Performance collapse is an intractable issue of Differentiable Architecture Search (DAS), where severe performance degradation of DAS happens when it trains on different search spaces or datasets. We theoretically analyze the issue from the information bottleneck (IB) perspective, and disclose that a solution to overcome this problem is to seek the bifurcation point of IB tradeoff between compression and prediction of the supernet. To this end, we propose a simple yet highly effective method, namely, Batch Entropy-decay Regularization (BER), to guide the learning of DAS, which restricts compression in DAS by imposing a penalty on the architecture parameters. Comprehensive theoretical analyses demonstrate that BER is able to completely resolve DAS's performance collapse issue. Compared with a number of state-of-the-art DAS variants, BER shows its overwhelmingly better performance on 7 search spaces (i.e., NAS-Bench-201, DARTS, S1-S4, MobileNet-like) and 5 popular datasets (i.e., CIFAR-10, CIFAR-100, ImageNet1k, PASCAL VOC 2007, and MS COCO 2017).

The proliferation of Unmanned Aerial Vehicles (UAVs) in 6G-enabled edge computing presents a dual challenge of ensuring secure data transmission and performing accurate object detection on resource-constrained platforms. This paper proposes a Synergistic Encryption and Detection framework (SED-UAV) that integrates a lightweight chaotic image encryption algorithm, LB-ICA, and an enhanced small object detector, YOLO-LFP. The LB-ICA algorithm leverages an improved chaotic map and a plaintext-aware key mechanism using SHA-256. It achieves a key space of over 2100 and an average encryption speed of 20.3 ms per block, providing robust security against differential and chosen-plaintext attacks with high efficiency suitable for UAVs. For detection, the YOLO-LFP model enhances the YOLOv8 baseline with a hybrid attention module, an adaptive feature fusion strategy, and a dedicated small object head. Experimental results on the VisDrone2019 benchmark show YOLO-LFP achieves a state-of-the-art performance of 44.7% [email protected], significantly outperforming existing models. This work provides a comprehensive solution for deploying secure, real-time computer vision applications on UAV platforms.

The core function of quantum networks is to establish high-fidelity quantum entanglement for long-distance communication. However, the main challenge is to efficiently allocate resources under limited conditions, maximize throughput, satisfy end-to-end (E2E) fidelity requirements, and prevent quantum decoherence caused by inefficient routing algorithms. Current research focuses on optimizing either throughput or fidelity, with a lack of approaches that optimize both simultaneously; furthermore, existing algorithms suffer from high computational complexity. To tackle these challenges, this study proposes a Satisfying Fidelity Threshold Routing and Adaptive Purification Strategy (SFTRAP). SFTRAP maximizes throughput for each request by selecting multiple paths and dynamically choosing links for entanglement purification based on the current state of link resources, thus minimizing throughput loss while satisfying fidelity threshold. The strategy also adaptively adjusts the number of purification rounds according to the fidelity threshold, thereby optimizing the time required for deep purification and enhancing algorithmic efficiency. For multi-request scenarios, SFTRAP employs a priority sorting mechanism that takes into account both path cost and path freedom, which refines request scheduling and path selection to create more efficient request combinations, thus further boosting the overall network throughput. Simulation results indicate that SFTRAP surpasses state-of-the-art methods in terms of both throughput and algorithmic efficiency, highlighting its potential for optimizing resources in quantum networks.

We consider the energy-constrained task allocation problem in large-scale Aerial Edge Computing (AEC) systems, which encompasses a series of tightly coupled decision-making processes, includingwhichtasks need to be processed by unmanned aerial vehicles (UAVs),howto allocate these tasks and balance energy across UAVs for delay-sensitive requirements. However, little attention has been devoted to exploring the above coupled decision-making problem in AEC with various resource and energy constraints, which is further complicated by energy dynamics (UAV battery states), task-specific consumption, and allocation-feedback balance. In this paper, we formulate a multi-dimensional joint optimization problem, simultaneously optimizing task allocation and energy rewarding to maximize long-term system rewards while balancing service quality and energy efficiency. To this end, we propose a green aerial edge computing framework where partial UAVs are equipped with energy harvesting modules to collect ambient energy. To circumvent the intractable computational complexity arising from the coupled energy states of massive UAVs, we design a distributed solution method based on the mean field game, which decouples the dense multi-agent interactions into a game between an individual UAV and the aggregate population state, thereby transforming the complex global optimization problem into a set of equivalent scalable subproblems. We develop an optimal energy valuation scheme to guide UAV behavior. Numerical results show that our mechanism can effectively ensure sustainable system operation while maintaining high quality of service for metaverse users, outperforming existing methods in both system sustainability and service responsiveness.

With the rapid development of Artificial Intelligence Generated Content (AIGC), single data centers are increasingly unable to meet the growing demands for data and computational resources in distributed large-scale language model (LLM) training. In this context, distributed training across heterogeneous data centers has become a necessary choice to enhance computational power and flexibility. However, the networks in heterogeneous data centers are polymorphic, with diverse communication protocols and network architectures. This heterogeneity renders traditional routing devices ineffective in recognizing and processing gradient data. Moreover, frequent copying and excessive parsing of gradient data by routing devices across heterogeneous data centers significantly increase model training time. To address these challenges, we propose XAForward, a method for accelerating distributed LLM in heterogeneous data centers using eXpress Data Path (XDP). Specifically, XAForward introduces a polymorphic-compatible protocol that reconstructs the header of gradient data packets to enable efficient data forwarding across different communication protocols in heterogeneous data centers. Additionally, to accelerate distributed LLM computing and reduce gradient data copying and excessive parsing during training, XAForward leverages kernel-bypass techniques based on XDP for packet processing and kernel-level data forwarding using network index identifiers. Experimental results show that, compared to state-of-the-art methods, XAForward reduces the distributed LLM training time by approximately 35% to 40%.

