  The Industrial Internet of Things (IIoT) is an emerging paradigm to make
industrial operations more efficient and intelligent by deploying a massive
number of wireless devices to industry scenes. However, due to the limited
computing capability and batteries, the Industrial Internet of Things Devices
(IIoTDs) can't perform the computation-intensive or delay-sensitive tasks well
and provide long-term services in practical. To tackle these challenges, we
present an effective global joint resource allocation scheme for Unmanned
Aerial Vehicle (UAV) service of Pervasive Edge Computing (PEC) in Industrial
Internet of Things (IIoTs) and studied a collaborative UAV server-IIoTDs scheme
in this paper. In our proposed scheme, the IIoTDs can keep high-efficiency
performance even their battery ran out, by deploying the UAV as a PEC server
and a mobile power source to provide task offloading and energy harvesting
opportunities for IIoTDs. In consideration of the offloading position
selection, devices resource allocation and system performance, we aim to
minimize the overall service latency of all IIoTDs consisting of task
computation latency and offloading latency, by joint optimizing the task
offloading decisions, charging resources allocation, connection management, and
UAV computation resources allocation. However, the formulated optimization
problem is a mixed-integer nonlinear programming (MINLP) problem which is
challenging to solve in general. In order to address the problem, we decompose
it into multiple convex sub-problems based on block-coordinate descent (BCD)
method to obtain the optimal solution. Performance evaluation demonstrates that
our scheme outperforms the existing schemes in terms of the overall service
latency of IIoTDs.
