Uncrewed aerial vehicle (UAV) has emerged as a promising solution for automating railway inspections due to its high mobility, flexible deployment, and reduced labor cost. In this paper, we investigate UAV-assisted railway inspections, which include object recognition, humidity monitoring, and critical infrastructure modeling, each with distinct data volumes and computational requirements. Particularly, we introduce a UAV-assisted railway inspection framework. Different types of sensors are divided into several clusters. The UAV departs from the hive, flies over each cluster to collect their computational requirements, and performs task offloading before returning to the hive. This process is formulated as a joint optimization problem of trajectory planning and task offloading to minimize the weighted sum of latency and energy consumption. Considering the constrained computing and storage capabilities of UAVs, it is crucial but challenging to develop a lightweight yet high-performing solution for the multi-objective optimization problems. As such, a novelArtificial General Intelligence (AGI)-orientedTransformer (AoT) algorithm is proposed to solve the optimization problem. It uses an encoder-only architecture to process either sensor location or task features, and then directs the encoded outputs to different output heads to make decisions on UAV trajectory and task offloading. Simulation results demonstrate that the proposed AoT algorithm outperforms benchmark algorithms in terms of trajectory length and average offloading cost.

Computing-aware networks (CAN) can provide ubiquitous AI inference services for intelligent applications. However, due to the huge differences in the demand for inference services of intelligent applications and the continuous innovation of computing devices, traditional protocol-based scheduling methods make it difficult to schedule complex heterogeneous computing resources. In this paper, we propose a CAN resource scheduling method, named SMAD, which can adapt to external environmental changes without human modification of the protocol mechanism. Aiming at the scheduling problem of complex heterogeneous computing resources and concurrent random diverse inference tasks, a constrained multi-objective optimization problem of scheduling service quantity, accuracy, and delay is formulated. Through the general Markov Decision Process (MDP) transformation from the model, the Deep Reinforcement Learning (DRL)-based AI-native scheduling algorithm framework can further solve the optimization problem. Meanwhile, the AI-native framework aggregates diverse device states into a data tensor, integrates the DRL algorithm in a data-driven manner to generate a dynamic action tensor, and reversely drives full-stack resource scheduling for global closed-loop optimization in CAN, aligning with inference service demands. Extensive simulation results show that the proposed SMAD has good convergence performance. Compared with the traditional DRL algorithm, it significantly increases the number of concurrent schedulable tasks and reduces the inference service delay.

Industrial Internet of Things (IIoT) applications, such as industrial process control, demand ultra-high reliability and bounded delay. The Reliable and Available Wireless (RAW) initiative within the IETF DetNet working group addresses these needs by applying IEEE 802.15.4 time-slotted channel hopping (TSCH) technology and leveraging techniques like Packet Replication, Elimination, and Ordering Functions (PREOF) to ensure deterministic performance for IIoT. However, while PREOF improves reliability, its redundant transmission mechanism inevitably increases energy consumption, conflicting with the energy constraints of TSCH nodes. The existing multipath routing approaches struggle to address this challenge, failing to jointly consider both energy efficiency and deterministic performance. Additionally, these approaches often overlook the delay variation caused by multipath transmissions of different lengths—a key factor that can undermine deterministic performance by increasing buffering requirements and affecting the predictability of data flows. In this paper, we investigate a multipath optimization problem aiming at improving energy efficiency and minimizing delay variation while meeting the requirements of bounded reliability and delay for deterministic flows. Considering the above multipath routing optimization problem, which aims to satisfy multiple objectives under multiple constraints, is typically NP-hard, solving these challenges with traditional methods is highly complex. Thus, we further propose a Energy-Efficient Multi-path Routing (EEMR) algorithm that utilizes deep reinforcement learning (DRL) to optimize the multipath selection, effectively enhancing energy efficiency for deterministism. EEMR can be extended to solve optimization problems in holistic-deterministic multi-domain scenarios, such as smart factories integrating 5G and DetNet. We compare the performance of our proposed method with several baseline methods. Empirical evaluations show that EEMR significantly reduces energy comsumption and delay variation compared to baseline methods under various environment settings.