Computing Power Networks (CPNs) integrate heterogeneous resources across the cloud–edge–end continuum to support wide-area distributed computational services, but the geographical separation of computation and data makes cross-domain data access a major bottleneck. Intelligent cross-domain data orchestration in CPNs is difficult because replica selection and end-to-end path planning must be jointly optimized under Service Level Agreement (SLA) and resource constraints, while each domain observes only partial congestion and resource information. This paper presents AE-MAAC, an attention-enhanced multi-agent reinforcement learning framework that formulates cross-domain data orchestration as a Multi-Agent Markov Decision Process (MMDP) with a hierarchical composite action space and constraint-aware masking under a centralized-training–decentralized-execution paradigm. An attention-based state representation captures heterogeneous cross-domain topology and resource information, an attention-enhanced centralized critic strengthens inter-domain credit assignment in large-scale settings, and parallel dual-policy actors together with a parallel experience ensemble and prioritized sampling improve training stability in large constrained action spaces. Extensive simulations across three CPN scales show that AE-MAAC achieves the highest average episode reward. In the representative 5×10 network, it reaches an average episode reward of 431.7 with a 94.2% request success rate and a 259.8 ms average end-to-end delay, while yielding a lower multi-objective cost than state-of-the-art RL baselines.

The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, In-Network Aggregation is a critical enabling technology. However, its effectiveness is fundamentally undermined by the profound resource heterogeneity of edge networks. Specifically, edge devices, adapting to hardware constraints, operate at varying numerical precisions, leading to significant data inflation as gradients are aggregated. Compounding this, unevenly distributed network resources and traditional, precision-oblivious routing strategies often misallocate critical, high-precision gradients to low-quality paths. This mismatch creates severe network congestion, crippling the efficiency of distributed training. To address this, we propose the Precision-Aware Hierarchical In-Network Aggregation (PAHInA) framework, the first, to our knowledge, to perform routing optimization for in-network aggregation that explicitly considers precision heterogeneity. The core of PAHInA is an intelligent control-plane scheduler that co-optimizes for gradient priority and path cost, dynamically planning the most cost-effective aggregation strategy for each flow. This fine-grained scheduling guarantees that high-priority gradients are routed through premium, low-latency paths, minimizing global communication overhead. On the data plane, we leverage the eXpress Data Path (XDP) for high-performance packet processing to reduce aggregation-induced overhead. Extensive simulations show that, compared to state-of-the-art baselines, PAHInA significantly mitigates network congestion, reducing end-to-end communication time by up to 33% and boosting overall training throughput by approximately 30%.

Sharding, as a Layer-1 scaling technique, is widely recognized as a promising solution to address the scalability limitations faced by blockchains. However, distributing accounts across shards generates numerous cross-shard transactions (CSTs) and inter-shard workload imbalances, potentially compromising scalability. Existing state-of-the-art methods typically optimize transaction distribution for subsequent epochs by periodically reallocating accounts using graph partitioning or community detection to balance loads and minimize CSTs. Nevertheless, these methods often involve computationally expensive account migration and overlook real-world transaction skewness, exacerbating workload imbalances. To address the above problems, we propose a two-phase account group migration service with dynamic load awareness in this paper. This service can monitor and analyze system state to determine the necessity of account migration. Once triggered, it employs a two-phase algorithm that combines account grouping with global group-level re-partitioning to balance workloads and minimize CSTs. Additionally, group-level migration further helps reduce computational overhead. We evaluate the designed service by replaying large-scale real Ethereum transactions. Experimental results demonstrate that, compared with the baselines, our method can not only improves system throughput and reduces transaction confirmation latency, but also enhances overall scalability.

Accurate encrypted malicious traffic detection is crucial for improving network service quality. Existing methods leverage the widespread application of machine learning (ML) to distinguish encrypted malicious traffic from normal traffic by learning the statistical characteristics of traffic. However, the scarcity of high-quality annotated encrypted malicious traffic data, especially malicious traffic samples, limits the performance of these supervised learning methods. Additionally, annotating network traffic is challenging as it requires domain-specific expert knowledge. Therefore, this paper proposes an encrypted malicious traffic detection framework based on the active learning method. This framework achieves high recognition rates using a limited number of samples. It employs a hybrid weighted uncertainty sampling strategy that utilizes the independence coefficient method to weight uncertainty measurements across multiple scales. This improves the reliability during the automatic instance selection process. In the experimental section, we achieved a detection accuracy exceeding 93 % using a data subset comprising only 1 % of the original dataset. Furthermore, we validated the robustness of the proposed framework through calibration rate measurements in the experiments.

Latency-sensitive services across the end-edge-cloud continuum require not only low mean latency but explicit control of tail latency and data freshness. We propose Control-Loop Freshness-aware Service Function Chain orchestration (CLFSFC), a freshness-aware orchestration framework for Service Function Chains (SFCs) that jointly selects model variants and function placements. We define a Control-Loop Freshness (CLF) objective that combines 95th/99th-percentile (P95/P99) end-toend latency with an Age of Information (AoI) proxy. To make this objective operational under uncertainty, we allocate per-stage risk budgets via the union bound and convert mean/variance profiles into percentile constraints using Cantelli's inequality, yielding a two-stage greedy solver with interpretable quotas. We implement CLF-SFC with offline profiling of YOLOv5 n/s/m variants across end/edge/cloud devices, and evaluate it with profiling-driven measurements on COCO 2017 under synthesized network regimes. Across bandwidth and round-trip time settings, CLF-SFC reduces P95/P99 latency and Service Level Objective (SLO) violations relative to Edge-only, Cloud-only, and a riskagnostic heuristic; at high bandwidth it remains comparable to the Shortest-Latency-Path (SLP) baseline. The proposed CLF-SFC framework naturally fits embodied-AI pipelines where perception, fusion, and policy modules operate in a closed loop. By explicitly incorporating the Age-of-Information (AoI), our orchestration ties data freshness to control stability, complementing tail-latency minimization.

To address the challenge of data acquisition in unknown disaster environments — such as earthquake zones, flood-affected regions, or industrial accident sites — this paper proposes a Hybrid Centralized - Distributed Collaborative Coverage (HCC) strategy for UAV swarms. The HCC framework integrates centralized trajectory planning with distributed obstacle avoidance to achieve rapid, efficient, and safe coverage of Points of Interest (PoIs). Specifically, a Centralized Collaborative Optimization (CCO) strategy is designed to compute secure cooperative coverage schemes on an edge server, while a Distributed Obstacle-avoidance Coverage Strategy (DOCS) enables each UAV to perform safety-aware navigation based on local sensing. Unlike sequential exploration, the proposed method supports parallel and synchronized coverage execution, ensuring that data acquisition across the entire region occurs concurrently. Simulation results demonstrate that the proposed method outperforms benchmark algorithms in terms of coverage rate, safety, energy efficiency, and network lifetime.

