LLHR：面向资源受限无人机集群的低时延高可靠CNN分布式推理
LLHR: Low Latency and High Reliability CNN Distributed Inference for Resource-Constrained UAV Swarms
AI总结：
本文针对资源受限无人机集群的实时推理需求，提出LLHR分布式推理框架，通过联合优化发射功率、无人机位置和CNN层放置，在保证传输可靠性的同时降低端到端时延。
摘要：
近年来，无人机（UAV）在监视、搜索救援、环境监测等许多关键应用中表现出色。在这些应用中，无人机通常采集图像及其他传感数据，然后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、能量有限以及严格的端到端时延要求，这种方法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划分为子任务，并根据可用资源在无人机集群中进行分布式处理。此外，这些任务会产生中间结果，当集群移动以覆盖目标区域时，这些中间结果需要被可靠传输。我们的系统模型处理实时请求，目标是寻找最优发射功率，以保证更高的可靠性和低时延。我们将低时延高可靠（LLHR）分布式推理建模为一个优化问题，由于问题复杂度较高，将其划分为三个子问题。第一个子问题求解已连接无人机在保证传输可靠性前提下的最优发射功率；第二个子问题旨在确定无人机在网格中的最优位置；最后一个子问题求解CNN层在可用无人机上的最优放置。我们进行了大量仿真，并将所提方法与两个基线模型进行比较，结果表明我们的模型优于对比模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