Mobile edge caching (MEC) is a promising technique to improve the quality of service (QoS) for mobile users (MU) by bringing data to the network edge. However, optimizing the crucial QoS aspects of message freshness and service promptness, measured by age of information (AoI) and service delay, respectively, entails a tradeoff due to their competition for shared edge resources. This article investigates this tradeoff by formulating their weighted sum minimization as a sequential decision-making problem, incorporating high-dimensional, discrete-valued, and linearly constrained design variables. First, to assess the feasibility of the considered problem, we characterize the corresponding achievable region by deriving its superset with the rate stability theorem and its subset with a novel stochastic policy, and develop a sufficient condition for the existence of solutions. Next, to efficiently solve this problem, we propose a mixed-order drift-plus-penalty algorithm by jointly considering the linear and quadratic Lyapunov drifts and then optimizing them with dynamic programming (DP). Finally, by leveraging the Lyapunov optimization technique, we demonstrate that the proposed algorithm achieves an$O(1/V)$versus$O(V)$tradeoff for the average AoI and average service delay.

The emerging intelligent services, spurred by the rise of the intelligent Internet, are placing multidimensional requirements on the network to collaboratively guarantee computing and networking resources. In this article, we propose a unified end-to-end intelligent resource scheduling method for converging networks [e.g., Internet of Things (IoT)], which can always globally abstract the available resources from different networks with a unified model description, and jointly planning the resources from end-to-end by deep reinforcement learning (DRL) algorithms supporting both discrete and continuous variable decisions. The method proposes a three-layer architecture, including service layer, network layer, and adaption layer, which aims at optimizing the flow transmission performance. Through the general Markov decision process (MDP) transformation from the model, the DRL-assisted algorithm can further solve the optimization problem. We categorize heterogeneous network resource scheduling into horizontal and vertical scenarios, applying the proposed architecture to both. Compared with the existing diverse learning (DiLearn) and naive (DiNaive) approaches, the proposed approach is not only time-saving but also can schedule 28.4% and$8\times $more flows in horizontal scheduling scenarios, and improve 54.2% and$3.5\times $flows in vertical scheduling scenarios, respectively.

Traditional route forwarding generates obvious performance problems and it cannot fulfill the increasing diversity in the number of user accesses and Quality of Service (QoS). It is necessary to investigating an intelligent and reliable routing for online audio/video mixed traffic with high-real time to meet different QoS requirements. In this article, we design a routing scheme based on deep reinforcement learning (DRL) and graph neural networks (GNNs), which could be easily implemented as an application on a controller, named RtDG. Specifically, a network topology is first extracted using GNN to generate high-dimensional feature representations. To obtain QoS utility values comprehensively, we set four parameters, namely, bandwidth, delay, packet loss rate, and delay jitter, and construct weighted formulas using the parameters determined by Bayesian optimization. We use proximal policy optimization (PPO) to make output decisions while adding a KL scatter penalty to the loss function. And then, the controller assigns it to switches via traffic table based on the calculated QoS values. Furthermore, we deploy an overlay network using Mininet and ONOS, enabling optimal pathfinding without changing the existing network architecture. Meanwhile, tests are conducted under background traffic of online audio/video. Extensive simulation results demonstrate that the RtDG can significantly reduce average delay and packet loss rate by 52.21% and 57.83%, compared to traditional routing strategies. Especially under high traffic conditions, it is able to consider the uncertainty during path selection and achieve excellent routing performance.

Given the delay-critical nature of AI-driven industrial automation applications, industrial cyber-physical systems are evolving from centralized cloud automation to decentralized cloud-fog automation to reduce model inference delay. However, traditional centralized deep federated learning is not wellsuited for this evolution, primarily due to scalability and delay issues caused by centralized parameter synchronization. Thus, we introduce a decentralized deep federated learning (DDFL) architecture. While DDFL resolves scalability and delay concerns, decentralized parameter synchronization amplifies the delay imbalance impact caused by uneven computing and network loads. Additionally, traditional single-task load balancing approaches with fixed load balancing weights face challenges posed by diverse delay requirements across different model training tasks. To overcome these challenges, we formulate a hybrid multi-task Markov decision process with the objective of minimizing flexibly weighted computing and network load. We further propose a hybrid multi-task deep reinforcement learning (MTDRL) scheme based on the importance-weighted actor-learner architecture, which trains a hybrid-MTDRL decision model to select fog servers and paths with balanced loads suited to diverse delay requirements. Realistic trace-based simulation and testbed evaluation results demonstrate that hybrid-MTDRL outperforms benchmarks in load balancing and reducing training delay.

Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.

Multicasting is an efficient technique for simultaneously transmitting common messages from the base station (BS) to multiple mobile users (MUs). Multicast scheduling over multiple channels, which aims to jointly minimize the energy consumption of the BS and the latency of serving asynchronized requests from the MUs, is formulated as an infinite-horizon Markov decision process (MDP) problem with a large discrete action space, multiple time-varying constraints, and multiple time-invariant constraints. To address these challenges, this paper proposes a novel distribution-embedding multi-agent proximal policy optimization (DE-MAPPO) algorithm, which consists of one modified MAPPO and one distribution-embedding module. The former one handles the large discrete action space and time-varying constraints by modifying the structure of the actor networks and the training kernel of the conventional MAPPO; and the latter one iteratively adjusts the action distribution to satisfy the time-invariant constraints. Moreover, a performance upper bound of the considered MDP is derived by solving a two-step optimization problem. Finally, numerical results demonstrate that our proposed algorithm outperforms the existing ones in terms of applicability, effectiveness, and robustness, and achieves comparable performance to the derived upper bound.

To reduce redundant traffic transmission in both wired and wireless networks, optimal content placement problem naturally occurring in many applications is studied. In this paper, considering the limited cache capacity, unknown popularity distribution and non-stationary user demands, we address this problem by jointly optimizing content caching and routing with the objective of minimizing transmission cost. By optimizing the routing with theroute-to-least cost-cachepolicy, the content caching process is modeled as a Markov decision process (MDP), aiming to maximize caching reward. However, the optimization problem consists of multiple nodes selecting caching contents, which leads to the combinatorial increase of the number of action dimensions with the number of possible actions. To handle this curse of dimensionality, we propose an intelligent caching algorithm by embedding action branching architecture into a dueling double deep Q-network (D3QN) to optimize caching decisions, and thus the agent at the controller can adaptively learn and track the underlying dynamics. Considering the independence of each branch, a marginal gain-based replacement rule is proposed to satisfy cache capacity constraint. Our simulation results show that compared with the prior art, the caching reward and hit rate of the proposed algorithm are increased by 35.3% and 33.6% respectively on average.

Satellite-terrestrial integrated network (STIN) is a full-scale communication paradigm, which can support joint information processing and seamless service provision by leveraging satellites' wide coverage and terrestrial networks' high capacity. The existing STIN operates with insufficient synergy in transmission scheduling, impacting resource allocation efficiency and transmission delay optimization, particularly in complex transmission scenarios. In this paper, we designDeterministic STIN (DetSTIN), a novel architecture for STIN, along with two algorithms tailored for transmission scheduling to collaboratively optimize resource adaptation and service flow scheduling. Specifically, the DetSTIN enables the smooth interconnection and integration of heterogeneous networks by providing layered deterministic services. Besides, a genetic-based resource adaptation algorithm is designed for fixed-mobile-satellite heterogeneous networks to reduce resource allocation overhead while maintaining the network performance. Furthermore, we propose a deep reinforcement learning-based differentiated scheduling algorithm to solve the routing-queue two-dimensional decision problem to differentially optimize transmission delay of service flows, thus obtaining higher transmission scheduling benefit. By addressing resource adaptation and differentiated scheduling synergistically, the proposed solution achieves reduced resource allocation overhead and increased transmission scheduling benefit, ultimately leading to increased network operation revenue of the DetSTIN. Simulation results demonstrate that the proposed solution delivers effective performance across various flow proportions, and as the number of flows increases, the network operation revenue exhibits a noticeable improvement, compared with benchmark algorithms.

The widespread adoption of real-time services on the Internet has aroused interest in the study of low-latency and deterministic communications. Deterministic guarantee over wide-area networks (WANs), the primary infrastructure for communications, is essential to achieving end-to-end deterministic transmission. However, applying off-the-shelf deterministic schemes to WANs is challenging due to the statistical multiplexing nature of WANs and the non-periodic nature of WAN traffic. In this paper, we propose a novel deterministic framework for WANs, named DetWAN, which guarantees the timely delivery of WAN traffic via deadline-aware routing and scheduling. We design a coordinated earliest deadline first (CEDF) scheduling scheme in the data plane of the DetWAN, which provides determinism for non-periodic deadline-constrained traffic while following statistical multiplexing. To precisely estimate the capacity of deadline-constrained traffic that the DetWAN can satisfy, we derive an end-to-end deadline satisfiability criterion in the DetWAN by introducing the deadline curve into traffic modeling. Based on the criterion, we formulate the deadline-aware routing and scheduling problem as a stochastic optimization problem to maximize the timely delivery ratio. Furthermore, we propose a distributed admission control algorithm based on multi-agent deep reinforcement learning in the control plane to solve the problem in a highly autonomous manner. The algorithm can jointly determine optimal routes and per-hop deadline budgets for traffic flows in a decentralized mode. Extensive evaluation results validate the deterministic guarantee as well as the high throughput of the DetWAN and show that the proposed admission control algorithm can significantly improve the timely delivery ratio compared with benchmarks in WAN scenarios.

Vehicle trajectory data plays a pivotal role in simulation testing for autonomous driving. Hence, there exist well-established trajectory generation methods employing deep generative models to generate trajectories mapping the distribution of the original dataset, thereby augmenting existing trajectory datasets. However, these methods typically rely on large datasets gathered by governmental or organizational entities for central training, which may pose data privacy, security, and accessibility issues. Therefore, it is challenging to generate high-quality traffic trajectory data while preserving privacy which involves a delicate balance between these two objectives. To deal with this challenge, we introduce Federated Learning into the diffusion model and propose a Federated Learning-based diffusion model (FedDifftraj) to generate traffic trajectory data. Unlike existing central training methods, FedDifftraj aggregates model parameters uploaded by different vehicles and then updates a global model. Additionally, there is a substantial communication overhead incurred during the training of the federated diffusion model. Therefore, we quantize the local diffusion model before uploading it to the parameter server. Through extensive simulations on real-world datasets, FedDifftraj can generate high-quality traffic trajectory data that is consistent with the results of the central training while preserving privacy and reducing communication overhead by 93.74% when utilizing 8-bit quantization.

Unmanned Aerial Vehicles (UAVs) provide a flexible working paradigm for device-cloud communication. Besides working as a relay between devices and clouds, UAVs can also provide mobile edge computing (MEC) services. In this paper, we investigate a computation offloading problem for UAV-assisted MEC networks in which local devices, UAVs, and clouds collaboratively process computing tasks to achieve energy-saving and latency reduction. In such green UAV-assisted MEC networks, we treat the same energy consumption of task processing differently due to processing location and assign different weights to the energy consumption of devices, UAVs, and clouds. Specifically, we propose a two-stage computation offloading framework including 1) the device clustering stage to determine the device cluster connected to certain UAVs and 2) the network operation stage to conduct computation offloading. We formulate the offloading process as a stochastic optimization problem to minimize the offloading cost. Furthermore, we decouple the optimization problem into a UAV selection subproblem and an offloading decision subproblem. Particularly, for the former subproblem, we employ a simulated annealing-based algorithm to minimize the total transmit energy of devices and UAVs. For the latter, we utilize a proximal policy optimization-based offloading algorithm to ascertain the processing locations of computing tasks. Simulation results show that the proposed algorithm outperforms in terms of energy reservation and latency reduction.

Integration of the fifth-generation mobile communication technology (5G) into time-sensitive networking (TSN) was first proposed in the 3GPP Release 16. However, this conceptual proposal lacks of detailed designs to guarantee bounded latency and high reliability of this integration. In this paper, we study a deterministic scheduling problem for TSN-5G integrated networks in industrial Internet of things (IIoT) scenarios, in which a unified control plane jointly allocates the time-frequency resources for TSN and 5G to support deterministic end-to-end transmission. Specifically, we design a novel control architecture, i.e., centralized network and distributed user, for the integrated networks to reduce the signaling overhead. Moreover, we formulate a stochastic optimization problem for IIoT scenarios to maximize the number of successfully scheduled flows as well as realize throughput fairness for wired and wireless equipment. Since the resource allocation of TSN and 5G are coupled, this problem is NP-hard. We propose a dueling double deep Q network (D3QN) based Joint Resource Allocation (DJRA) algorithm. By leveraging two convolution-enhanced neural networks, with their parameters periodically synchronized, the accuracy of the estimated Q-value can be increased and the convergence speed of DJRA can be accelerated. Simulation results show that the proposed algorithm can facilitate efficient cooperation between TSN and 5G as compared to the other heuristic and learning-based algorithms.

