解为三个子问题：第一个子问题在保证传输可靠性的前提下，确定已连接无人机的最优传输功率；第二个子问题旨在找到无人机在网格中的最优位置；最后一个子问题则确定CNN层在可用无人机中的最优部署方案。我们进行了大量仿真实验，并将我们的工作与两个基线模型进行对比，结果表明我们的模型优于对比模型。