Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV are rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ( $$\mathbf {P}_{\mathbf {T}}$$P T ), resource allocation at UAV ( $$\mathbf {P}_{\mathbf {R}}$$P R ) and offloading decisions at IoT devices ( $$\mathbf {P}_{\mathbf {O}}$$P O ) and then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

Mobile Edge Computing (MEC) is a promising architecture to reduce the energy consumption of mobile devices and provide satisfactory quality-of-service to time-sensitive services. How to jointly optimize task offloading and resource allocation to minimize the energy consumption subject to the latency requirement remains an open problem, which motivates this paper. When the latency constraint is taken into account, the optimization variables, including offloading ratio, transmission power, and subcarrier and computing resource allocation, are strongly coupled. To address this issue, we first decompose the original problem into three subproblems named as offloading ratio selection, transmission power optimization, and subcarrier and computing resource allocation. Then, we propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution of offloading ratios, employ the equivalent parametric convex programming to obtain the optimal power allocation policy, and deal with subcarrier and computing resource allocation by the primal-dual method. Simulation results demonstrate that the proposed algorithm can save 20%–40% energy compared with the reference schemes, and can converge to local optimal solutions.

UAV-assisted MEC revolutionizes edge computing by deploying UAVs for real-time data processing in areas lacking infrastructure, supporting a wide range of applications from emergency responses to smart cities. Unlike edge servers, UAVs face substantial computational constraints, necessitating a comprehensive strategy that integrates UAV trajectory with task offloading, caching, and migration. Existing studies often overlook the synergy among these strategies, impacting their overall effectiveness. Furthermore, the focus on content pre-caching overlooks task caching's critical role in addressing high computational demands with limited UAV resources. This research aims to jointly optimize UAV trajectories and task management strategies, including offloading, caching, and migration. Utilizing the Lyapunov optimization framework, we break down the complex optimization problem into manageable subproblems: UAV placement, user-UAV association, task offloading, scheduling, and bandwidth allocation, addressed iteratively using the Block Coordinate Descent method. Specifically, the scheduling subproblem is transformed into a non-convex quadratically constrained quadratic programming problem, managed effectively through semidefinite relaxation and a probabilistic mapping approach. Our simulations show that this integrated approach significantly boosts system throughput and reduces execution times compared to conventional methods. This study enhances the understanding of the interplay between UAV trajectory planning and task management, offering vital theoretical insights for advancing UAV-assisted MEC systems.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged to provide data processing and caching in the infrastructure-less areas. However, the limited battery capacity of UAV constrains its endurance time, and makes energy efficiency one of the top priorities in implementing UAV-enabled MEC architecture. In this backdrop, we aim to minimize the UAV’s energy consumption by jointly optimizing its trajectory and resource allocation, and task decision and bits scheduling of users considering fairness. The problem is formulated as a mix-integer nonlinear programming problem with strongly coupled variants, and further transformed into three more tractable subproblems: 1) trajectory optimization $\mathbf {P_{T}}$ ; 2) task decision and bits scheduling $\mathbf {P_{S}}$ ; and 3) resource allocation $\mathbf {P_{R}}$ . Then, we propose an iterative algorithm to deal with them in a sequence, and further design a penalty method-based algorithm to reduce computation complexity when the branch-and-bound (B&B) algorithm incurs a high complexity to solve $\mathbf {P_{S}}$ . Simulation results demonstrate that our proposed algorithm can efficiently reduce the energy consumption of UAV, and help save 17.7% – 54.6% and 78.9% – 91.9% energy compared with Equal Resource Allocation and Random Resource Allocation. Moreover, it reduces more than 88% running time and achieves relatively satisfactory performance compared with B&B.

The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.