This paper addresses the need for an explainable and generalized intrusion detection system (IDS) for the in-vehicle networks (IVNs). While machine learning (ML)-based IDS solutions show promising performance, there are still some challenges, such as the lack of trustworthiness and scarcity of attack representing data, hindering their adoption in the automotive cybersecurity. To address these issues, this paper proposes a centralized ML model training and decentralized execution-based framework, namely ACHILLES, that facilitates an explainable and generalizable automotive IDS. Under ACHILLES, different ML models can be trained centrally to enhance decentralized and onboard intrusion detection performance with multiple automotive datasets. In addition, we generate standard feature formats to assess the ML model’s generalization efficacy, where the quality of generalization and explainability is evaluated with SHapley Additive exPlanations (SHAP) by identifying the importance of the feature. We also propose a meta-learning scheme to construct suitable ML models trained by the proposed standard feature formats. The proposed feature format exhibits significant performance gain during ML model training and testing with four state-of-the-art controller area network (CAN)-bus datasets containing real, advanced attacks. The experimental results indicate that developing ML models using the generated generalized features and the meta learning-based model building process leads to enhanced performance. In particular, under the dataset cross train-test setting, the proposed feature format enhances the average accuracy by 40.1% for the baseline model, 32.4% for the meta-learned DNN, and 23.6% for the meta-learned Random Forest, compared with the baseline feature format.

Accurate mobile network traffic prediction is crucial for transit infrastructure service optimization in industrial informatization. Traditional linear models fail to capture complex non-linear dynamics, while existing deep learning methods struggle with rapid temporal changes, signal fluctuations, and diverse network conditions, limiting real-time applicability. To address these challenges, this paper proposes a hybrid model integrating Convolutional Neural Networks (CNNs) and Transformers, tailored for High-Speed Railway (HSR) environments. The proposed hybrid model is evaluated using both public datasets and a real-world HSR dataset collected through empirical field measurements, it not only achieves state-of-the-art (SOTA) predictive accuracy, reducing root mean square error by 4.7% over strong baselines in the challenging HSR environment, but also delivers this performance with superior computational efficiency, achieving over 3.6 times lower inference latency than leading SOTA models. This establishes an optimal performance-to-cost ratio, demonstrating its practical value for real-time HSR systems.

Integrating cyber-physical systems and the Internet of Things into industrial operations has significantly improved automation, efficiency, and data-driven decision making. However, these advances have also made industrial environments more vulnerable to cybersecurity risks. Our previous work explored lightweight deep learning models for real-time intrusion detection systems on edge devices, yet these models often operate as black boxes, limiting their trustworthiness. This issue is especially critical in the European Union, where the AI Act mandates transparency, accountability, and human oversight for AI solutions to be interpretable. In this paper, we integrate explainable AI solutions into lightweight real-time intrusion detection systems on edge devices to enhance the transparency and interpretability of black-box models. The study demonstrates that integrating SHapley Additive exPlanations significantly enhances the interpretability of intrusion detection systems, providing more transparent insights into model decisionmaking processes while maintaining accuracy and computational efficiency. This work contributes to the development of more secure and trustworthy industrial ecosystems by improving the effectiveness and reliability of intrusion detection.

Ensuring reliable and predictable communications is one of the main goals in modern industrial systems that rely on Wi-Fi networks, especially in scenarios where continuity of operation and low latency are required. In these contexts, the ability to predict changes in wireless channel quality can enable adaptive strategies and significantly improve system robustness. This contribution focuses on the prediction of the Frame Delivery Ratio (FDR), a key metric that represents the percentage of successful transmissions, starting from time sequences of binary outcomes (success/failure) collected in a real scenario. The analysis focuses on two models of deep learning: a Convolutional Neural Network (CNN) and a Long Short-Term Memory network (LSTM), both selected for their ability to predict the outcome of time sequences. Models are compared in terms of prediction accuracy and computational complexity, with the aim of evaluating their applicability to systems with limited resources. Preliminary results show that both models are able to predict the evolution of the FDR with good accuracy, even from minimal information (a single binary sequence). In particular, CNN shows a significantly lower inference latency, with a marginal loss in accuracy compared to LSTM.

Accurate traffic forecasting in high-speed railway (HSR) systems is hindered by abrupt signal fluctuations and varied mobility scenarios. Conventional approaches that rely on fixed weighted combinations of local and global features are unable to adjust rapidly to real-time changes, resulting in suboptimal performance. To address this limitation, we propose a novel gated guided serial CNN and Transformer network (GsCT) that employs a dynamic combination mechanism implemented via a multilayer perceptron (MLP). In GsCT, CNNs capture fine-grained local variations while Transformers model longrange dependencies, and the adaptive MLP-based gating module adjusts the contribution of each branch based on time-window statistics. This dynamic fusion improves prediction quality by 6.5% compared to conventional fixed weighting mechanisms. Evaluations on both public and real-world HSR datasets demonstrate that GsCT achieves a 2.4% reduction in RMSE relative to LSTM-based methods, and the learned gating coefficients offer transparent interpretability of the feature fusion process. Overall, GsCT provides an effective solution for real-time railway traffic forecasting, paving the way for next-generation HSR services.

