  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.

Distributed, Parallel, and Cluster Computing;Machine Learning