Artificial Intelligence-Generated Content (AIGC) is a rapidly evolving field that utilizes advanced AI algorithms to generate content. Through integration with mobile edge networks, mobile AIGC networks have gained significant attention, which can provide real-time customized and personalized AIGC services and products. Since blockchains can facilitate decentralized and transparent data management, AIGC products can be securely managed by blockchain to avoid tampering and plagiarization. However, the evolution of blockchain-empowered mobile AIGC is still in its nascent phase, grappling with challenges such as improving information propagation efficiency to enable blockchain-empowered mobile AIGC. In this paper, we design a Graph Attention Network (GAT)-based information propagation optimization framework for blockchain-empowered mobile AIGC. We first innovatively apply age of information as a data-freshness metric to measure information propagation efficiency in public blockchains. Considering that GATs possess the excellent ability to process graph-structured data, we utilize the GAT to obtain the optimal information propagation trajectory. Numerical results demonstrate that the proposed scheme exhibits the most outstanding information propagation efficiency compared with traditional routing mechanisms.

In this paper, we present a three-layer (i.e., device, field, and factory layers) deterministic federated learning (FL) framework, named DetFed, which accelerates collaborative learning process for ultra-reliable and low-latency industrial Internet of Things (IoT) via integrating 6G-oriented Time-sensitive Networks (TSN). Utilizing dispersive local data, industrial IoT devices distributively train a deep neural network (DNN) model, and the updated model parameters are aggregated at their associated field servers every round or at a centralized factory server every a few rounds. Aiming at optimizing the learning accuracy of FL without affecting the co-transmission of burst traffic (e.g., safety-critical traffic), an integrated TSN is considered to establish connections among the three layers, where a cyclic queuing and forwarding mechanism is deployed in each switch to support deterministic model parameter transmission with microsecond-level delay and near-zero packet loss requirements. To improve the FL performance, we formulate a multi-objective stochastic optimization problem to simultaneously maximize the scheduling success ratio and learning accuracy while satisfying the deterministic requirements of delay, jitter, and packet loss. Since the objective function is implicit and the available time slots of the considered TSN in each FL round are temporally correlated, the problem is difficult to solve in real time. Therefore, we transform the problem into a Markov decision process formulation and propose a dynamic resource scheduling algorithm, based on deep reinforcement learning, to make optimal resource scheduling decisions while adapting to device heterogeneity and network dynamics. Experimental results based on real-world dataset demonstrate that the proposed DetFed significantly accelerates FL convergence and improves learning accuracy as compared to state-of-the-art benchmarks.

Multipath transmission is a critical enabling technology to enhance QoE for edge users. The packet scheduler plays an irreplaceable role in overcoming heterogeneity and dynamicity in multipath transmission. However, current schedulers depend on an inaccurate delay estimation and lack systematic traffic intensity awareness, performing poorly in wireless heterogeneous networks (HetNets). In this paper, we propose a novel traffic-aware two-level packet scheduler (TA2LS) to address the problem and improve aggregated bandwidth while trading off delay. In particular, we design a multipath transmission state machine (MTSM) to perceive link traffic intensity. MTSM replaces network prediction algorithms by identifying the contribution of each link in multipath transmission in a cost-effective way. Further, we propose a scheduling mechanism based on a two-level optimal-path evaluation method (2LOSM) to adjust the packet scheduling policy adaptively. 2LOSM increases the priority of links with low traffic intensity during scheduling, improving aggregated bandwidth performance and reducing end-to-end delay. We have built a real-world 4G/5G/WiFi testbed and deployed 47 dynamic scenarios to evaluate TA2LS and other five schedulers. In 4G/5G/WiFi scenarios, TA2LS improves aggregated bandwidth by 10.32%–48.27% compared to the second-best scheduler and reduces end-to-end delay by 5.04%–39.98% under the premise of fewer or equivalent overheads.

