ABSTRACT Deploying defense strategies in dispersed computing consumes limited resources, a challenge exacerbated when nodes have only partial information about the system state. This article proposes a security defense strategy for dispersed computing based on Backward Mean Field Games. We first analyze the problem of limited node perception ability during attacks, and construct a backward stochastic differential equation to model the evolution of node resource states. We define an individual cost function intended to optimize resource consumption for the defense strategy. Then we find the optimal decentralized defense strategy and prove that it is ‐Nash equilibrium in the limit system. Finally, simulation experiments validate the strategy's effectiveness, demonstrating that the average system state rapidly stabilizes, and individual nodes robustly track this mean‐field trajectory toward their security targets, even under local attacks. These findings confirm that the strategy effectively guides nodes toward their terminal security targets, highlighting the strategy's good performance and robustness in balancing security needs against resource consumption.

Abstract Vehicular networks increasingly necessitate a robust paradigm that harmonizes high-performance multi-task optimization with stringent data transmission privacy. Nevertheless, current research often struggles to achieve an ideal equilibrium between rigorous security, efficient coordination logic, and the constrained computational capacities of on-board units. To surmount these hurdles, this paper proposes a homomorphic encryption-based privacy preservation for adaptive quantum cross-task optimization (QHE-PSO). The proposed scheme synergistically integrates localized identity authentication, homomorphic fitness appraisal, and a cross-domain strategy migration mechanism. By managing encrypted particle swarms in distinct local regions where particles represent specific strategy configurations, QHE-PSO implements a secure migration protocol to facilitate seamless multi-task coordination. Extensive experimental evaluations confirm that QHE-PSO delivers a superior balance of cryptographic resilience and optimization efficacy compared to state-of-the-art benchmarks.

Context: Digital twins (DTs) have become integral in diverse cyber–physical production systems (CPPS), enabling dynamic interactions between physical entities and their digital counterparts. Yet their integration into such complex ecosystems raises substantial interoperability challenges. While these challenges and associated frameworks for DTs have been extensively theorized in scholarly literature, there are limited empirical investigations that capture industrial perspectives on these aspects. Objective: This exploratory study aims to empirically investigate real-world interoperability challenges in DT deployments and assess the relevance of a layered interoperability framework as a structured approach to address these issues. Methods: We addressed this gap by conducting interviews with 12 DT practitioners from 10 companies across five European countries. Interviewees are guided through two reference models: a simplified view of the DT ecosystem and a layered framework based on the Level of Conceptual Interoperability Model (LCIM). The thematic synthesis and systematic mapping of the collected data have used Grounded Theory (GT)-based open coding. Sentiment analysis was used as an illustrative complement to the qualitative findings by capturing expert attitudes towards the LCIM for DTs. Results: The analysis identified 26 practical interoperability challenges, thematically synthesized into 7 categories. Experts’ perspectives on the LCIM for DTs revealed two key outcomes: 4 drivers of the open and closed-ended nature of interoperability layers, and 4 value propositions highlighting the framework’s relevance for DT deployments. Further, the identified challenge categories are mapped across layers, highlighting the dichotomy of open-source and proprietary approaches, the need for Dynamism and Ecosystem-oriented Interoperability. Conclusions: This work advances empirical and theoretical understandings of DT interoperability within CPPS. Our findings contribute to addressing practical interoperability challenges, provide empirical values for the layered model in cross-disciplinary approaches to DT integration, and offer guidance for researchers and practitioners. Future work could validate and adapt the layered approach through domain-specific DT applications to assess its effectiveness in digital transformation initiatives.

Satellite edge computing offers promising solutions for extending the coverage of terrestrial networks, particularly in remote and harsh environments. By offloading ground data to satellites for processing, this paradigm enables real-time data handling in non-terrestrial networks (NTNs). However, due to limited onboard resources and dynamic task requirements from Internet of Things (IoT) devices, online energy management becomes a critical challenge that hinders the scalability and deployment of satellite edge computing systems. In this paper, we propose an online energy management framework based on satellite-edge collaboration. A queue-based collaboration scheme is designed to coordinate satellites in handling tasks with random arrival patterns. Building upon this scheme, we formulate a joint optimization problem that integrates transmission resource allocation, computational resource assignment, power control, routing strategies, and collaboration policies, aiming to minimize the energy consumption of the satellite network. Given the dynamic, complex, and distributed nature of the problem, we present a Lyapunov-based multi-agent deep reinforcement learning (MADRL) algorithm. Specifically, we first transform the long-term stochastic optimization problem into a sequence of deterministic subproblems using the Lyapunov theory. Sub-sequently, each subproblem is decomposed into a resource allocation subproblem and an edge collaboration subproblem. Correspondingly, we devise a MADRL-based algorithm for edge collaboration and a Lagrangian multiplier iteration (LMI)-based algorithm for resource allocation. Finally, the overall problem is iteratively optimized using the block coordinate descent (BCD) framework. Simulation results demonstrate that the proposed approach achieves significant performance improvements over both baseline methods and several state-of-the-art pure deep reinforcement learning (DRL) approaches.

