# LLHR: Low Latency and High Reliability CNN Distributed Inference for Resource-Constrained UAV Swarms

arXiv: https://arxiv.org/abs/2305.15858 · HTML: https://mlsanity.com/paper/2305.15858/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 25 May 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distributed Deep Neural Networks over the Cloud, the Edge and End Devices](https://mlsanity.com/paper/1709.01921/) (2017)
- [A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence](https://mlsanity.com/paper/2010.09317/) (2020)
- [Offloading Optimization in Edge Computing for Deep Learning Enabled Target Tracking by Internet-of-UAVs](https://mlsanity.com/paper/2008.08001/) (2020)
- [Distributed Deep Convolutional Neural Networks for the Internet-of-Things](https://mlsanity.com/paper/1908.01656/) (2019)
- [Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks](https://mlsanity.com/paper/2107.04648/) (2021)

## Built on by (most cited first, 3 of 3)

- [Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms](https://mlsanity.com/paper/2401.11118/) (2024)
- [Multi-UAV Multi-RIS QoS-Aware Aerial Communication Systems using DRL and PSO](https://mlsanity.com/paper/2406.16934/) (2024)
- [Think, Act, Learn: A Framework for Autonomous Robotic Agents using Closed-Loop Large Language Models](https://mlsanity.com/paper/2507.19854/) (2025)

## Abstract (arXiv)

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.