With the rapid development of Internet of Vehi-cles (IoV) technology, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication have become an important part of intelligent transportation systems (ITS). In V2V commu-nication, clustering is essential to optimize network efficiency and reliability. Conventional V2V clustering prioritizes the physical distance between vehicles, neglecting key attributes information of the vehicles (i.e., computational capacity, energy consumption) and behavior like relative motion states between vehicles. Thus, this paper proposes a clustering algorithm based on vehicle attributes and behavioral characteristics (C-VABC) that utilizes the Fuzzy C-Means (FCM) algorithm. Once clusters are established, intelligent data routing decisions between clusters is facilitated by a Deep Q-Network (DQN) based algorithm. This algorithm selects neighboring vehicular Cluster Heads with similar mobility states and higher computational power as the next hop vehicles for packet forwarding. The experimental results demonstrate that the proposed clustering algorithm possesses strong adaptability and reliability, while enhancing the communication quality and efficiency in vehicle networking situations. This provides invaluable support for intelligent transportation systems, as well as intelligent and autonomous driving systems.

With the development of intelligent edge computing (IEC) in industrial IoT (IIoT), there is a growing number of service providers trying to leverage computing resources in the cloud and at the edge to meet the users‘ demand for low latency and high reliability in diversified applications. This evolving landscape necessitates innovative approaches to manage and process the vast amounts of data generated by IIoT devices. Among these approaches, distributed learning frameworks, such as federated learning (FL), have emerged as popular solutions. However, compared to computing, communication remains the primary bottleneck that constrains the speed of federated model training. Most of the previous solutions have focused on reducing communication overhead. Differently, we propose a transmission-centric approach by designing an efficient communication archi-tecture for FL with cloud-edge collaboration, specifically aimed at enhancing communication capabilities through multi-path transmission. We deploy this FL system in a real environment and conduct extensive testing. The results demonstrate that the new approach can significantly reduce communication time in FL setting, thereby enhancing model aggregation efficiency and shortening the overall training duration. Compared to conventional single-path transmission, the proposed solution improves training efficiency by up to 26.4%.

In the rapidly evolving cybersecurity landscape, detecting and preventing network attacks has become crucial within the industrial sector. This study aims to explore the potential of intrusion detection by employing deep learning within edge computing, especially for the Industrial Internet of Things. Specifically, TinyML converted CNN, LSTM, Transformer-LSTM, and GCN models on the UNSW-NB15 dataset. A comprehensive dataset analysis gained insights into the nature of attack behavior data. Subsequently, a comparative analysis in an edge computing setup using Raspberry Pi units revealed that the GCN model, with its accuracy of 97.5%, was the best suited of the compared models for this application. However, the study also explored variables like time consumption, where the CNN model was the fastest out of the compared models. This research also highlights the need for continued exploration, especially in addressing dataset imbalances and enhancing model generalizability. By recognizing each model's strengths and areas of improvement, this research serves as a step toward bolstering digital safety and security in an increasingly interconnected industrial world.

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machine learning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios.

In this article, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an electrical vehicle (EV) charging facility for human-driven connected vehicles (CVs) and AVs. The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and autonomous vehicles (AVs). To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data-informed manner. We propose a novel risk adversarial multiagent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. The experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE’s active charging time, and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.

A recent take toward federated analytics (FA), which allows analytical insights of distributed data sets, reuses the federated learning (FL) infrastructure to evaluate the summary of model performances across the training devices. However, the current realization of FL adopts single server-multiple client architecture with limited scope for FA, which often results in learning models with poor generalization, i.e., an ability to handle new/unseen data, for real-world applications. Moreover, a hierarchical FL structure with distributed computing platforms demonstrates incoherent model performances at different aggregation levels. Therefore, we need to design a robust learning mechanism than the FL that 1) unleashes a viable infrastructure for FA and 2) trains learning models with better generalization capability. In this work, we adopt the novel democratized learning (Dem-AI) principles and designs to meet these objectives. First, we show the hierarchical learning structure of the proposed edge-assisted Dem-AI mechanism, namelyEdge-DemLearn, as a practical framework to empower generalization capability in support of FA. Second, we validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions by leveraging the distributed computing infrastructure. The distributed edge computing servers construct regional models, minimize the communication loads, and ensure distributed data analytic application’s scalability. To that end, we adhere to a near-optimal two-sided many-to-one matching approach to handle the combinatorial constraints in Edge-DemLearn and solve it for fast knowledge acquisition with optimization of resource allocation and associations between multiple servers and devices. Extensive simulation results on real data sets demonstrate the effectiveness of the proposed methods.

