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Edge learning (EL), which uses edge computing as a platform to executemachine learning algorithms, is able to fully exploit the massive sensing datagenerated by Internet of Things (IoT). However, due to the limited transmitpower at IoT devices, collecting the sensing data in EL systems is achallenging task. To address this challenge, this paper proposes to integrateunmanned ground vehicle (UGV) with EL. With such a scheme, the UGV couldimprove the communication quality by approaching various IoT devices. However,different devices may transmit different data for different machine learningjobs and a fundamental question is how to jointly plan the UGV path, thedevices' energy consumption, and the number of samples for different jobs? Thispaper further proposes a graph-based path planning model, a network energyconsumption model and a sample size planning model that characterizes F-measureas a function of the minority class sample size. With these models, the jointpath, energy and sample size planning (JPESP) problem is formulated as alarge-scale mixed integer nonlinear programming (MINLP) problem, which isnontrivial to solve due to the high-dimensional discontinuous variables relatedto UGV movement. To this end, it is proved that each IoT device should beserved only once along the path, thus the problem dimension is significantlyreduced. Furthermore, to handle the discontinuous variables, a tabu search (TS)based algorithm is derived, which converges in expectation to the optimalsolution to the JPESP problem. Simulation results under different taskscenarios show that our optimization schemes outperform the fixed EL and thefull path EL schemes.

Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning