  Edge learning (EL), which uses edge computing as a platform to execute
machine learning algorithms, is able to fully exploit the massive sensing data
generated by Internet of Things (IoT). However, due to the limited transmit
power at IoT devices, collecting the sensing data in EL systems is a
challenging task. To address this challenge, this paper proposes to integrate
unmanned ground vehicle (UGV) with EL. With such a scheme, the UGV could
improve the communication quality by approaching various IoT devices. However,
different devices may transmit different data for different machine learning
jobs and a fundamental question is how to jointly plan the UGV path, the
devices' energy consumption, and the number of samples for different jobs? This
paper further proposes a graph-based path planning model, a network energy
consumption model and a sample size planning model that characterizes F-measure
as a function of the minority class sample size. With these models, the joint
path, energy and sample size planning (JPESP) problem is formulated as a
large-scale mixed integer nonlinear programming (MINLP) problem, which is
nontrivial to solve due to the high-dimensional discontinuous variables related
to UGV movement. To this end, it is proved that each IoT device should be
served only once along the path, thus the problem dimension is significantly
reduced. Furthermore, to handle the discontinuous variables, a tabu search (TS)
based algorithm is derived, which converges in expectation to the optimal
solution to the JPESP problem. Simulation results under different task
scenarios show that our optimization schemes outperform the fixed EL and the
full path EL schemes.