Recently, with big data and high computing power, deep learning models have achieved high accuracy in prediction problems. However, the challenging issues of utilizing deep learning into the content's popularity prediction remains open. The first issue is how to pick the best-suited neural network architecture among the numerous types of deep learning architectures (e.g., Feed-forward Neural Networks, Recurrent Neural Networks, etc.). The second issue is how to optimize the hyperparameters (e.g., number of hidden layers, neurons, etc.) of the chosen neural network. Therefore, we propose the reinforcement (Q-Learning) meta-learning based deep learning model deployment scheme to construct the best-suited model for predicting content's popularity autonomously. Also, we added the feedback mechanism to update the Q-Table whenever the base station calibrates the model to find out more appropriate prediction model. The experiment results show that the proposed scheme outperforms existing algorithms in many key performance indicators, especially in content hit probability and access delay.

Caching is the key feature of Content Centric Networking (CCN) that allows the Internet Service Provider (ISP) to reduce network traffic crossing its network, and save bandwidth usage cost. On the other hand, it is also on benefit of the Content Providers (CPs) to cache the contents within the ISP network near the consumers. However, caching paid contents (the contents that only paying consumers can access), which are the main source of income for CP, in the ISP network complicates the CP's task of controlling content access and payment. Thus, ISP manages content placement inside its cache-enabled routers and serves content based on user demands, without any coordination with CP. There is no profit sharing mechanism between both ISP and CPs. Therefore, a payment mechanism between ISP and CPs that considers paid content caching and distribution inside the ISP network is needed. To address this challenge, we propose a new incentive mechanism for paid content caching that satisfies both ISP and CPs through the use of reverse auction. The ISP monetizes its cache storage through caching contents from multiple CPs and selling them to its customers. The reverse auction helps the ISP to get prices from multiple CPs, and to select the price that minimize its total payment. The simulation results show that our proposal satisfies all network players involved in in- network caching through increasing their utilities.

Ability of caching the contents is one of the most important feature of an Information Centric Networking (ICN) node. By managing the cache space intelligently we can improve network’s performance and increase users’ Quality of Experience (QoE). Moreover, scalable video streaming in ICN is envisioned to be very beneficial as well as a challenging issue. In this paper, we are proposing a mechanism for cache management and request forwarding policies for scalable video streaming in ICN. Our proposed cache decision policy ensures to cache the base layer of a scalable video near to the users, which is mandatory layer for decoding any SVC encoded video and is needed by all the users with any data-rate budget, and consequently cache the higher layers in the upper nodes in the CCN/ICN within a specific RTT range. Furthermore, our intelligent cache decision cover fairness by considering router’s cache capacity (inside the RTT range) and at the same time giving more priority to the nodes that are nearer to the users. A limited cooperative request forwarding mechanism, which is the part of our proposal, plays a role to improve users’ QoE by providing the popular requested contents quickly. We have intensively simulated our proposed cache management and request forwarding scheme. The simulation results show that our proposed solution outperforms the current cache management schemes and improve the cache utilization. Also our proposed scheme decrease the traffic flowing inside the network by eliminating the request flooding and providing the requested contents from the nearby location to the users. The proposed scheme provides video faster to the users, specially the mandatory base layer is provided very quickly to the users.

Information-Centric Networking (ICN) and Wireless Network Virtualization (WNV) are two emerging technologies for the next-generation network infrastructure. ICN provides the key technology to reduce the network traffic by caching the contents temporarily and aggregating the same content requests. WNV enables the resources sharing among Infrastructure Providers (InPs) and Mobile Virtual Network Operators (MVNOs), to reduce capital expenditures and operating expenses. Also, the network resource management becomes easier because of WNV. In this paper, we combine these two technologies to improve the performance of the network and the profit of the MVNOs. We formulate the optimization problem to solve the cache allocation problem and maximize the profit of MVNOs by controlling the usage of cache space, backhaul link, and radio resources. Finally, we validate our proposed scheme using a chunk-level simulator. The simulation results show that the proposed mechanism can improve the profit of MVNOs and user's QoS.