Blockchain is increasingly integrated in Multiaccess Edge Computing (MEC) to coordinate secure and lowlatency resource provisioning and service orchestration among resource-constrained embodied AI devices. However, conventional blockchains perform costly transaction verification during propagation, which can be exploited by spam transaction attacks and overload resource-limited edge devices. To mitigate the substantial overhead of verification, we propose a two-stage BLS multi-signature backed transaction propagation mechanism for blockchain-enabled MEC: a small-scope random-walk phase with deep verification and signing, followed by a large-scope propagation phase with probabilistic verification. In the first stage, nodes conduct deep verification and sign valid transactions using the BLS multi-signature, then forward the signed transaction to a small and randomly sampled subset of neighbors to rapidly accumulate valid signatures. In the second stage, transactions whose aggregated signature count exceeds a threshold will be broadcast throughout the entire blockchain network and undergo deep verification with a specific probability, relieving edge nodes' verification burden. Moreover, verifiers record signers associated with failed deep verifications. Signers whose failures exceed a system threshold are quarantined to restrain the spread of spam transactions. Experimental results demonstrate that the proposed mechanism reduces energy consumption by at least 60% and 18.6% compared with the original and benchmark mechanisms respectively, while maintaining nearly identical transmission performance and ensuring that the proportion of invalid transactions propagated to honest nodes does not exceed 14%.

The rise of Embodied AI, including autonomous robots, cooperative autonomous driving, and augmented reality agents, is driving a deep integration of intelligent systems with the physical world, imposing stringent demands on the underlying network for real-time, low-latency interaction. However, these Embodied AI systems typically operate in complex polymorphic network environments, simultaneously handling heterogeneous identifiers such as content, IP, and geographic location. This causes traditional routing mechanisms to suffer from significant latency overhead and compatibility bottlenecks due to protocol conversion and adaptation, severely limiting the performance and responsiveness of Embodied AI applications. To address this challenge, we propose the Polymorphic Compatible Segment Routing (PCSR) protocol. PCSR adopts an innovative paradigm of decoupling the protocol from the infrastructure. It dynamically maps native protocol semantics to lightweight 8 -byte identifiers and utilizes compatibility logic encapsulated at the packet tail to achieve smooth compatibility with traditional networks without requiring large-scale modification of existing equipment. Furthermore, we built a zero-copy forwarding engine using the kernel eXpress Data Path (XDP) technology to fundamentally optimize data transmission efficiency. Experimental validation on a 10-node heterogeneous testbed shows that PCSR reduces end-toend latency by 32.7% and maintains a high throughput rate in hybrid network environments. This work demonstrates that PCSR provides an efficient and deployable routing solution for latency-critical, cross-domain collaborative services required by Embodied AI.

Beyond 5G-enabled edge computing networking (ECN) will further deploy computing and communication resources to the edge of the networks. Then, edge service demands for Internet of Things (IoT) applications are becoming more and more diverse, while the corresponding routing service capability is limited and not flexible enough to deal with the demands of ECN, which then leads to reducing the inherent routing capability of ECN. It becomes extremely difficult for ECN to support diversified demands and provide diverse IoT applications quickly and flexibly. In this article, we propose a novel and customized deep routing mechanism for IoT applications in ECN, in which the network slicing and deep learning methods are jointly applied and leveraged. First, we design a new ECN architecture that formulates four kinds of network slices to cope with various IoT scenarios, which are eMBB, uRLLC, mMTTC, and backup slices. Second, using these slices, we can customize the ECN environment flexibly, based on which we propose the corresponding routing method for the purpose of fast and efficient service delivery. In particular, the mapping between network slices and the infrastructure is established with the object of maximizing the resource utilization. Then, the routing is designed and customized by using the deep learning model. Lastly, the experimental results show that the deep customized mechanism designed in this article can reduce the average loss rate of the model, decrease the average delay, as well as improve the average resource utilization compared with the existing studies.

The growing prevalence of cardiovascular diseases (CVDs) underscores the critical need for accurate, real-time monitoring and personalized diagnostics in cardiac healthcare. This paper presents DeepCardioNet, an innovative deep learning framework integrated with Internet of Things (IoT) devices, designed to address the limitations of existing cardiac health monitoring systems. The proposed framework leverages multimodal physiological data, including electrocardiogram (ECG), heart rate variability (HRV), and blood pressure (BP), to provide comprehensive and precise diagnostics. DeepCardioNet incorporates a multi-stream convolutional neural network and transformer architecture, enhanced with an attention mechanism for dynamic feature prioritization. In addition, we optimize the model through techniques such as pruning and quantization to support the possibility of real-time edge AI deployment, ensuring efficient inference on resource-constrained IoT devices. Experimental results on the MIT-BIH arrhythmia database and MIMIC-III waveform database demonstrate the superiority of DeepCardioNet over state-of-the-art methods, achieving an accuracy of 93.6% and an F1-score of 91.8%. Extensive ablation studies validate the contributions of key architectural components, including multimodal data fusion and attention mechanisms. The findings highlight the framework’s robustness and suitability for real-world cardiac health monitoring applications. By integrating advanced AI techniques with IoT capabilities, DeepCardioNet represents a significant step toward achieving personalized and precise healthcare in the context of the healthcare industry.

The growing concern regarding the environment and the compounded nature of healthcare-related emergencies has impacted the effectiveness of medical supply chain management. As a rule, demand forecasting techniques do not synchronize the altitude of medical need with the level of environmental care throughout a public health crisis. This study presents MedSC, a deep learning framework for dynamically forecasting emergency medical supply requirements within an AIoT green supply chain environment. The framework features advanced fuzzy clustering with long short-term memory) prediction models in medical supply prioritization using AIoT sensor data and integrating metrics of environmental care. MedSC determines the relevance and importance of medical supply through eco-friendly hierarchical clustering for prioritized grouping. It then devises unique forecasting models for non-interconnected and interconnected supply chain subsystems. The results of experiments conducted at three major medical distribution centers validate the claims in the hypothesis, proving that the framework outperforms traditional forecasting methods and even sophisticated contemporary approaches. These performed exceedingly well on ultra-low latency real-time predictive bounds, claiming an industry record in fast predictive bounds, holding double the precision while throttling emissions by 28.5%. The outstanding attribute of the framework is the balance of healthcare needs and environmental care, which improved energy per unit work by 91.3% while 89.7% improved resource per unit work.

The optimization of modern healthcare facilities presents unique challenges at the intersection of medical service quality, energy efficiency, and environmental impact. By integrating carbon-intelligent Industrial Internet of Things (IIoT) technologies with healthcare operations, our approach enables real-time monitoring and optimization of carbon emissions while maintaining medical service quality. Specifically, this paper presents a novel deep reinforcement learning-based multi-objective optimization algorithm (HC-DMOPSO) for IIoT-enabled healthcare building management. By integrating healthcare-specific constraints with an enhanced swarm intelligence framework, our approach optimizes building operations while considering medical device power demands, patient comfort, and environmental requirements. The proposed algorithm combines dual-distance metrics -population average distance and crowding distance -with deep Q-networks to effectively explore the complex solution space. Experimental results demonstrate HC-DMOPSO’s superior performance across multiple metrics: The integration of carbon intelligent IIoT sensors and actuators enables HC-DMOPSO to achieve 24.8% reduction in energy consumption while maintaining 99.92% medical power reliability, 32.5% decrease in peak load with only 0.38∘C average temperature deviation, and 28.7% improvement in carbon reduction compared to baseline methods.

The rapid development of the Internet of Things (IoT) and wireless communication technologies has enabled the realization of vehicle-road cooperative systems. However, the vast amount of data generated by IoT devices in these systems poses challenges for traditional data processing methods. Augmented intelligence, such as deep reinforcement learning (DRL), has emerged as a powerful solution for processing large-scale real-time data and making accurate decisions. This article proposes an edge-cloud framework for vehicle-road cooperative traffic signal control in the context of Augmented IoT (AIoT). The framework integrates an edge-cloud collaborative resource allocation algorithm based on DRL and a traffic signal timing method that combines DRL with an extended Kalman filter. Simulation results demonstrate the effectiveness of the proposed framework in improving traffic efficiency and reducing vehicle waiting times. The average queue length was reduced by 35.7%, and the average waiting time increased by 29.1%. The proposed edge-cloud framework for vehicle-road cooperative traffic signal control in AIoT provides a promising solution for enhancing traffic management in smart cities.

With the rapid advancement of technologies such as B5G/6G and edge computing, network scenarios are becoming increasingly complex and diverse, leading to the emergence of slicing networks. Virtualizing applications into distinct categories and establishing corresponding network slices ensures performance to a certain extent. However, the challenges posed by the complex slicing environment demand more fine-grained routing control and higher costs to locate requested content or services, areas where current state-of-the-art methods fall short. To address these challenges, this work introduces a system framework that integrates the principles of Segment Routing over IPv6 (SRv6). An SRv6 optimization layer is created between the control and infrastructure layers to manage slices effectively and enhance routing control. Additionally, we propose a novel policy routing method based on zero-trust and Graph Convolutional Network (GCN) technology. This method transforms actions into policies that can be flexibly deployed on SRv6 nodes, segment by segment. These actions encompass both routing and security measures, allowing for dynamic and flexible deployment of policies on each segment to achieve the desired goals. This integration of segment routing and zero-trust principles simplifies implementation and enhances security. Comprehensive experiments were conducted to evaluate the proposed method. The results demonstrate significant improvements over state-of-the-art methods, including a higher service acceptance rate, better resource utilization, and reduced average latency and packet loss rate.

Fuzzy-rough sets-based feature selection (FRFS), as an effective data pre-processing technique, has drawn significant attention with the growing prevalence of large-scale datasets. However, centralized FRFS approaches suffer from the following shortcomings: 1) low computational efficiency, 2) bottlenecks in memory and computational resources, and 3) strict limitation of collaborative implementation using nonshared datasets owned by different data providers. These limitations highlight the growing necessity of integrating FRFS into a distributed FS framework. Nevertheless, most existing distributed FS schemes are reliant on a designated central server to collect and merge the local results from all slave nodes, which may result in several challenges including single point of failure risk, lack of trust and reliability, and lack of transparency and traceability. To relieve the above issues, this paper proposes a blockchain assisted distributed FS framework, successfully implementing a distributed solution for FRFS (BDFRFS). Firstly, this framework introduces blockchain to merge, reach consensus and publish the global results generated during each iteration of FRFS, including the currently selected feature subset with its corresponding similarity matrix and dependency degree. This not only eliminates the reliance of central server and alleviates the burden on the central server, but also enhances the credibility and traceability of the results. Additionally, the implementation of FRFS is designed within this framework, utilizing three strategies to improve the efficiency of centralized FRFS: 1) eliminating the irrelevant and redundant features prior to the executing FRFS; 2) removing redundant and unnecessary computations involved in generating the similarity matrices; and 3) enabling parallel computation of dependency degrees. Finally, the experimental results conducted on eight large-scale datasets demonstrate that the proposed framework can significantly reduce the runtime cost and improve the classification accuracy compared to centralized FRFS and several distributed FS approaches.

In the era of large-scale machine learning, largescale clusters are extensively used for data processing jobs. However, the state-of-the-art heuristic-based and Deep Rein-forcement Learning (DRL) based job scheduling mechanisms are facing challenges such as slow training speed and underexploitation of jobs' complex dependencies. We propose MPDA, a Massively Parallel learning and Dependency-Aware scheduling algorithm, consisting of a fast-training mechanism and a novel dependency-aware policy network, GATNetwork, to address these two challenges respectively. The fast-training mechanism is a two-level massively parallel training method that can significantly accelerate the training process and maximally utilize the resources of the cluster. Additionally, its decoupled learning and interacting design enables hybrid-workload training for MPDA, which guarantees the generalization and robustness of MPDA. The GATNetwork exploits the dependencies among stages/jobs using Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) networks to improve the performance of the scheduling policy. The experiments show that MPDA accelerates the training speed by one to two orders of magnitude and achieves better scheduling performance, i.e., lower average job completion time, compared with existing scheduling algorithms.

