Under the global context of carbon neutrality, electric vehicles (EVs) have become a key component of urban logistics. However, the operational constraints of electric vehicles—particularly their limited driving range requiring frequent recharging and the reality of insufficient charging infrastructure—pose significant challenges for logistics companies in terms of cost management. To address this issue, we propose a multi-depot collaborative electric vehicle routing problem with heterogeneous fleet (MDCEVRPHF). It aims to enhance the operational efficiency of electric vehicle transport networks and reduce overall operational costs by integrating multi-depot collaborative operations with heterogeneous fleet management strategies. We formulate a mixed integer programming (MIP) model for the problem. To efficiently solve this NP-hard problem, we develop a Q-learning enhanced variable neighborhood search algorithm (QLVNS), which improves search performance through the introduction of Q-Learning mechanism and four newly designed neighborhood structures. Extensive experiments validate the effectiveness and superiority of QLVNS in solving MDCEVRPHF. Furthermore, the study shows that integrating multi-depot collaboration and heterogeneous fleet management can not only significantly reduce the dependence of electric vehicles on charging infrastructure, but also effectively control operational cost while maintaining transportation efficiency. Finally, we also give suggestions for cost allocation mechanisms.

Nowadays, the development of Collaborative Edge Computing (CEC) has greatly accelerated deep neural network (DNN) inference by enabling edge service providers (ESPs) in the JointCloud federation to deploy computational resources and well-trained DNN models at the edge of the network for collaborative inference with mobile devices (MDs), thereby promoting the rapid advancement of intelligent applications. This raises the need for an effective DNN inference offloading mechanism between MDs and ESPs. However, existing schemes often lack market efficiency and fail to ensure desirable economic properties. To this end, we propose a truthful double auction mechanism for delay-aware DNN inference offloading (TDAD), which integrates a dynamic programming approach with an adaptive resource allocation and pricing strategy to maximize social welfare while ensuring truthfulness, budget balance, and individual rationality. Specifically, TDAD first employs a binary search-based delay-aware partitioning and offloading method to determine the minimum feasible resource profile for each MD, and then applies a dynamic programming-based double auction to match MDs' inference demands with ESPs' combinatorial resources, and compute the corresponding payments and rewards. Theoretical analysis proves that TDAD satisfies the desired economic properties, while experiments in realistic CEC environments validate its effectiveness and efficiency.

Hot rolling production scheduling (HRPS) is an essential process in modern steel manufacturing. Its effectiveness is influenced by three primary challenges: selecting suitable slabs to maximize efficiency and maintain product quality, adhering to increasingly stringent carbon tax regulations, and managing uncertainties in processing times stemming from fluctuations in rolling speed. This article presents a novel robust HRPS problem under carbon tax regulation (RHRPSP-CTR) that considers slab selection, carbon tax regulation, and uncertain processing times simultaneously for the first time. Based on a budgeted uncertainty set, we develop a robust counterpart model that utilizes a classical dualization scheme and dynamic programming recursive equations to address the challenges associated with evaluating the worst case cost of carbon emissions (CEs) and determining the worst case completion time for each slab caused by processing time uncertainty, respectively. Recognizing the characteristics of slab selection and the high computational complexity in the large-scale RHRPSP-CTR, we propose an adaptive large neighborhood search algorithm incorporating two enhancement strategies: a slab selection rule and a max-min weight update mechanism. Extensive computational experiments demonstrate that the proposed method yields optimal solutions for small-scale problem instances and high-quality, robust solutions for large-scale instances within a relatively short computation time. Moreover, the results indicate that compared with deterministic HRPS schemes, robust HRPS schemes lead to only a slight increase in cost. Notably, a higher carbon tax does not necessarily lead to lower CEs, and a larger uncertainty budget coefficient or uncertainty range does not always result in higher CEs.

The efficiency of data retrieval is crucial for the lifecycle traceability of large-scale manufacturing products. This article proposes a novel on-chain and off-chain parallel retrieval method based on blockchain and Merkle Patricia Trie (MPT), which is called parallel retrieve index MPT (PRIMPT). In the proposed method, index MPT (InMPT) is designed to store the index information and the off-chain address of transactions based on MPT. In addition, the transaction division and the node division are proposed to realize the parallelization. Among them, the transaction division adds a new stage branch node under the root node of InMPT and divides transactions according to their stages, while the node division groups the nodes in the blockchain according to the derived most efficient group number and assigns retrieval requests to these node groups. Experiment results show that the PRIMPT method improves the efficiency by 44% compared with the state-of-the-art on-chain and off-chain method.

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.

As mobile applications become increasingly computation-intensive, mobile devices (MDs) face growing limitations due to their constrained computational capabilities and battery life. Collaborative Edge Computing (CEC) has emerged as a promising solution to address these challenges by enabling multiple edge service providers (ESPs) to offer computation offloading services to MDs. As such, a CEC resource trading market is essential for efficient interactions between MDs and ESPs. However, jointly determining the offloading ratios, allocating combinatorial computation and communication resources, and designing appropriate pricing strategies in a dynamic market remains a significant challenge. To this end, we propose a truthful online double auction based resource allocation mechanism for partial computation offloading (TRAPO) that explicitly accounts for the stochastic nature of both MDs and ESPs. TRAPO first leverages spatial diversity to construct a set of bids for each MD by mapping their task requirements into resource demands through considering MDs' preferences and partial offloading. Next, we match resource-demanding MDs with resource-supplying ESPs based on adaptive valid price thresholds to maximize social welfare, and calculate the payments of MDs and the rewards of ESPs. Theoretical analyses demonstrate that TRAPO satisfies truthfulness, budget balance, individual rationality, and computational tractability. Simulation experiments further verify the effectiveness and efficiency of TRAPO.

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.

In cloud-edge collaborative networking, intelligent devices often need to offload tasks that they cannot handle to edge or cloud servers for processing. So the key problem is how to deploy various types of services and offload tasks to the appropriate servers. Deep Reinforcement Learning (DRL) algorithms have been widely used to address these issues. However, existing DRL solutions typically use centralized training methods, which can not address the challenge of obtaining global network states in practical scenarios due to the extremely large network scale and privacy concerns. In this paper, we designed a Knowledge-Defined Cloud-Edge Collaborative Networking (KDCECN) architecture for managing network information and proposed a Partially Observed Lightweight exchange Hierarchical DRL algorithm (PO-LeHDRL). This algorithm fully considers factors such as the tolerable delay of tasks, the size of tasks, and different service deployment strategies, which solves the joint service placement and task offloading problems in the cloud-edge collaborative network in a distributed training and decision-making manner. The innovation of this scheme lies in achieving data privacy protection. In this scheme, edge nodes do not need to share their respective network measurement information and apply Laplace noise to the reward value using the differential privacy mechanism, effectively reducing additional communication overhead and protecting the privacy of network data on edge devices. The experimental results show that compared with the baselines, our scheme can improve the system's task completion rate and reduce the task completion delay, and it shows scalability across different network environments.

Offloading a plethora of end workflows to edge servers in mobile edge computing (MEC) systems involves a series of coupled decision-making steps, includinghow muchedge resources will be allocated for each workflow,whichsubtasks will be offloaded, andhowto determine edge-end transaction prices to ensure system stability. Particularly, these decisions must jointly account for workflow characteristics, the resources available at each edge server, as well as local constraints (e.g., communication distance, task latency, and bandwidth conditions), which again increases the difficulty of optimizing the problem. However, no existing study addresses such joint optimization problems for these tightly coupled decisions. To fill this gap, a minimum-delay workflow partitioning algorithm is first designed to determine the optimal task offloading solution under various resource conditions. Based on this algorithm, two locality-based social welfare maximization models (basic and dynamic) are constructed. Specifically, for basic model, a multi-stage task matching game with the second lowest cost strategy is developed to determine the resource selection and pricing. For the dynamic model with uncertain requests, an online learning algorithm is introduced to track the dynamic valuations of mobile devices and to ensure that the resulting task allocation solution achieves an upper-bounded regret. Strict theoretical analysis demonstrates that our mechanism guarantees individual rationality, Nash Equilibrium, and stable approximation ratio. Simulation results verify the effectiveness and efficiency of our mechanism, and show that the proposed mechanisms obtain at most 18% higher social welfare than existing studies.

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.

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%.

This paper investigates the physical layer security (PLS) issue of unmanned aerial vehicle (UAV) aided multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) networks with randomly distributed passive eavesdroppers (Eves). Considering Nakagami-m fading, we propose a novel secure communication protocol that integrates transmit antenna selection (TAS) and maximum ratio combining (MRC) diversity technology. Specifically, to tackle the challenges of low spectrum efficiency and PLS performance, we propose two TAS solutions: TAS-max UN and TAS-max UF, the first one aims to enhance the performance of UN and the other one focuses on UF. To mitigate the impact of passive eavesdropping, a secure protected zone is established around the UAV to limit the Eve’s ability. Accordingly, we derive the closed-form expressions for the ergodic secrecy rate to evaluate the impact of spatial randomness. Then, the accuracy of the derived expressions is verified through Monte-Carlo simulations.To further support the theoretical analysis, asymptotic expressions under the high-SNR regime are derived, which offer valuable insights into secrecy rate trends and model convergence. Moreover, we propose a three-dimensional UAV deployment optimization framework that adopts a hybrid approach combining grid-based evaluation and Genetic Algorithm refinement, which improves ESR performance while significantly reducing computational complexity. In addition, a Simulated Annealing based power allocation scheme is introduced to optimize the power coefficient aF, achieving enhanced secrecy rate with improved search efficiency and adaptability. Extensive simulation results confirm that the proposed TAS/MRC framework, together with the secure protected zone, consistently outperforms conventional OMA and MRT schemes in terms of secrecy rate and robustness. The impact of key system parameters, including power allocation, UAV altitude, antenna configuration, and Eve density, is also thoroughly analyzed.

In this work, we investigate the physical layer security (PLS) of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) networks where non-colluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pair, while a backscatter device (BD) simultaneously transmits its unique signal by reflecting and modulating the BS’s signal. Meanwhile, Eves passively attempt to wiretap the ongoing transmissions. Notably, the number and locations of Eves are unknown, posing a substantial security threat to the system. To address this challenge, the BS injects artificial noise (AN) to mislead the Eves, and a protected zone is employed to create an Eve-exclusion area around the BS. Theoretical expressions for outage probability (OP) and intercept probability (IP) are provided to evaluate the system’s reliability-security trade-off. Asymptotic behavior at high signal-to-noise ratio (SNR) is further explored, including the derivation of diversity orders for the OP. Numerical results validate the analytical findings through extensive simulations, demonstrating that both the AN injection and protected zone can effectively enhance PLS. Furthermore, analysis and insights of different key parameters, including transmit SNR, reflection efficiency at the BD, power allocation coefficient, power fraction allocated to desired signal, Eve-exclusion area radius, Eve distribution density, and backscattered AN cancellation efficiency, on OP and IP are also provided.

Time-Sensitive Networking (TSN), renowned for its deterministic properties, has become a pivotal technology under-pinning real-time industrial control in Cyber-Physical Systems. Existing research emphasizes enhancing the transmission services of TSN networks for control applications by improving flow schedulability and minimizing end-to-end delay. However, these studies abstract the performance requirements of control applications into rigid, impractical constraints for flow scheduling, disrupting the connection between control optimization and transmission enhancement, and eventually undermining genuine progress in industrial control. Within a co-optimization framework of communication and control, this paper proposes AFS-RT, an Adaptive TSN Flow Scheduling method for Robotic arm Teleoperation, a representative industrial control application. Specifically, through a comprehensive analysis of the teleoperation case, we first integrate slot allocation-based flow scheduling with remote control to formulate a control-driven co-optimization model. To tackle the complexities arising from the implicit mapping between communication and control, we augment the Deep Reinforcement Learning agent responsible for slot allocation with slot-correlation-guided feature extraction, improving feature comprehension by leveraging inherent correlations between slots and thereby boosting the agent’s decision-making capabilities. Extensive testbed and simulation experiments demonstrate that AFS-RT significantly improves teleoperation performance under diverse network conditions compared to SOTA algorithms.

Machine learning (ML) typically relies on the assumption that training and testing distributions are identical and that data are centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly, and data are often distributed across different devices, organizations, or edge nodes. Consequently, it is to develop models capable of effectively generalizing across unseen distributions in data spanning various domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG synergizes federated learning (FL) and domain generalization (DG) techniques, facilitating collaborative model development across diverse source domains for effective generalization to unseen domains, all while maintaining data privacy. However, generalizing the federated model under domain shifts remains a complex, underexplored issue. This article provides a comprehensive survey of the latest advancements in this field. Initially, we discuss the development process from traditional ML to domain adaptation (DA) and DG, leading to FDG, as well as provide the corresponding formal definition. Subsequently, we classify recent methodologies into four distinct categories: federated domain alignment (FDAL), data manipulation (DM), learning strategies (LSs), and aggregation optimization (AO), detailing appropriate algorithms for each. We then overview commonly utilized datasets, applications, evaluations, and benchmarks. Conclusively, this survey outlines potential future research directions.

The range search in Hamming space is to explore the binary vectors whose Hamming distances with a query vector are within a given searching threshold. It arises as the core component of many applications, such as image retrieval, pattern recognition, and machine learning. Existing searching methods in Hamming space require much pre-processing overhead, which are not suitable for processing multiple batches of incoming data in a short time. Moreover, significant pre-processing overhead can be a burden when the number of queries is relatively small. In this paper, we propose a query-aware method for the approximate range search in Hamming space with no pre-process. Specifically, to eliminate the impact of data skewness, we introduce JS-divergence to measure the divergence between data's distribution and query's distribution, and specially design a Query-Aware Dimension Partitioning (QADP) strategy to partition the dimensions into several subspaces according to the scales of given searching thresholds. In the subspaces, the candidates can be efficiently obtained by the basic Pigeonhole Principle and our proposed Anti-Pigeonhole Principle. Furthermore, a sampling strategy is designed to estimate the Hamming distance between the query vector and arbitrary binary vector to obtain the final approximate searching results among the candidates. Experimental results on four real-world datasets illustrate that, in comparison with benchmark methods, our method possesses the superior advantages on searching accuracy and efficiency. The proposed method can increase the searching efficiency up to nearly 16 times with high searching accuracy.

Blockchain enhances trust and collaboration among entities through its inherent features of transparency, immutability, and traceability, leading to its extensive integration into Multi-access Edge Computing (MEC). However, existing transaction propagation mechanisms require MEC devices to consume significant computing resources for complex transaction verification, increasing their vulnerability to malicious attacks. Adversaries can exploit this by flooding the blockchain network with spam transactions, aiming to deplete device energy and disrupt system performance. To cope with these issues, this paper proposes a reputation-based energy-efficient transaction propagation mechanism that alleviates spam transaction attacks while reducing computing resources and energy consumption. Firstly, we design a subjective logic-based reputation scheme that assesses node trust by integrating local and recommended opinions and incorporates opinion acceptance to counteract false evidence. Then, we optimize the transaction verification method by adjusting transaction discard and verification probabilities based on the proposed reputation scheme to curb the propagation of spam transactions and reduce verification consumption. Finally, we enhance the transaction transmission strategy by prioritizing nodes with higher reputations, enhancing both resilience to spam transactions and transmission reliability. A series of simulations demonstrate the effectiveness of the proposed mechanism.

In large-scale dynamic network environments, optimizing the computation offloading and resource allocation strategy is key to improving resource utilization and meeting the diverse demands of User Equipment (UE). However, traditional strategies for providing personalized computing services face several challenges: dynamic changes in the environment and UE demands, along with the inefficiency and high costs of real-time data collection; the unpredictability of resource status leads to an inability to ensure long-term UE satisfaction. To address these challenges, we propose a Knowledge-Defined Networking (KDN)-based Adaptive Edge Resource Allocation Optimization (KARO) architecture, facilitating real-time data collection and analysis of environmental conditions. Additionally, we implement an environmental resource change perception module in the KARO to assess current and future resource utilization trends. Based on the real-time state and resource urgency, we develop a deep reinforcement learning-based Adaptive Long-term Computation Offloading and Resource Allocation (AL-CORA) strategy optimization algorithm. This algorithm adapts to the environmental resource urgency, autonomously balancing UE satisfaction and task execution cost. Experimental results indicate that AL-CORA effectively improves long-term UE satisfaction and task execution success rates, under the limited computation resource constraints.

With the advent of network function virtualization (NFV), many users resort to network service provisioning through virtual network function instances (VNFIs) run on the standard physical server in clouds. Following this trend, NFV markets are emerging, which allow a user to procure VNFIs from cloud service providers (CSPs). In such procurement process, it is a significant challenge to ensure differential privacy and truthfulness while explicitly considering dynamic resource provisioning, location sensitiveness and budget of each VNFI. As such, we design a differentially private and truthful reverse auction with dynamic resource provisioning (PTRA-DRP) to resolve the VNFI procurement (VNFIP) problem. To allow dynamic resource provisioning, PTRA-DRP enables CSPs to submit a set of bids and accept as many as possible, and decides the provisioning VNFIs based on the auction outcomes. To be specific, we first devise a greedy heuristic approach to select the set of the winning bids in a differentially privacy-preserving manner. Next, we design a pricing strategy to compute the charges of CSPs, aiming to guarantee truthfulness. Strict theoretical analysis proves that PTRA-DRP can ensure differential privacy, truthfulness, individual rationality, computational efficiency and approximate social cost minimization. Extensive simulations also demonstrate the effectiveness and efficiency of PTRA-DRP.

With the continuous development of Internet technology, social networks have provided convenient conditions for information dissemination. The rapid dissemination of information has provided us with great convenience, but some criminals extensively spread rumors based on such convenience, adversely affecting social stability. In this context, two key challenges arise: the survivability of rumors, which refers to their persistence and long-term impact, and the dynamic viewpoint changes of individuals, which influence how rumors spread and diminish over time. This article proposes a dynamic-susceptible-exposed-infected-recovered (DSEIR) rumor propagation model based on human social behavior to solve the spread of rumors problem. This model considers the characteristics of rumors spread in social networks with the Markov chain and makes the simulation more authentic. To suppress rumor propagation, we introduce the concept of rumor survivability and propose a dynamic truth movement blocking strategy, which adapts to people’s evolving viewpoints to curb the influence of rumors effectively. Finally, we analyze the proposed model and blocking strategy in four real networks. The experimental results show that the proposed propagation model can authentically simulate the propagation of rumors in social networks and the proposed blocking strategy efficiently suppresses the rumor propagation. We have released our code here:https://github.com/gf9264/DSEIR.

The design of effective multimodal feature fusion strategies is the key task for multimodal learning, which often requires huge computational costs with extensive expertise. In this paper, we seek to enhance multimodal learning via hierarchical fusion architecture search with inconsistency mitigation. Different from previous works, our Hierarchical Fusion Multimodal Neural Architecture Search (HF-MNAS) considers the inconsistency in modalities and labels, and fine-grained exploitation in multi-level fusion architectures. Specifically, it disentangles the hierarchical fusion problem into two-level (macro- and micro-level) search spaces. In the macro-level search space, the high-level and low-level features are extracted and then connected in a fine-grained way, where the inconsistency mitigation module is designed to minimize discrepancies between modalities and labels in cell outputs. In the micro-level search space, we find that different intermediate nodes in the cells exhibit different importance degrees. Then, we propose an importance-based node selection mechanism to form the optimal cells for feature fusion. We evaluate HF-MNAS on a series of multimodal classification tasks. Empirical evidence shows that HF-MNAS achieves competitive trade-off performance across accuracy, search time, and inference speed. In particular, HF-MNAS consumes minimal computational cost compared with state-of-the-art MNASs. Furthermore, we theoretically and experimentally verify that the modality-label inconsistency deteriorates the overall fusion performance of models such as accuracy and F1 score, and demonstrate that the proposed inconsistency mitigation module could effectively mitigate this phenomenon.

Few-shot classification is increasingly relevant in emerging applications, such as university course classification in intelligent education systems. University course classification helps students acquire specific skills, comprehend course purposes, and assists departments in defining training goals. However, classifying frontier courses presents challenges due to the absence of labels and descriptions. Few-shot learning addresses this by acquiring meta-knowledge. Heterogeneous graphs (HGs), rich in semantic information, introduce complexities that make few-shot particularly challenging. Addressing this problem, we propose a subgraph-aware convolutional few-shot classification method on HGs (HG-SCC). We first formalize the subgraph sampling strategy for HGs and different views under meta-paths. Then, the layer number adaptive spectral-based graph convolution is designed for personalized node embedding. Furthermore, a high-order convolution operation with classes as nodes is designed to increase the class representation coverage. Modeling subgraph centrality, combined with node features, captures structural information, improving awareness of each sampled subgraph, thus alleviating sparsity in new class labels and enhancing classification accuracy. Euclidean distance-based and task-affected cosine similarity-based classifiers under different meta-paths are proposed, with stacking introduced to blend multiple classifiers based on subgraph features. Experimental results show that our method has high performance in course classification and also outperforms state-of-the-art methods on benchmark datasets.

Recently, Edge-Cloud Collaborative (ECC) has emerged as an efficient and promising technique to empower various computation-intensive applications in Digital Twin Network (DTN). The integration of ECC and DTN serves to bridge the gap between data analysis and physical states. In ECC, a reliable and optimal task offloading scheme is required to maximize resource utilization and provide satisfying services to End Users (EU). However, existing offloading schemes still face significant challenges, such as the instability and complexity of network topologies, the intricacies of massive data, and the lack of trust among EU. In this paper, we propose anenhancinGedge-clOud collaboraTion wiTh blockchain-assistEd digital twin intelligence offloadiNgscheme (GOTTEN) which transmits large-scale tasks generated by DTs to Edge Station (ES) or Cloud Station (CS) in dynamic DTN scenarios. We first formulate this resource allocation and task offloading problem and provide an appropriate initial solution which guarantees that tasks generated by DTs can be accurately mapped to physical entities, while optimizing block allocation and reducing the decision space of task offloading. Then, we employ the Lagrange Multiplier based Distributed Island model-enhanced Genetic Algorithm (LM-DIGA) to transform our formulated problem into a convex form and achieve an optimal resource allocation under a specific scheme. Additionally, our proposed architecture also leverages blockchain verification mechanisms to enhance system stability, strengthening privacy protection for DT data as well. Finally, extensive simulation results demonstrate that, compared with seven baselines, our proposed scheme achieves a 10 percent the total system delay and privacy overhead with regard to other schemes in ECC.

Mobile edge computation (MEC) is envisioned as a prospective approach for processing the computation-intensive and delay-sensitive tasks of smart mobile devices (SMDs) through offloading them to base stations (BSs) nearby. In fact, efficient task offloading mechanisms are crucial to accomplish an MEC system. The key challenge is to make on-spot decisions upon the arrival of each task and at the same time achieve truthfulness of each SMD. The challenge further escalates, when the unique characteristics of an MEC system, such as locality constraint, delay constraint, etc., are explicitly considered. To solve the challenge, we present a truthful online combinatorial auction-based mechanism (TOCA) for task offloading in an MEC system. Specifically, we first devise the candidate offloading scheme determination algorithm, aiming to determine the candidate offloading schemes of an SMD upon the arrival of its task. Next, we devise the winning offloading scheme selection and pricing algorithm based on the online primal-dual optimization framework, to decide the winning scheme among the SMD's candidate offloading schemes and calculate its payment. By solid theoretical analysis, we verify that TOCA achieves truthfulness, individual rationality and computational efficiency and a smaller competitive ratio. Trace-driven simulation studies validate the effectiveness and efficacy of TOCA.