Information Centric Networking (ICN) is envisioned to be the future Internet architecture and mobile access network e.g., Long Term Evolution (LTE), and 5G will be the major access networks. In this paper, we present a cache management and cooperative request forwarding schemes for Scalable Video Streaming (SVS) in Information Centric Networking (ICN) enabled mobile access networks. H.264/SVC encoded video is consisted a mandatory baselayer and multiple optional enhancement layers. Baselayer, which is enough to decode the video, though with the lowest quality, is needed by every user who want to watch the video while enhancement layers are used to improve the video quality. Only a subset of users download enhancement layers of the video. Therefore, caching the baselayer nearer to the users will increase their Quality of Experience. Furthermore, we introduce cooperative request forwarding for the baselayer of video to take more benefits from cache of neighboring base stations. We have intensively simulated our proposed schemes by extending chunk level simulator ccnsim which is developed over Omnet++. Our experimental results show that, cache hit rate can be improved significantly by adopting our proposed caching and Interest forwarding schemes.

In this paper, we study the relay-aided cooperative modeling that supports the load-sharing in uplink two-tier cellular networks. In our model, users in heavily loaded macrocell are shifted to lightly loaded smallcells with the assistance of relay users to mitigate Signal to Interference plus Noise Ratio (SINR) degradation problem in conventional direct handover. In order to promote relaying data of users which are selfish and rational, a trading exchange model based on Stackelberg game is proposed to optimize strategies of users. Relay users have pricing-based strategies on theirs power unit while shifted heavily loaded macrocell users have strategies to buy power levels of relay users. Optimal strategies are investigated using the backward induction analysis. Specifically, problems of NP-hard combinatorial optimization in relay user selections in the game are solved with a distributed algorithm based on matching theory. We intensively evaluate our proposed model by simulating it in Matlab which shows the efficiency of our proposal.

Internet has grown very rapidly in the last couple of decades and still growing because of the expansion and utilization of various services and applications. Consequently, demand of delay and throughput sensitive services, like audio/video is also increasing. Information Centric Networking (ICN) is proposed as an architecture for the future Internet to meet the modern users and application requirements. In ICN users send requests (Interest Packets) for the Data they need. Interest packet is assigned a lifetime, which greatly affects the Quality of Experience (QoE) because user needs to resend the Interest, when the lifetime expires. Interest lifetime may be expired because of congestion, or Interest lifetime is shorter than the network delay, etc. Waiting for the expiration of an Interest lifetime to resend it is merely appropriate for best effort traffic, rather than services which require high throughput and are delay sensitive. In this paper, we propose Network-Assisted Congestion Control mechanism in ICN, which detects the congestion before it happens, and provides notification to downstream node. On reception of the notification, downstream node continuously reduces the traffic rate. However, when the downstream node fails to adjust the sending rate, the same procedure continues, until the sending node reduces the traffic rate through adjusting its congestion window. We have intensively evaluated our proposal by comparing it with similar proposal using ndnSIM. The experimental results show that our proposal achieves up to 59 percent performance improvement over other proposal in the literature.

Content Centric Networking (CCN) is one of the most promising network architectures of future Internet. In CCN, Content Router (CR) floods request in the network to find the content. This flooding may degrade the network performance by generating too much traffic. Also, in legacy CCN architecture, each CR caches all contents that pass through it. Thus, same contents are replicated in all CRs along the request path, which incurs faster cache replacement and degrades cache utilization consequently. Furthermore, caching and forwarding decisions are made by the CR only on the basis of its local knowledge, which may not be optimum decisions. In this paper we propose a hybrid caching, cache replacement and requests forwarding approaches to overcome the above drawbacks. In our proposal, there is a virtual controller inside the data center with high performance computational capacity. The controller makes all the forwarding and caching decisions and passes it to the physical CRs via virtualized core router. Physical CRs are grouped inside regions on the basis of their geographical location, in order to enhance users' QoE. We have intensively simulated the proposed mechanism in a chunk level simulator and the performance is compared with existing schemes. The simulation results show that the proposed mechanism outperforms the existing state-of-the-art schemes.

In this paper, we optimize resource allocation for multimedia cloud based on queuing model. Specifically, we optimize the resource allocation in both single multimedia service provider (MSP) scenario and multiple MSPs scenario. In each scenario, we formulate and solve the MSPs' revenue maximization problem under eviction probability constraint of users. Numerical results demonstrate that the proposed optimal allocation scheme can optimally utilize the cloud resources to achieve a maximum revenue.

The original Content Centric Network (CCN) employs a simple caching scheme, Leave Copy Everywhere (LCE). However, this scheme is not efficient because cache redundancy reduces the storage capacity of the CCN network. In this paper, to resolve the issue of cache redundancy, we propose a cooperative caching decision and forwarding mechanism which is based on consistent hashing and virtual routers. We divide the Autonomous System (AS) into several groups of routers. The routers in the group cooperatively store the contents (Data) and also forward the requests (Interest) cooperatively in order to increase the caching performance of the CCN network. Finally, we evaluate our proposal by using a chunk-level simulator. The results show that the cache hit ratio of our proposed scheme is better than other proposed schemes.