Traditional single-cloud storage suffers from single points of failure, leading to low data availability. As a result, it fails to meet users' demands for reliable cloud storage services. Therefore, the current cloud storage paradigm has shifted to distributed-cloud storage (e.g., multi-cloud storage, JointCloud storage), where users store multiple replicas of data across multiple Cloud Service Providers (CSPs). However, this imposes significant storage pressure on CSPs. To reduce costs and maximize profits, some malicious CSPs may delete user data, undermining trust in cloud services and hindering the growth of the cloud computing industry. To address this issue, we propose a novel distributed-cloud storage based on permissioned blockchain, which effectively reduces storage costs while ensuring data availability. Firstly, we integrate Byzantine Fault Tolerance in permissioned blockchain with erasure coding (EC) to replace the traditional multi-cloud multi-replica storage approach. This integration significantly reduces storage costs while providing an efficient means for data recovery. Based on blockchain, we further propose a data integrity auditing approach that eliminates reliance on semi-trusted third-party auditors and enables decentralized data integrity verification. Combined with this auditing approach, our EC-based data recovery approach ensures data availability while enhancing users' trust in distributed-cloud storage. Theoretical analysis indicates that our scheme reduces storage overhead from$O(n)$to$O(1)$with$n$CSPs while ensuring data availability. Meanwhile, experimental results demonstrate that computational overhead is reduced by approximately 78% compared to traditional multi-cloud multi-replica storage, achieving the cost-effective and highly reliable distributed-cloud storage.

Blockchain, a promising distributed ledger for decentralization, plays a crucial role in JointCloud computing, offering secure data storage and sharing as well as reliable transaction tracking and resource allocation. While promising, it faces storage limitations due to its full-replication strategy. This issue has been addressed by scholars through storage partitioning mechanisms like BFT-Store and PartitionChain. These mechanisms leverage Erasure Coding with the Byzantine Fault Tolerant consensus protocol to overcome storage constraints. However, challenges persist: i) high computational complexity in encoding and decoding leading to prolonged computation time; ii) extensive use of verified signatures causing increased network message transmission and communication burden; iii) during system re-initialization, under-performing scalability and substantial time consumption due to the involving of all nodes in data decoding and re-encoding. To overcome these challenges, we propose two novel storage partitioning mechanisms (i.e., GPartition-Store and ParallelC-Store) for the permissioned blockchain that reduce the computational complexity of the coding processes by dividing nodes into multiple groups called storage units (SUs), avoid additional communication of generating verification proofs by employing the Bloom Filter, and improve the stability and scalability of the system by implementing the re-initialization process exclusively within a specific SU. Particularly, the computational complexity of encoding/decoding can be further degreased by about g2/g3and g/g2compared to PartitionChain, by GPartition-Store and ParallelC-Store respectively (g is the number of SUs). Compared with the full-replication strategy, BFT-Store and PartitionChain, the experimental results demonstrate that the proposed mechanisms improve the Quality of Service (QoS) of the blockchain system including performance (i.e., efficiency and throughput), scalability and stability, while guaranteeing the availability.

The Fifth-Generation mobile networks (5G) and Beyond 5G (B5G) have been proposed to support a variety of application scenarios, such as enhanced Mobile Broadband (eMBB), ultra-Reliable Low-Latency Communications (uRLLC), and massive Machine Type Communications (mMTC). On the other hand, Mobile Edge Computing (MEC) and Network Functions Virtualization (NFV) technologies have been widely advocated by service providers to meet diverse service demands and reduce operational costs. To alleviate the pressure on the edge network, resource consumption can be minimized by considering the reuse of Virtual Network Function (VNF) instances. However, implementing VNF chain deployment with latency guarantees and resource efficiency in a distributed network architecture remains an urgent issue to be addressed. In this paper, we explore the Service Function Chains (SFCs) orchestration problem with distributed edge network resources, aiming to design efficient service flow routing and resource allocation schemes to significantly respond to local user requests. We propose a low-complexity Distributed SFCs Orchestration algorithm with VNF Reuse (DSOR), which initially uses local information at the edge to explore the VNFs orchestration scheme and executes the distributed service orchestration. Subsequently, service chains are deployed based on asynchronous consensus to enhance network utility and reduce resource costs. Finally, the performance of DSOR is evaluated through extensive simulation experiments. The experimental results indicate that DSOR can improve the utilization of network resources, as well as the response rate to edge service requests.

The emergence of metaverse applications and services heralds a new era of immersive driving experiences in future vehicular ad hoc networks (VANETs). This unprecedented metaverse-infused driving experience, however, requires high-throughput transmissions for real-time high-quality 360° video streaming, which challenges today’s limited bandwidth resources provisioned by wireless communication infrastructure. To this end, this article introduces virtual road, real connection (VRRC), a novel framework for enhancing the metaverse-infused driving experience in VANET through the Internet of Robotic Things (IoRT). In the face of limited bandwidth resources, VRRC effectively addresses the challenge of high-throughput demand from two key perspectives. First, VRRC reduces redundant transmission by implementing a graph neural network (GNN)-based vehicle clustering method for dynamic multicast group formation, taking into account both the geographical status of vehicles and their communication patterns. Second, VRRC aggregates bandwidth resources across various channels by employing a multiagent reinforcement learning (MARL)–based multipath packet scheduling policy to adapt to heterogeneous channel conditions and dynamic vehicular mobility. Extensive experiments with real-world vehicular traces validate the effectiveness of VRRC and demonstrate its outperformance in reducing redundant traffic by 54% and improving overall throughput by 28%. VRRC represents a substantial leap forward in the integration of the metaverse experience into VANET.

Fuzzy-rough sets (FRS) provide an outstanding theoretical tool for feature selection (FS). Whilst promising, the FRS model is sensitive to noisy information and ineffectively applicable to the data with large class density difference, with existing FRS-based FS methods only tackling one of these challenges. Therefore, to overcome both of these issues, this article presents a robust FS algorithm using linear reconstruction measure for the first time. First, a pseudo FRS model is proposed, where the distribution-aware linear reconstruction relation serving as the fuzzy similarity relation is constructed by considering the insight of meaningful information (i.e., distribution information of samples and density information of classes) to enhance the robustness and the pseudofuzzy rough approximations are further redefined based on$k$-Nearest Neighbor ($k$NN) granules determined by the linear reconstruction coefficients to empower the antinoise ability. Then, the pseudo FRS model is employed to guide the robust FS algorithm from the perspective ofredundant filter,strongly relevant priority, anddiscriminative selectionto determine the final feature subset. The experimental results on 31 datasets and practical applications (i.e., cancer diagnosis and face recognition) demonstrate that the reduct gained by the proposed approach generally outperforms those attained by alternative implementations of FRS-based FS and state-of-the-art FS techniques.

The current global economy is undergoing a transformative phase, emphasizing collaboration among multiple competing entities rather than monopolization. Economic globalization is accelerating the adoption of globalized cloud services, and in line with this trend, cloud 2.0 introduces the concept of “cloud cooperation”. JointCloud, as a novel computing model for Cloud 2.0, advocates for the establishment of an evolving cloud ecosystem. However, a critical challenge arises due to the lack of direct incentives for a cloud to join the JointCloud ecosystem, leading to uncertainty regarding the rationale for the existence of the JointCloud ecosystem. To address this ambiguity, we draw inspiration from supply chain competition and formulate the market dynamics of resources within the JointCloud ecosystem. Our focus is particularly on the analysis of data resource trade within the JointCloud market. To comprehensively analyze the JointCloud market, we propose a market game that examines the competition among clouds within the ecosystem. We theoretically prove that a Nash Equilibrium always exists under the JointCloud market. Subsequently, we conduct an in-depth analysis of the profits of cloud resource manufacturers and cloud resource retailers as the number of clouds varies within the JointCloud ecosystem. Based on our analysis, we further explore the incentives for a cloud to participate in the JointCloud ecosystem. We then evaluate the performance of the proposed market game through extensive experiments, illustrating how process variables and profits change with the market size. The experiments demonstrate that the trends of various variables are aligned with our analysis obtained from the market game. Compared with the Cournot model, our proposed model captures the market power of both manufacturers and retailers, resulting in a model that closely mirrors real market dynamics. Our findings provide valuable insights into the cloud market within Cloud 2.0, offering guidance for stakeholders navigating the evolving landscape of cloud cooperation and competition.

Network Function Virtualization (NFV) provides a flexible way to provision new services by decoupling network functions from hardware and implementing them as Virtual Network Functions (VNFs). However, the rapid development of technologies greatly promotes the explosion of diverse services, which directly results in the exponential increase of heterogeneous traffic. In addition, such a tremendous amount of heterogeneous traffic will generate bursts in a more dynamic and unexpected manner, so it becomes extremely hard to satisfy the customer demands. Aiming at addressing these challenges, this work proposes a positive and elastic VNF deployment mechanism for service provisioning, which introduces three novelties:1) a Gated Recurrent Unit (GRU) based traffic prediction model is established to predict the unexpected and dynamically changing traffic behaviors in advance with the accuracy over 98%; 2) a closed-loop system is formed, in which the prediction model can learn and evolve continuously to respond to more complex scenarios; 3) different states of VNF are introduced and dynamically switched to deal with the current demands with reduced cost by avoiding frequent VNF initialization and destroy.The experimental results indicate that the proposed mechanism outperforms the state-of-the-art methods, which include achieving over 98% prediction accuracy, improving the service acceptance rate by more than 18%, and reducing the overall cost by more than 20%.

Applying the Multiple Drones System (MDS) to perform the repetitive and dangerous tasks for human in many complex environments has become a trend all around the world, due to the increasing capacity of mobile communication and the increasing intelligence of drone robots. However, to fulfill the target with less cost as much as possible, drones need to collaborate deeply with each other to make the optimal decision, which is now important and challenging. In this work, we focus on addressing the efficient task allocation among large-scale drones with the object of minimizing the resource waste and cost, which is proved to be NP-hard. Specifically, we first introduce the Digital Twin (DT) technology to dynamically construct the spatial structure for drones, in which a density clustering based algorithm is proposed to decompose the large-scale task allocation problem among all drones into smaller sub-problems among partial drones. Then, for each sub-problem, we propose an improved auction algorithm to allocate the sub-tasks to local drones according to the task difficulty and drone ability. The experimental results indicate that the proposed method outperforms the state-of-the-art methods in terms of the moving distance, resource utilization and task completion time, etc.

With the rapid development of network function virtualization (NFV), more and more enterprises and operators are seeking network service provisioning via service chains of virtual network functions (VNFs), instead of depending on proprietary hardware appliances. Following this trend, an NFV market is emerging, where users can procure different VNF instances (VNFIs) and their combinations from multiple network service providers (NSPs) in a pay-as-you-go way. In such a procurement process, how to guarantee truthfulness while enabling flexible resource provisioning in form of VNFIs is a significant challenge. In this paper, we propose a truthful reverse combinatorial auction-based mechanism to solve the combinatorial VNFI procurement problem. To support flexible resource provisioning, this mechanism allows NSPs to be multi-minded, and determine the provisioning VNFIs according to the auction results. Specifically, we design a heuristic algorithm to determine the winning bids in polynomial time. Furthermore, we devise a critical-payment-based pricing algorithm to induce NSPs to disclose their real costs, aiming to achieve truthfulness. Rigorous theoretical analysis shows the proposed mechanism can guarantee truthfulness, individual rationality and computational efficiency. Simulation results also verify the effectiveness and efficiency of the proposed mechanism.