Low-Earth orbit (LEO) satellite networks can achieve global network coverage without geographical restrictions and are essential to the future communication network. In this article, we study the computing offloading problem in a satellite-terrestrial integrated network for the Internet of Remote Things (IoRT), which aims to reduce the total cost (weighted sum of energy consumption and delay), and jointly offload node selection, offloading ratio, and computational resource allocation to achieve the dynamic management of network resources. First, we propose a hybrid cloud and satellite multilayer multiaccess edge computing (MEC) network architecture that can provide heterogeneous computing resources to terrestrial users. Subsequently, since the problem under consideration is a mixed-integer nonlinear programming problem, we propose a computing offloading algorithm for multiagent reinforcement learning, which is an integration of double deep Q learning (DDQN) and deep deterministic policy gradient (DDPG). The algorithm can learn the optimal policy for actions containing a mixture of discrete and continuous variables. Finally, an optimal computational resource allocation scheme is proposed to improve the task computation efficiency. Simulation results show that the proposed task offloading and resource allocation scheme can achieve reasonable scheduling of computational tasks and optimal allocation of computational resources, reducing the cost of task computation.

In intelligent connected vehicle applications, tasks such as path planning and health management involve numerous matrix operations, particularly matrix multiplication. Due to limited resources, these tasks are often outsourced to the edge server. However, outsourcing these tasks involving matrix multiplication might incur potential risks, such as returning incorrect results to expedite processing or even exposing sensitive data during the computation. Privacy-preserving verifiable matrix multiplication schemes address these concerns. However, it is meaningful in practice only if the verification and decoding time is lower than that of local computation. In this paper, we propose a privacy-preserving verifiable matrix multiplication for intelligent connected vehicles that further reduces the verification and decoding time. To achieve this, we first reduce the length of the ciphertext of linearly homomorphic encryption when encrypting a group of messages. Subsequently, we construct our verifiable matrix multiplication scheme based on the improved linearly homomorphic encryption. It has a lower critical dimension than the state-of-the-art scheme with a similar security level, since the shorter ciphertext and the simpler linearly homomorphic encryption algorithm. Performance analysis and experimental results demonstrate that the critical dimensions of our improved scheme are reduced by 23.3%, while the communication cost is reduced by 68.3%, making it particularly suitable for intelligent connected vehicle applications.

Edge collaboration has emerged as a promising paradigm for Internet of Things (IoT) applications. However, achieving efficient cooperation among these server nodes still faces several critical challenges, including 1) secure node interaction, 2) online task scheduling, and 3) heterogeneous resource management. Unfortunately, most existing solutions address these issues in isolation, lacking an integrated framework that jointly considers security, task scheduling, and resource management. To address these limitations, this paper proposes a blockchain-based online collaboration framework for IoT, where blockchain serves as a trusted top-layer management platform to ensure secure information sharing and resource management. In the proposed framework, we introduce two dynamic queues to effectively manage randomly arriving tasks and develop an online collaboration mechanism tailored for heterogeneous edge servers. Furthermore, we formulate a long-term system utility maximization problem by jointly optimizing collaboration strategies, resource allocation, and block producer selection, subject to queue stability and security constraints. Due to the coupling among decision variables and across time slots, solving the optimization problem directly is challenging. Therefore, we design a novel Lyapunov-based algorithm that integrates convex optimization theory with deep reinforcement learning (DRL), significantly improving the solving efficiency. Extensive simulations demonstrate that the proposed method and algorithm outperform conventional baseline methods and pure DRL-based approaches in terms of system utility, stability, and security performance, making it a promising solution for secure and efficient edge collaboration in dynamic IoT environments.

Digital twin (DT) ecosystems are rapidly evolving, connecting many stakeholders, such as manufacturers, customers, and application platform providers. These ecosystems require collaboration and interaction between diverse actors to create value. This study delves into the collaboration of such stakeholders within DT-focused ecosystems. This research aims to understand stakeholder collaboration within DT ecosystems, identify potential challenges, and provide insights for managing these stakeholders. It also seeks to define the DT ecosystem and its implications for both research and practice. A systematic literature review was conducted, supplemented by empirical evidence gathered from interviews with DT experts who were knowledgeable about the DT ecosystem. The study also analyzed DT systems, stakeholder roles, and the challenges with ecosystem-focused DT development. The study identified various stakeholders and their roles in adding value to a DT ecosystem. It highlighted the benefits of stakeholder collaboration, such as knowledge gain during DT system development. The research also revealed the technical and non-technical challenges encountered in ecosystem-focused DTs, emphasizing the importance of standardization as a solution. A new definition of the DT ecosystem was proposed, emphasizing its data-driven nature, interconnected DTs, stakeholder value creation, and technology enablement. Stakeholder collaboration is pivotal in DT ecosystems, with each actor playing a distinct role. Addressing challenges, especially through standardization (OPC UA and ISO 23247), can lead to more efficient and coherent DT ecosystems. The insights provided by this study can guide industries in designing, developing, and maintaining their DT ecosystems, ensuring value creation and stakeholder satisfaction. Future research avenues that emphasize the importance of understanding the challenges involved and deploy appropriate solutions were suggested.

