Large-scale wind grid integration weakens frequency regulation due to the use of converters, which decouple wind turbine rotor speed from grid frequency. Pertinent studies have primarily focused on frequency regulation through adjusting rotor speed and pitch angle on the mechanical side, or by controlling direct-axis current of the motor, but adjusting rotor current phase is also possible. Thus, based on rotor current phase adjustment, this article proposed a deep reinforcement learning coordinated control strategy for a doubly-fed induction generator (DFIG) wind turbine to coordinate the participation of the mechanical and motor sides in frequency regulation. To establish the relationship between rotor current phase and active power, a signal named driving phase angle (DPA) is constructed and introduced into the dq/abc transform in the rotor-side vector control, to change the rotor current phase angle and, furthermore, the power output. Then, the model for DPA influencing DFIG power output is established, and its static and dynamic characteristics are clarified. Furthermore, combined with deep reinforcement learning, a coordinated control strategy is proposed and solved by the deep deterministic policy gradient algorithm. Finally, the simulation results show that the proposed strategy effectively improves the performance of frequency regulation.

As factories in the Industrial Internet of Things (IIoT) scale up and complexity increases, distributed Large Language Models (LLMs) enabled Digital Twin Network (DTN) is required to enhance real-time mapping capabilities. However, current wired communication processes in distributed LLMs fine-tuning cannot meet the demands of mobile IIoT scenarios. Confronting this thorny problem, we propose a wireless communication strategy for gradient synchronization to enhance the efficiency of distributed LLM-enabled DTN fine-tuning. Specifically, leveraging mobile edge intelligence, we jointly optimize central node closeness centrality and computational capacity, thereby enhancing the efficiency of distributed LLM fine-tuning. Subsequently, we design the Dueling Double Deep Q-Network (D3QN) enabled parameter fine-tuning gradient synchronization (D3QN-REFRESH) algorithm and analyze its complexity. Extensive simulations using real factory data demonstrate the superior performance and generalization capabilities of D3QN-REFRESH.

The Industrial Internet of Things (IIoT), a core driver of Industrial 4.0, is considered as one of the most promising revolutionary technologies propelling the evolution of smart manufacturing towards Specialization, Reinforcement, Distinctiveness, and Innovation. The security and efficiency of smart manufacturing depend on the secure and efficient interaction of massive production data among entities. Yet, as a crucial measure of securing entity interactions, current authentication mechanisms overlook the single-point-of-failure issue and lightweight design. Moreover, interaction efficiency is rarely optimized and enhanced from the perspective of communication-supporting nodes. Paramountly, the assurance and optimization of entity interaction security and efficiency are strongly coupled, which is not considered in existing interaction frameworks. This paper designs a three-layer entity interaction framework based on mobile edge computing (MEC) and blockchain technology. Specifically, the double-layer blockchain and MEC-cluster assisted lightweight authentication (BCLA) mechanism is proposed under the three-layer framework to achieve lightweight entity authentication in a weakly centralized manner. To optimize the entity interaction efficiency from joint authentication and transmission, this paper further proposes an industrial edge server (IES) deployment optimization scheme and the proximity policy optimization based IES deployment (PAID) algorithm. The security features and efficiency of the three-layer framework are demonstrated by carrying out security analysis and performance evaluation, which is based on the Hyperledger Fabric platform.

Autonomous vehicles in industrial parks can provide intelligent, efficient, and environmentally friendly transportation services, making them crucial tools for solving internal transportation issues. Considering the characteristics of industrial park scenarios and limited resources, designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research hotspot. This paper proposes an efficient autonomous driving solution based on path planning, target recognition, and driving decision-making as its core components. Detailed designs for path planning, lane positioning, driving decision-making, and anti-collision algorithms are presented. Performance analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial parks. This solution provides important references for enhancing the performance of autonomous vehicles in these areas.