The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration among mobile edge devices, which is a lack of research currently. In this article, we focus on addressing the computation migration oriented resource allocation problem among mobile edge devices. Specifically, we first construct a framework for Mobile Social Cloud(MSC), in which the mobile devices with rich resources are abstracted as resource suppliers and those resource-lacking devices are abstracted as resource demanders. Then, a mathematical model is formulated and an evolutionary algorithm is proposed to effectively solve this model based on decomposition, dominance, and genetic operations. Moreover, the parallel computing is introduced to further improve the efficiency of the proposed algorithm. The experimental results indicate that the proposed algorithm outperforms the other state-of-the-art methods and it improves the calculation efficiency by about 178 percent (2 cores) and 262 percent (3 cores) by introducing parallel computing.

Information Centric Networking (ICN) has the characteristics of supporting mobility naturally. To make the most of 5G and ICN, this work intends to propose a 5G-enabled and mobility supported routing based on ICN to satisfy the requirements of various new network applications. In particular, the corresponding routing method is designed leveraging the ant swarm behavior. Specifically, we firstly formulate a corresponding networking model, during which four node tables are designed for routing. Then the routing method is designed based on the ant swarm behavior. Lastly, the experiments are carried out over DFN topology and the results indicate that the proposed method can significantly improve the performance.

In order to support the 5GB5G network with huge connections, large traffic, and low latency, the evolution of the IP network to the IPv6 network is an inevitable trend. Although IPv6 network has significantly improved network performance, there are still a series of network failures. After the failure occurs, it is necessary for the routing algorithm to create a backup path to ensure the continuous provision of network services. So, the selection of the backup path directly determines the performance of the network service. SRv6 takes advantage of the programmable capability of the IPv6 extension header to implement differentiated reliable routing, satisfies the QoS requirements of different network services, and improves user experience. First, we design the network model for single link failure, define the network service, and design the two-dimensional flow table. Then, the backup path traversal algorithm is designed to obtain the universal set of backup paths in the network, and the optimal backup path for a specific network service is selected through comprehensive evaluation method. Experimental results show that the differentiated reliable routing mechanism proposed in this paper exhibits better performance in terms of bandwidth, delay, jitter and packet loss rate compared with the benchmark mechanism.

Abstract 5G cellular IoT has several advantages compared to other access technologies, enabling operators to serve a wider area and more IoT devices. However, in the urban transportation system, a massive number of vehicles exhaust the available resources in the cell, resulting in excessive load in the 5G cellular network. This article proposes a novel reinforcement learning based V2V routing (RLbR) framework, which offloads non‐realtime traffic into the V2V network and significantly relieves the load of 5G cellular network. Meanwhile, we propose a V2V routing algorithm. Specifically, the Q ‐values of neighbouring vehicles are firstly calculated according to the cache factor CF and energy factor EF and evaluate the quality of neighbouring vehicles. Then, the position factor PF is calculated, based on which, the vehicle forwards the data packet. In addition, an environment model is designed to accelerate the convergence of Q ‐table. The results show that the RLbR framework brings the highest offload rate compared to the other three frameworks, and simultaneously, the proposed algorithm improves the lifetime of the V2V network and performs well in terms of delivery ratio and average delay.

Abstract Best‐effort service model of traditional routing is gradually hard to meet the personalized demands under the rapid development of network technologies (e.g. 5G and IPv6). Therefore, service customization should be considered. In this work, a service customized routing mechanism based on deep learning in IPv6 network is proposed, which includes deep learning‐based service customization module, reliability evaluation module, and routing calculation module. The first module uses neural network to learn the complex service customization function, which can quickly output win‐win customized service strategies based on user demands. The second module can quantify the reliability of service routing paths, where not only the link status of IPv6 Neighbor Unreachable Detection (NUD) is considered, but also propose link performance weights to ensure the reliability of differentiated service performance. The third module uses the gray wolf optimization algorithm to calculate an optimal routing path to forward services with the customized strategies as the constraints and the maximum reliability and minimum cost as the goal. Finally, the mechanism is tested on the IPv6 Source Address Validation Improvement (SAVI) platform, which can reduce the execution time by 12.25% and improve the average routing reliability, user and ISP satisfaction by 9.0%, 40.45% and 7.4%, respectively.

Computation offloading decisions play a crucial role in implementing mobile-edge computing (MEC) technology in the Internet of Things (IoT) services. Mobile network operators (MNOs) can employ computation offloading techniques to reduce task completion delay and improve the Quality of Service (QoS) for users by optimizing the system’s processing delay and energy consumption. However, different IoT applications (e.g., entertainment and autonomous driving) generate different delay tolerances and benefits for computational tasks from the MNO perspective. Therefore, simply minimizing the delay of all tasks does not satisfy the QoS of each user. The system architecture design should consider the significance of users and the heterogeneity of tasks. Unfortunately, rare work has been done to discuss this practical issue. In this article, from the perspective of MNO, we investigate the computation offloading optimization problem of multiuser delay-sensitive tasks. First, we propose a new optimization model, which designs different optimization objectives for the cost and revenue of tasks. Then, we transform the problem into a Markov decision processes problem, which leads to designing a multiagent iterative optimization framework. For the strategic optimization of each agent, we further propose a cooperative multiagent deep reinforcement learning (CMDRL) algorithm to optimize two different objectives at the same time. Two agents are integrated into the CMDRL framework to enable agents to collaborate and converge to the global optimum in a distributed manner. At the same time, the priority experience replay method is introduced to improve the utilization rate of effective samples and the learning efficiency of the algorithm. The experimental results show that our proposed method can effectively achieve a significantly higher profit than the alternative state-of-the-art method and exhibit a more favorable computational performance than benchmark deep reinforcement learning methods.

Competitive opinion maximization (COM) in signed social networks targets at selecting a subset of influential individuals (i.e., seed nodes), spreading the desired opinions of the product to their neighbors against its opponents, and eventually achieving the maximum opinion propagation. Current studies mainly focus on competitive influence maximization and opinion maximization. However, COM in signed social networks has not been studied in depth. In this article, we study the COM in signed social networks and propose a novel reinforcement-learning-based opinion maximization framework (RLOM) to solve the COM problem. The proposed RLOM is composed of two phases: the activated dynamic opinion model and the reinforcement-learning-based seeding process. We theoretically prove the COM problem to be NP-hard. To model the opinion propagation process, we propose the activated dynamic opinion model based on a stateless Q-learning approach. Moreover, we propose the reinforcement-learning-based seeding scheme, which is leveraged in an unknown opponent strategy. Experiment results verify the effectiveness of our method in terms of effective opinions on three signed datasets.