Deterministic transmission guarantee in time-sensitive networks (TSN) relies on queue models (such as CQF, TAS, ATS) and resource scheduling algorithms. Thanks to its ease of use, the CQF queue model has been widely adopted. However, the existing resource scheduling algorithms of CQF model only focus on periodic time-triggered (TT) flows without consideration of bursting flows. Considering that the bursting flows often carry high-priority data in real systems, in this paper we investigate the mixed-flow (i.e., TT and bursting flows) scheduling problem in CQF-based TSN aiming to maximize the number of schedulable flows and system load balance while satisfying the deterministic demands of delay, jitter, and reliability for both TT and bursting flows. Unfortunately, it is challenging to schedule the mixed flows with the original CQF model because of the huge difference between TT and bursting flows. To resolve this problem, we firstly design an enhanced Multi-CQF model to satisfy the basic demands of bursting flows sent at any time without affecting the deterministic transmission of TT flows. Given the complexity of mixed-flow scheduling and the proposed queue model, it is difficult for traditional algorithms to fully utilize network resources. Thus, we further propose a uline time-correlated uline DRL uline resource uline scheduling (TimeDRS) algorithm to optimize the resource allocation. TimeDRS can be extended to other time-related resource scheduling scenarios, such as TDMA-based scheduling. Experimental results demonstrate that our proposed approaches can greatly reduce frame loss and end-to-end latency for bursting flows, and well balance runtime and schedulability compared with state-of-the-art benchmarks.

More data and information is being captured from systems, machines, and devices and made available to industrial information technology (IT) systems. The information is processed on-the-fly, enabling IT-based management systems to generate updated information for real-time control of the manufacturing processes. This data capturing and collection for IT systems is often referred to as the Internet of Things (IoT). When adopted to the industrial requirements, such as robustness, reliability, timeliness, and security, it is often termed as the industrial IoT (IIoT) [A1]. IIoT has attracted the attention of both the industry and academia since it is expected to enhance day-to-day activities, create new business models, products, and services, and as a broad source of research topics and ideas. Meanwhile, it is envisioned that the fifth-generation (5G) networks will be a cornerstone in future wireless industrial connectivity, and currently, there are multitude of ongoing research efforts in their design and optimization. Future industries willembrace use cases with numerous wireless-connected sensors and devices, and judging by the demand, massive machine-type communication and ultra-reliable low-latency communication (URLLC) in the literature and standardization activities, have been identified as two of the three main communication scenarios for 5G. These scenarios demand intelligent, scalable, and robust radio access techniques, network architectures, and deployment options to meet industrial demands [A2]. Therefore, more in-depth research is needed for IIoT and sensor networks in 5G-and-beyond wireless communication systems to address various challenges, including the following. 1)Transmit power control policy should be judiciously designed to improve both the spectrum efficiency and energy efficiency effectively; higher transmit powers can improve reliability but increase the interference and battery consumption. 2)Low-latency communication and computing is one of the significant challenges in 5G-and-beyond IIoT; uploading the device data to the cloud computing centers has high latency and resources waste issues in sensor networks. 3)Addressing privacy and security problems [A3] in the 5G-IIoT is fundamental to the further development and spread of 5G-IIoT. 4)Reliability and latency requirements of URLLC services, requiring less than 1-ms user plane latency and higher than 99.999% reliability, are demanding to meet, especially in time-varying industrial wireless channels. 5)Radio resource allocation, sharing, and isolation with performance guarantees under dynamic traffic conditions are critical issues for emerging IIoT applications requiring real-time support of massive connected devices. 6)5G-and-beyond IIoT networks must satisfy industrial-grade coverage, capacity, time-sensitive networking, and over-the-air time synchronization requirements [A4].

