A Novel Task Offloading and Resource Allocation Framework With Parallel Intelligence Collaboration in DT-Empowered IIoT

The rapid development of artificial intelligence and 5G paradigm, opens up new possibilities for emerging applications in industrial Internet of Things (IIoT). However, the large amount of data, the limited resources of Internet of Things devices, and the increasing concerns of data privacy, are major obstacles to improve the quality of services in IIoT. In this article, we propose the digital twin edge networks (DITENs) by incorporating digital twin into edge networks to fill the gap between physical systems and digital spaces. We further leverage the federated learning to construct digital twin models of IoT devices based on their running data. Moreover, to mitigate the communication overhead, we propose an asynchronous model update scheme and formulate the federated learning scheme as an optimization problem. We further decompose the problem and solve the subproblems based on the deep neural network model. Numerical results show that our proposed federated learning scheme for DITEN improves the communication efficiency and reduces the transmission energy cost.

Task offloading in edge computing is important for the Industrial Internet of Things (IIoT) to implement computation-intensive applications in real time. However, achieving efficient task offloading in IIoT is very challenging due to the limited computing resources of IIoT devices, the coupling of computing and communication resources, and the unreliability in multihop wireless transmission. In this article, we construct a link model by considering the influence of unreliable links in multihop transmission to reveal the relationship between reliability and transmission delay. Then, a nonconvex optimization problem that minimizes task processing delay is formulated, and task offloading is decided by considering transmission path selection, bandwidth allocation, and computational resource allocation. To solve this problem, an algorithm based on the alternating direction method of multipliers (ADMM) is designed using auxiliary variables and reformulation linearization technology (RLT). The simulation results show that our proposed algorithm can fully utilize the computing power of the edge server and reduce the task processing delay. Compared with the centralized algorithms, the performance of the proposed scheme is only 1% worse, but the calculation time can be reduced by 40%.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

The rapid growth of Industrial Internet of Things (IIoT) terminals has resulted in tasks exhibiting increased concurrency, heterogeneous resource demands, and dynamic priorities, significantly increasing the complexity of task scheduling in edge computing. Cloud-edge-end collaborative computing leverages cross-layer task offloading to alleviate edge node resource contention and improve task scheduling efficiency. However, existing methods generally neglect the joint optimization of task offloading, resource allocation, and priority adaptation, making it difficult to balance task execution and resource utilization under resource-constrained and competitive conditions. To address this, this paper proposes a two-stage dynamic-priority-aware joint task offloading and resource allocation method (DPTORA). In the first stage, an improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm integrated with a Priority-Gated Attention Module (PGAM) enhances the robustness and accuracy of offloading strategies under dynamic priorities; in the second stage, the resource allocation problem is formulated as a single-objective convex optimization task and solved globally using the Lagrangian dual method. Simulation results show that DPTORA significantly outperforms existing multi-agent reinforcement learning baselines in terms of task latency, energy consumption, and the task completion rate.

Emerging technologies such as digital twins and 6th Generation mobile networks (6G) have accelerated the realization of edge intelligence in Industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users, hinder the effective application of federated learning in IIoT. In this paper, we introduce the Digital Twin Wireless Networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system, and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multi-agent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning method.

With the development of artificial intelligence technology and intelligent devices, people show great interest in intelligent applications and services, but it is impossible to complete these compute-intensive AI tasks locally, especially video analysis tasks. Edge computing is regarded as an appropriate solution to these problems. In this paper, we study the multi-user multi-server edge-end collaboration video analytics task offloading problem aiming at minimizing the overall delay for each device to finish its task. Each device chooses whether to execute the task locally or to offload the task to an edge server, and which edge server to select. At the theoretical level, we model the joint problem of task offloading and resource allocation as a mixed integer programming problem. We first determine the optimal resource allocation policy with a given task offloading decision profile. Then, task offloading problem is modeled as a congestion game and propose a decentralized mechanism to achieve a Nash equilibrium. Moreover, experimental results demonstrate that the proposed method is efficient and can significantly and steadily improve the system performance, reducing the overall delay by 33.96% on average, compared with other algorithms.

Task offloading frameworks play a crucial role in modern healthcare by optimizing resource utilization, reducing computational burdens, and enabling real-time medical decision-making. However, existing Digital Twin (DT)-based healthcare models suffer from high latency, inefficient resource allocation, cybersecurity vulnerabilities, and computational limitations when processing large-scale patient data. These constraints pose significant risks in time-sensitive applications such as ICU monitoring, robotic-assisted surgeries, and telemedicine. To address these limitations, this paper introduces a Quantum-Enhanced DT-IoT framework, integrating Artificial Intelligence (AI), Quantum Computing (QC), DT, and the Internet of Things (IoT) for real-time, secure, and efficient healthcare task offloading. The proposed system introduces two key optimization algorithms: (1) DTH-ATB-MAPPO, which dynamically adjusts task scheduling and resource distribution, and (2) AQDT-IoT, which enhances computational efficiency and cybersecurity compliance in 6 G-enabled IoT networks. By leveraging Approximate Amplitude Encoding (AAE) and Grover’s search, the framework enhances task offloading efficiency, enabling faster decision-making and optimized resource distribution across 6 G-enabled IoT networks. Empirical evaluations show that quantum preprocessing improved Task Offloading Success Rate (TOSR) by 32% and reduced the Error Rate (ER) by 80%, significantly outperforming traditional DT-based healthcare models. These enhancements enable. Additionally, theoretical analysis demonstrates computational speed enhancements, adaptive cybersecurity mechanisms, and improved system scalability, positioning this framework as a viable candidate for future cloud-based quantum healthcare infrastructures, even in resource-constrained hospital environments.