This work investigates the secure resource collaboration among selfish edge servers for multi-access edge computing (MEC)-enabled VR systems in a dynamic scenario. Due to the time-varying and stochastic nature of VR user requests, the edge servers usually have significant differences in workload. To this end, we first propose a type judgment method to perceive their service capability and divide them into two types, i.e., the requesting node (RN) with a poor service capability and the cooperative node (CN) with a powerful service capability. To promote collaboration among self-interest nodes, we then model the competitive interactions among RNs and CNs as a multi-leader and multi-follower Stackelberg game. For the RN (as the leader), we design a novel pricing strategy based on deep reinforcement learning (DRL) to motivate CNs to provide resource assistance. Meanwhile, an optimal selling strategy for the CN (as the follower) is presented to maximize its payoffs from the network. To overcome the security problem during the resource collaboration, we finally introduce the blockchain as a secure and trusted platform for resource publishing and trading, where an efficient consensus mechanism called Proof-of-Trust (PoT) is developed to improve the performance of blockchain. The simulation results show that the proposed approach achieves superior performance.

This paper presents a computing power scheduling framework based on reinforcement learning (CSFRL) for custom fine-grained resource scheduling on Kubernetes. With the rise of edge computing networks, efficiently adapting computing resources is essential to support various services. While Kubernetes is widely used for container orchestration, few studies have implemented fine-grained resource scheduling using AI algorithms. CSFRL enables the scheduling algorithm to be trained according to user requirements and capable of handling complex scheduling environments, leading to more effective computing resource scheduling on Kubernetes. By conducting a detailed analysis of microservices that require different computing power and using the sorting-based PPO algorithm, CSFRL achieves efficient scheduling of computing power. Experimental results show that CSFRL outperforms the default scheduler on Kubernetes and achieves the expected scheduling results.

Observing blending of realities, daily life and gameplay in location-based mobile games is challenging. This study aims at observing this blending by targeting a vast number of images (N = 2432), which have been taken during gameplay of a well-known game, Pokémon GO. Images were collected from social media communities of Pokémon GO players in Twitter, Facebook and Instagram, and analysed using visual and online ethnography. To keep the sample size manageable for analysis, the images were collected only from Nordic Pokémon GO player communities in eight cities during 2016–2018. The findings show that the blending of daily life and gameplay is observable from the shared photos especially from the augmented reality screenshots which is why in this article the context of gameplay, both outdoors and indoors, in Pokémon GO is described in more detail than in previous studies.

The integration of blockchain and mobile edge computing (MEC), as a secure, efficient, and reliable edge computing paradigm, has been widely applied in many applications, such as large-scale Internet of Things (IoT), Internet of Vehicles (IoV), and smart grid. However, due to the restricted transaction throughput of blockchain, the combination of blockchain and MEC in most existing works cannot support applications with frequent transaction requirements. In this paper, we propose an integrated blockchain and MEC (IBM) framework based on a space-structured ledger to meet the transaction demands for IoT applications. In the framework, a collaborative mining process is designed, where we consider the cooperation between mobile devices (MDs) and MEC servers. To promote mining efficiency, we further develop a high-performance consensus mechanism called reputation-based proof of work (Re-PoW), in which differentiated mining targets are assigned according to the reputation of MDs. In the Re-PoW consensus mechanism, heterogeneous capabilities and historical behaviors of MDs are all considered for accurately evaluating their reputation. In addition, we present an alternating optimization algorithm by jointly optimizing bandwidth allocation and computation resource allocation to further enhance the performance of the proposed scheme. Simulation results show that the proposed approach can achieve significant throughput improvement.

Abstract Enabling and empowering the diverse energy resources to have active yet efficient participation in the smart grid and energy market is an unrivaled challenge for the energy industry. This research expands the four dominant archetypes of business models in the energy and electricity market, creating a fifth archetype, the “blockchain marketplace”. The contributions of the study are to identify the extant electricity market designs and architectures as centralized and pseudo-decentralized while proposing a fully decentralized architecture enabled by the blockchain. The research contributes to the literature of smart grids and demand-side management and introduces the value configuration/architecture approach for the energy market and business model domains.