In this paper, we aim to make the best joint decision of device selection and computing and spectrum resource allocation for optimizing federated learning (FL) performance in distributed industrial Internet of Things (IIoT) networks. To implement efficient FL over geographically dispersed data, we introduce a three-layer collaborative FL architecture to support deep neural network (DNN) training. Specifically, using the data dispersed in IIoT devices, the industrial gateways locally train the DNN model and the local models can be aggregated by their associated edge servers every FL epoch or by a cloud server every a few FL epochs for obtaining the global model. To optimally select participating devices and allocate computing and spectrum resources for training and transmitting the model parameters, we formulate a stochastic optimization problem with the objective of minimizing FL evaluating loss while satisfying delay and long-term energy consumption requirements. Since the objective function of the FL evaluating loss is implicit and the energy consumption is temporally correlated, it is difficult to solve the problem via traditional optimization methods. Thus, we propose a “Reinforcement on Federated” (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem. Specifically, the RoF scheme is executed decentralizedly at edge servers, which can cooperatively make the optimal device selection and resource allocation decisions. Moreover, a device refinement subroutine is embedded into the RoF scheme to accelerate convergence while effectively saving the on-device energy. Simulation results demonstrate that the RoF scheme can facilitate efficient FL and achieve better performance compared with state-of-the-art benchmarks.

With the rapid development of artificial intelligence and industrial Internet of Things (IIoT) technologies, intelligent predictive maintenance (IPdM) has received considerable attention from researchers and practitioners. To efficiently predict impending failures and mitigate unexpected downtime, while satisfying the instant maintenance demands of industrial facilities is very important for improving the production efficiency. In this article, a self-attention based “Perception and Prediction” framework, called DeepHealth, is proposed for the instant IPdM. Specifically, the framework is composed of two submodels (i.e., DH-1 and DH-2), which are respectively utilized to perform the health perception and sequence prediction. By operating the framework, the proposed models can predict the health conditions via predicting the future signal samples, thereby completing the instant IPdM. Considering the potential temporal correlation in time series, we deploy an enhanced attention mechanism to capture global dependencies from the vibration signals, and leverage the long- and short-term sequence prediction of sensor signals to support instant maintenance decision-making. On this basis, we conduct a destructive experiment based on the IIoT-enabled rotating machinery and construct a balanced industrial dataset for model evaluations. Extensive experiment results show that the proposed solution achieves good prediction accuracy for instant IPdM on the automatic washing equipment and Case Western Reserve University datasets.

In this paper, we investigate the joint task assignment and resource allocation for deep neural network (DNN) inference in the device-edge-cloud based industrial Internet of things (IIoT) networks. To efficiently orchestrate the limited spectrum and computing resources in IIoT networks for massive DNN inference tasks, a resource management problem is formulated with the objective of maximizing the average inference accuracy while satisfying the quality-of-service of DNN inference tasks. Considering the strict delay requirements of inference tasks, we transform the formulated problem into a Markov decision process, and propose a deep deterministic policy gradient based learning algorithm to obtain the solution rapidly. Simulation results show that the proposed algorithm can achieve high average inference accuracy.

Industrial wireless sensor networks (IWSNs) have mainly been used to monitor applications, but recently an interest in control and safety applications has emerged. Functional safety and communication in open transmission systems have been laid down in the IEC 61784-3-3 standard. The standard is based on a cyclic polling mechanism, which consumes a considerable amount of bandwidth; since existing IWSNs are very resource-constrained, this becomes a major challenge. To overcome this problem, this paper proposes a novel framework that uses an event-triggered failsafe mechanism based on synchronous wired polling and wireless time-slotted time division multiple access. We analytically derive the minimum and maximum bound for the most important metric for safety-critical applications, safety function response time (SFRT). A new metric, normal state interrupt time (NSIT), is proposed in this paper. Furthermore, we also implement the proposed framework by using the WirelessHART standard. The results are compared to the classical time-triggered approach used in the IEC 61784-3-3 standard. The obtained results show that the proposed framework can reduce the bandwidth usage by 90% and support safety-critical applications that require a SFRT less or equal to 150 ms.

Wireless control system for industrial automation has been gaining increasing popularity in recent years thanks to their ease of deployment and the low cost of their components. However, traditional low sample rate industrial wireless sensor networks cannot support high-speed application, while high-speed IEEE 802.11 networks are not designed for real-time application and not able to provide deterministic feature. Thus, in this paper, we propose Det-WiFi, a real-time TDMA MAC implementation for high-speed multihop industrial application. It is able to support high-speed applications and provide deterministic network features since it combines the advantages of high-speed IEEE802.11 physical layer and a software Time Division Multiple Access (TDMA) based MAC layer. We implement Det-WiFi on commercial off-the-shelf hardware and compare the deterministic performance between 802.11s and Det-WiFi under the real industrial environment, which is full of field devices and industrial equipment. We changed the hop number and the packet payload size in each experiment, and all of the results show that Det-WiFi has better deterministic performance.