Vehicular Edge Computing and Networks (VECoNs) have gained popularity for its enhanced Internet of Vehicles (IoV) capabilities. To satisfy the needs of delay-sensitive and computation-intensive in-vehicle applications, VECoNs need to provide low-latency task offloading services. However, existing offloading frameworks generally overlook the spatially and temporally heterogeneous computation task arrival patterns. The former causes overloading and underloading of RSU computational resources and thus hinders further reduction of offloading latency on the macro-scale, while the latter emphasizes the importance of long-term system performance, especially energy constraints, posing challenges to the design of offloading framework and optimization strategies. This paper introduces a novel distributed two-stage task offloading architecture based on Lyapunov and multi-agent deep deterministic policy gradient (MADDPG). On one hand, it jointly optimizes the initial offloading stage within VEC subsystems and the RSU peer offloading stage to minimize offloading delays for each VEC subsystem. On the other hand, it incorporates RSU energy consumption within long-term constraints to formulate the offloading optimization problem. After decoupling the energy coupling between RSU time slots using the Lyapunov algorithm, a Lyapunov and MADDPG-based distributed task offloading (LAMETO) algorithm is presented to solve the optimal problem in a distributed manner. Simulation results show that the proposed framework and algorithm can reduce the system delay, energy consumption, and energy deficit while stabilizing convergence.

The emergence of edge computing (EC) and artificial intelligence (AI) is driving the rapid growth of industrial internet of things (IIoT). However, few works comprehensively consider the impact of resource reallocation and number of reallocation on the system delay in dynamic industrial scenarios with time-varying geographic location characteristics. This paper takes the dynamic resource reallocation problem between the physical layer and edge layer within a time-varying factory scenario into account, proposes a reallocation-decision variable and reduces the computational stress on edge nodes caused by frequent reallocation. An optimization problem with the objective of minimizing the system average delay is established and a proximal policy optimization (PPO) based dynamic resource reallocation (P-DRR) algorithm is proposed for the problem solving. Experimental results show that P-DRR algorithm can effectively reduce average delay compared to the baseline algorithms without causing large computational pressure on edge nodes.

Reliability is one of the evaluation indices of system performance. For improving the reliability of the system, offloading scheme in vehicle edge computing (VEC) has been extensively studied. In order to improve reliability, a priority based reliable offloading scheme is proposed applying a reliability model, which is modeled considering the impact of various evaluation. Specifically, a multi-factor priority evaluation approach is first proposed, taking into account the importance and the urgency of tasks in a comprehensive manner. And then, based on the multi-factor priority evaluation approach, an optimization problem is formulated to maximize the system reliability. Thereafter, a priority based soft actor-critic (P-SAC) algorithm is developed to solve the complex optimization problem effectively. Performance evaluation results validate that the proposed scheme can improve system reliability.

In vehicularad hocnetworks (VANETs), road safety and road traffic efficiency can be improved through message interaction between vehicle users, which inevitably relies on secure identity authentication, and message credibility verification. Existing authentication and message verification mechanisms are prone to severe single points of failure and low authentication efficiency due to their reliance on the trusted third party, especially during the user registration phase. This article proposes a double-layer blockchain and decentralized identifiers assisted secure registration and authentication (BDRA) mechanism for decentralized VANETs, which can achieve the following advantages: 1) realizing a secure and decentralized user registration phase by using the decentralized identifier (DID) technology; 2) accomplishing efficient authentication and message verification by combining double-layer blockchain, DIDs and a reputation feedback strategy; and 3) enabling a more efficient cross section reregistration that reduces the communication time by 30%. The security features and efficiency of the BDRA mechanism are demonstrated by carrying out security analysis and performance evaluation, which is based on the hyperledger fabric (HLF) platform.

With the fast development of the Internet of Vehicles (IoV), the determination of task offloading destinations has been extensively studied. However, the lack of global information makes the existing research unable to apply in the scenario of information asymmetry. In this paper, we first propose a novel intermediary vehicle-assisted task offloading (IVATO) framework under information asymmetry. Then, we develop an intermediary vehicle election mechanism comprehensively considering the trust value and information mastery degree. In this mechanism, we develop a trust value evaluation method based on resilience. In addition, we introduce the indicator of information mastery degree to measure how much information the vehicle has. Finally, simulation results manifest that the proposed trust evaluation method is more reasonable in the long term compared to existing works. Moreover, the performance of the intermediary vehicles can be stably maintained between 0.85 and 1.