Timely and effective early warning is essential for proactive intervention to mitigate students’ learning risks. Existing studies on learning early warning primarily predict students’ knowledge mastery based on academic performance. However, they lack the assessment of course development objective achievement. According to the outcome-based education (OBE) concept, the course objective achievement serves as the foundation for comprehensive student assessment spanning knowledge acquisition, learning ability, and learning attitude. Using the course objective achievement as a guide for learning early warning can help to obtain more objective and accurate warning results. This article proposes a novel approach to learning early warning guided by course objective achievement via knowledge state and learning state modeling. This approach constructs knowledge states related to course objectives through a deep knowledge tracing model and derives the learning states comprising learning ability and learning attitude from multidimensional learning behavior data. The achievement state, fusing the knowledge and learning states, is then fed into a transformer model to capture the temporal dynamics of the achievement state and predict the achievement levels for each course objective. Based on these predictions, a four-level warning rule is employed to assess students’ learning risks. Experiments based on two real-world datasets demonstrate the effectiveness and superiority of the proposed approach. The approach provides a new research paradigm and a feasible solution to achieve accurate personalized early learning warning.

The integration of mobile edge computing (MEC) and heterogeneous networks enables network operators to provide task offloading services to a large number of user devices (UDs) for low-latency task processing by equipping macro base stations and densely deployed small base stations with edge servers. Federated deep reinforcement learning allows each UD to collaboratively learn useful knowledge from the interaction with the environment in a privacy-preserving and high-efficiency way and thus has been applied to solve the task offloading problem in recent studies. However, very few of these studies have considered the energy and time costs incurred by the federated learning process. In this article, the goal is to minimize the total UDs’ energy consumption while guaranteeing deadline constraints considering both the task offloading process and the federated learning process in MEC-enabled heterogeneous networks. Toward this end, we propose a federated deep Q-network (DQN) method where each UD optimizes the offloading decision for the offloading process and the participation decision and training volume for the learning process based on its local DQN model. The simulation results demonstrate the proposed method is superior to several existing methods in terms of energy efficiency and Quality of Service (QoS).

User requests in edge computing environments are inherently decentralized and dynamic, posing significant challenges for efficient and adaptive service replica placement. To address this, we formulate the service replica placement problem in an edge-cloud collaborative environment, explicitly incorporating the spatio-temporal distribution of user requests. By capturing spatial and temporal correlations, we predict future request patterns to enable forward-looking replica placement. Given the NP-hard nature of the optimization problem, we design a DRL algorithm that optimizes replica placement decisions based on predictive modeling. To validate our approach, we conduct extensive experiments on real-world datasets across two typical application scenarios―grid-based and graph-based request distributions. Experimental results show our method reduces average response latency by up to 59.6% and boosts service provider profitability by 4.85% compared to reactive and temporal-only baselines. The proposed framework provides a novel and effective solution for proactive service provisioning in edge computing environments.

In recent years, since edge computing has improved the performance of transportation systems, research on edge-computing-enabled transportation systems has received widespread attention. However, most previous studies overlooked that task requests in transportation systems are unevenly distributed in time and space, which easily causes the overloading of edge servers, resulting in high response latency. To this end, we present a novel task offloading scheme based on graph neural network (GNN) and deep reinforcement learning (DRL) in edge-computing-enabled transportation systems (TransEdge). Specifically, we first propose an adaptive node placement algorithm to assign Internet of Things sensors to appropriate edge servers, thereby minimizing transmission latency. Then, an improved DRL scheme based on GNN is designed to capture the spatial features between sensors, aiming to improve the accuracy of task offloading decisions. Finally, we introduce a task forwarding strategy based on the greedy algorithm to achieve collaborative task offloading between different edge servers and overcome the system instability caused by a sudden surge in task requests. We conduct extensive experiments on two real-world traffic data sets. The results show that TransEdge reduces the response latency by at least 3.7% compared to four baselines while achieving a success rate of 99%.

Recently, the Industrial Internet of Things (IIoT) has shown great application value in environmental monitoring. However, it suffers from serious bottlenecks in energy and computing capability. To address them, researchers have made lots of effort. Nevertheless, they neglect either the edge–end collaboration or the impact of task queue backlog, resulting in low system revenue. To this end, we design a queue-aware computation offloading method based on DRL (QDRL). Specifically, we represent the long-term system operation as a multistage stochastic mixed-integer optimization problem (M-SMIP), which is further converted into a deterministic problem using Lyapunov optimization. Given that the resource allocation and computation offloading in this deterministic problem are strongly coupled and difficult to solve, we decompose this problem into two subproblems. Subsequently, a reinforcement learning scheme with actor–critic architecture is designed to solve these subproblems. The Actor module is designed based on a deep learning model and quantization strategy for generating computation offloading actions. The mathematical reasoning and learning-based methods are integrated as the Critic module for achieving resource allocation. Extensive simulation results show that the performance of QDRL surpasses four baselines and approaches the approximate optimal algorithm in terms of average task queue length, normalized real computation rate, and computation time.

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. UAVs can greatly support data collecting and processing for Internet of Things devices (IoTDs) in mobile edge computing (MEC) systems due to their advantages of high environmental flexibility. This paper focuses on the scenario where multiple heterogeneous rotary-wing UAVs complete data collection and processing missions cooperatively. This paper introduces an energy minimization problem for UAV-assisted MEC system which attempts to optimize route planning and task allocation of UAVs. The energy consumption of a UAV includes hovering energy and flight energy depending on its configuration. By jointly choosing optimal UAVs for tasks and routes, we aim to obtain a sub-optimal solution of allocating IoTD tasks to UAVs and UAV flying route design while minimizing energy consumption. The Ant Colony System (ACS) algorithm is employed to obtain a high-quality near-optimal solution to solve this optimization problem. Finally, the simulation results show the effectiveness and efficiency of our proposed solution.