Trust evaluation plays an important role in securing wireless sensor networks (WSNs), which is one of the most popular network technologies for the Internet of Things (IoT). The efficiency of the trust evaluation process is largely governed by the trust derivation, as it dominates the overhead in the process, and performance of WSNs is particularly sensitive to overhead due to the limited bandwidth and power. This paper proposes an energy-aware trust derivation scheme using game theoretic approach, which manages overhead while maintaining adequate security of WSNs. A risk strategy model is first presented to stimulate WSN nodes' cooperation. Then, a game theoretic approach is applied to the trust derivation process to reduce the overhead of the process. We show with the help of simulations that our trust derivation scheme can achieve both intended security and high efficiency suitable for WSN-based IoT networks.

Virtual machine (VM) live migration, the key problem of modern virtualized data centers, is a challenging task since 1) Frequent traffic across data center between coupling VMs limits the efficiency of current methods. 2) Most existing approaches suffered from poor scalability issues as multi-objective optimization is still an open question in these designs. To address these problems, in this paper, a novel multi-objective VM migration algorithm is proposed. Given the definition of dominant resource fairness, a max-min fair model subject to server-side constraints is introduced. Then, we further formulate the VM migration as an optimization problem which considers application dependencies to reduce network traffic caused by migration. By incorporating the two basic VM migration algorithms, we conduct a joint formulization for maximizing the utilization of physical machines while minimizing the traffic burden across dependent VMs. The simulation result demonstrates the accuracy of the theoretic model and it is shown that our proposed method decreases network traffic by up to 82.6%, significantly improving the efficiency of data centers.

Security is one of the most important issues we should solve before deploying wireless sensor networks in practical applications. Compared with traditional wireless networks, the industrial environment is harsher and more complex, which has a higher requirement for network security. Although cryptographic primitives can provide the capability to resist against the attacks from the external network, they can not address the problem caused by the internal failed or compromised field devices. In this paper, we propose a trust management scheme for Industrial Wireless Sensor Networks to solve this problem. By utilizing the centralized network manager, we first provide the architecture of trust management to improve the security of the network. Then a trust computation model is proposed to evaluate the trust value of field devices. Finally, we compare the performance of our scheme with some classic trust management schemes. The simulation results show that our scheme can improve the accuracy and robustness of the trust evaluation process in industrial environments and ensure the security of the network.

The energy efficiency of modern data centers has become a practical concern and has attracted significant attention in recent years. In contract to existing solutions that primarily focuses on only one specific aspect of management to reduce energy consumption, this paper explores the balance between server energy consumption and network energy consumption to present an energy-aware joint virtual machine (VM) placement. Given the definition of VM placement fairness, the basic algorithm of VM placement which fulfills server energy consumption constraints is conducted. Then, we further formulate the VM placement as an optimization problem which considers application dependencies to reduce network energy consumption. We design a joint algorithm that efficiently solves the VM placement problem for very large problem sizes. Using simulations, we conduct a comparative analysis on the impact of the data center architectures, server constraints and application dependencies on the potential performance gain of energy-aware VM placement. Compared to existing generic methods, we show a significant performance improvement such as efficiently reducing the number of physical machines used to save server energy consumption, decreasing the communication distance between VMs to obtain data center network energy consumption efficiency, improving scalability of data centers.

With the success of wireless technologies and the number of wireless devices increasing, the wireless coverage footprint expands and removes the needs of wired networks for industrial applications. Wireless industrial sensor networks (WISNs) are a type of wireless sensor networks (WSNs), which dedicate to industrial applications. The WISNs bring several advantages over traditional wired industrial networks for detection and control, include flexibility, scalability, remote maintenance, and rapid deployment. However, some technical issues and routing design principles need to be noticed in terms of the stringent requirements of industrial applications. In this paper, we firstly introduce the characteristics and some routing issues of WISNs. Then we discuss existing routing protocols for WISNs and analysis their performances. Finally, we outline several future research directions.