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value (μ) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations.

Bilevel optimization problems require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy development, transportation problems, and others. In the context of a bilevel single objective problem, there exists a number of theoretical, numerical, and evolutionary optimization results. However, there does not exist too many studies in the context of having multiple objectives in each level of a bilevel optimization problem. In this paper, we address bilevel multi-objective optimization issues and propose a viable algorithm based on evolutionary multi-objective optimization (EMO) principles. Proof-of-principle simulation results bring out the challenges in solving such problems and demonstrate the viability of the proposed EMO technique for solving such problems. This paper scratches the surface of EMO-based solution methodologies for bilevel multi-objective optimization problems and should motivate other EMO researchers to engage more into this important optimization task of practical importance.

Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices.

This chapter aims to explore the managerial aspects of implementing artificial intelligence (AI) governance and digital twins (DTs) within the context of Industrial Internet of Things (IIoT). It focuses on establishing ethical and secure cybersecurity frameworks to enhance operational efficiency and protect critical infrastructure. The focus is on conducting systematic literature review. It begins with an overview of AI governance principles and the role of DTs in IIoT. The research then delves into the challenges and best practices for integrating these technologies, emphasising the importance of ethical considerations and robust cybersecurity measures. The study reveals that effective AI governance and the use of DTs can significantly improve the security and operational performance of IIoT systems. However, the integration of these technologies requires careful planning and adherence to ethical standards. For managers in the industrial sector, this chapter provides actionable insights into developing and implementing ethical and secure AI governance frameworks. It highlights the importance of fostering a culture of cybersecurity awareness and ethical responsibility, which are essential for safeguarding DTs and IIoT systems against cyber threats. This chapter contributes to the emerging discourse on AI governance and DTs by offering a managerial perspective on the ethical and secure deployment of these technologies in IIoT. It fills a gap in existing literature by providing a comprehensive analysis of the interplay between AI, DTs, and cybersecurity, and offering practical recommendations for industry leaders.

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.

The task offloading and resource allocation strategies aim to provide efficient edge computing services for ground mobile devices in mobile edge computing (MEC) environments. In this paper, we propose a UAV-assisted dynamic deployment and decision-making framework (dDDM) to address the issue of insufficient base station coverage in remote areas or emergency scenarios, which leverages the flexibility and computational capabilities of unmanned aerial vehicle (UAV) to supplement terrestrial cellular networks. We focus on optimizing the UAV trajectory to support and enhance the effectiveness of task offloading and resource allocation strategies under conditions of user mobility and stochastic task arrivals. Therefore, a joint optimization problem is formulated with the objective of minimizing the weighted sum of the total latency of ground mobile devices and the overall energy consumption of UAV. To solve this problem, the original optimization problem is decomposed into two subproblems: UAV trajectory planning, and decision-making for task offloading & computing resource allocation. For the UAV trajectory planning subproblem, we propose an improved particle swarm optimization(PSO) algorithm integrated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>-means clustering, i.e., KMPSO algorithm, enabling dynamic UAV deployment in response to user mobility. For the task offloading and resource allocation decision-making, an improved black-winged kite algorithm (IBKA) is developed to determine optimal offloading and allocation strategies. Simulation results demonstrate that the proposed approach outperforms existing benchmark algorithms in terms of both latency and energy consumption.

Federated deep reinforcement learning for task offloading and resource allocation in mobile edge computing-assisted vehicular networks

While service-oriented low Earth orbit (LEO) satellite edge computing (LSEC) frameworks enable diverse edge services for user tasks, the heterogeneous distribution of terrestrial users causes substantial imbalance in computational task loads across satellites. This asymmetric workload leads to inefficient resource utilization and degraded edge computing performance. To address these challenges, we propose a joint optimization framework that integrates edge service deployment and task offloading, supported by service popularity analysis and spatiotemporal user-task modeling. The framework employs a two-timescale design: at the large timescale, an improved atomic orbital search (iAOS) heuristic dynamically optimizes service placement, configuration, and resource allocation; at the small timescale, a direction-selective multi-agent double deep Q-network (DS-MDDQN) leverages deep reinforcement learning to route tasks to the most suitable processing nodes. Extensive simulations show that our approach significantly outperforms six representative baselines in both user-perceived performance and system-level efficiency. Replacement studies further verify the effectiveness of each component: iAOS enhances resource utilization and reduces task failure through optimized service deployment, while DS-MDDQN mitigates network dynamics and lowers task completion latency via adaptive task offloading.