Network service provisioning becomes flexible and programmable with the help of Network Function Virfitualization (NFV), since NFV abstracts various service functions into software components called Virtual Network Function (VNF) and VNFs can be flexibly and quickly composed to form new services. It is commonly known that sharing the same VNF among different services can improve the resource utilization. However, we should be aware that such sharing also leads to serious resource preemption. In addition, VNF sharing aggravates the generation of the performance bottleneck, which then causes the rate mismatch problem between the upstream and downstream VNFs belonging to the same service chain. In this article, we propose a dynamic and flexible algorithm to jointly address the VNF sharing resource allocation and the rate coordination between the upstream and downstream VNFs. Specifically, 1) the VNFs are shared among different service chains with a fairness factor considered for the purpose of reducing the resource preemption probability and improving the resource utilization; 2) the backpressure indicator of each VNF is defined to judge its pressure condition, based on which we can dynamically adjust the processing rates between it and its downstream or upstream VNFs by maximizing the idle resource utilization. The experimental results indicate that the proposed algorithm outperforms the other methods in terms of the average delay, the flow completion time, the throughput and the backlog, etc. Meanwhile, the proposed algorithm achieves more stable performance than the other methods.

The rapid development and application of 5G/B5G generate tremendous amount of traffic which in turn cause great burden for the corresponding transmission network. One typical way to address such challenge is to sink the content (e.g., 4K and 8K videos) from the remote cloud to the edge servers. In this case, how to efficiently visiting and getting these contents becomes a new problem, in which the cooperation between cloud and edge should be taken into consideration. In this regard, this work builds an edge and cloud cooperative routing and caching system which consists of three main modules of content popularity prediction, cooperative caching and cooperative routing. Specifically, the content prediction is designed by jointly leveraging the technologies of Long Short-Term Memory (LSTM) and Temporal Pattern Attention (TPA) to dig the traffic features and predict the future content popularity. Based on the prediction results and the technology of reinforce learning, the cooperative caching module designs both a reactive content replacement and an active content caching strategies. After that, the cooperative routing is carried out to help customers visiting and obtaining these content efficiently with the objective of minimizing the overhead. The experimental results indicate that the proposed methods outperform the state-of-the-art benchmarks in terms of the caching hit rate, the average throughput, the successful content delivery rate and the average routing overhead.

The energy consumed by data centers has been growing rapidly in recent years. Among all the major contributors to the power consumption of entire data centers, data center network (DCN) can account for up to 20% of the total power consumption. In this article, we first devise a power-efficient software-defined DCN (PESD-DCN) framework, which can achieve desirable power efficiency, avoid potential link congestion, and reduce frequent device state transition. Then, we formulate the optimization problem of maximizing the radio full-utilized devices to all devices. To solve it, we propose correlation-aware flow routing (CFR) algorithm, which leverages correlation-aware flow consolidation (CFC) technique to improve energy efficiency, avoid the potential link congestion, and reduce frequent device state transition. Moreover, to further improve the DCN energy efficiency, we propose flow rerouting, link rate adaptation, and device sleeping (FLD) algorithm. Finally, simulation results demonstrate that PESD-DCN can achieve a good performance. More specifically, in comparison to the other baseline algorithms, PESD-DCN can achieve up to 79.19% energy efficiency, 67.1% decrease in switch state transition (SST), and 55.4% decrease in link state transition (LST).

Network function virtualisation and software‐defined networking are two emerging technologies which together enable a new service provisioning paradigm called service function chain (SFC). However, the SFC should be scalable enough to accommodate one or more service functions joining or leaving it during its lifecycle. In this work, the authors first formulate this problem as an integer linear programming (ILP) model, and then address this ILP model to obtain the optimal solution. Due to the extremely high execution time for solving the ILP model, they next proposed a dynamic heuristic to solve this problem. In particular, the proposed heuristic leverages two kinds of strategies to handle the scale‐in (i.e. removing existing service functions from SFCs) and scale‐out (i.e. adding new service functions to SFCs) requests. Since each SFC has a corresponding service function path (SFP) constructed, the first kind of strategy proposes to serve the arriving scale‐in and scale‐out requests based on the in‐use SFP, while the second kind of strategy intends to optimise the in‐use SFP to achieve low cost and packet loss probability proactively. Finally, the experimental results indicate that the proposed heuristic can achieve better performance than the existing algorithm.

The Virtual Network Function (VNF) embedding problem is important for service provision in the context of Network Function Virtualisation (NFV). However, this problem is proved to be NP‐hard and challenging, and requires to be explored further. In this study, the authors first formulate it as an Integer Linear Programming (ILP) model for optimal solutions. Then, to compensate for the high running time of solving the ILP model, they propose a heuristic approach which fulfils the embedding process by jointly taking the global network connectivity and the local substrate node capacity into consideration. The simulation on real‐world network topologies demonstrates that the proposed approach can provide solutions within 1.7 times of the optimal solution offered by ILP. In addition, the experiments also suggest that the proposed approach can provide up to 2.75 times reduction in the overall cost than the other benchmarks.

In the software defined network (SDN) environment, network function virtualisation enables the virtual machine migration. Owing to the fact that transferring large amount of data will impede competing workflows, virtual network function (VNF) migration has brought a new perspective. Many optimised algorithms focusing on limiting migration time and migration cost have been proposed. In this study, the authors address the problem from a different perspective. They view the network topology from a global perspective and focus on the network effect of the whole network caused by VNF migration in the context of SDN. They introduce a parameter delay to formulate the network effect and an effect model is proposed to evaluate the migration effect of the network. In addition, a heuristic algorithm is proposed to minimise network effect while balancing network load and improving the service considering the migration cost and resources limit at the same time. The practicability and efficiency of the proposed model and algorithm are validated by simulation evaluation. By comparing their proposed algorithm with traditional benchmarks and closely related benchmarks, the experimental results show that their proposed algorithm largely reduces the network effect, while at the same time limiting the run time.