Federated learning stands out as a promising approach within the domain of edge computing, providing a framework for collaborative training on distributed datasets without necessitating data sharing. However, federated learning involves the frequent transmission of machine learning model updates between the server and clients, resulting in high communication costs. Additionally, heterogeneous clients can further complicate the Federated Learning process and deteriorate performance. To address these challenges, we propose Adaptive Self-Knowledge Distillation-based Quality- and Reputation-Aware Cross-Device Federated Learning (ASDQR) - an efficient communication and inference framework designed for heterogeneous clients. ASDQR initiates the process by selecting high-reputation and high-quality clients to be involved in federated learning, significantly impacting communication efficiency and inference effectiveness. ASDQR also introduces a model of adaptive local self-knowledge distillation that incorporates multiple local personalized historical knowledge for more accurate inference, allowing the historical level to be dynamically adjusted across time. Finally, we present an inference-effective aggregation scheme that assigns higher weights to important and reliable local model updates based on clients’ contribution degrees when performing global model aggregation. ASDQR consistently outperforms baseline methods across all datasets and communication rounds, achieving 9.0% higher accuracy than FedAvg, 6.59% higher than MOON, 0.29% higher than FedProx, 0.2% higher than PFedSD, and 0.08% higher than FedMD on the MNIST dataset at 100 communication rounds. Similar improvements are observed on CIFAR, HAR, and WISDM datasets, demonstrating the robustness and efficiency of ASDQR in federated learning with non-IID data.

More and more application providers (APs) start to deploy their geo-distributed services in multiple cloud environments, such as JointCloud, federated clouds and InterCloud. Thus, massive cross-cloud traffic is generated from the services of APs, who need to pay Internet service providers (ISPs) for using their bandwidth. As such, an effective cross-cloud link bandwidth allocation and pricing mechanism is needed between APs and ISPs. Existing fixed-price scheme lacks market efficiency. Thus, we propose a truthful padding-based auction mechanism (TPAM) for cross-cloud bandwidth, which introduces the padding method and well-designed pricing strategy to ensure desirable properties. This mechanism is flexible enough to allow each AP to win the whole request, or win the specified proportional request, or lose and get nothing. Specifically, we first devise a linear-program-based method to calculate the padding vector for each candidate AP. Next, we design a padding-based method to determine the winning APs and match them with ISPs who offer the cheapest bandwidth. Finally, we design a critical-value-based pricing strategy and a marginal-cost-based pricing strategy for APs and ISPs to achieve truthfulness and budget balance. Theoretical analyses prove that TPAM achieves truthfulness, budget balance, individual rationality, asymptotic efficiency and computational tractability. Trace-driven simulation results also validate the effectiveness and efficiency of TPAM.

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.

Edge intelligence (EI) entails deploying artificial intelligence algorithms at the network’s edge. Utilizing edge computing infrastructure enables local data processing and decision-making, resulting in decreased latency, bandwidth consumption, and improved privacy security. With increasing the number of Internet of Things (IoT) devices and the development of 6G communication technologies, there is a growing demand for fast, efficient, and low-latency data processing, which has led to the rise of EI. This survey comprehensively reviews and analyzes the current state of research on EI from the perspective of technological development, with a particular focus on the following key aspects: (1) We review the basic concepts of EI and its distinctions from traditional cloud computing and edge computing; (2) We explore the technological framework of EI in detail, including key technologies such as edge computing and federated learning, and analyze how these technologies integrate with modern communication technologies like IoT devices and 6G networks; (3) We discuss the challenges faced when implementing EI technologies, such as data privacy and security issues, device resource constraints, and propose corresponding solutions or research directions. The survey also outlines the main research directions and technical challenges driving the future development of EI, providing valuable insights and guidance for researchers and practitioners in the field.

Edge-Cloud-End Collaborative (ECEC) computing emerges as a promising paradigm to support computationally intensive applications within Digital Twin (DT) networks, providing flexibility and real-time performance for heterogeneous task scheduling and resource allocation. However, effectively balancing key Service Level Agreement (SLA) parameters, such as response delay, availability, and throughput in dynamic network environments remains challenging, especially for complex, large-scale applications. Existing solutions typically lack SLA-oriented proactive resource reservation schemes. To address these limitations, we propose an SLA-driven hierarchical bidirectional closed-loop DT Network Architecture (SLA-DTNA), comprising four layers: real network, local DT, edge DT, and cloud DT. The proposed architecture systematically decomposes SLA requirements into measurable system parameters, aiming to minimize overall system cost. Specifically, a differentiated task management mechanism is designed at the local DT layer to ensure Quality of Service (QoS) for critical tasks. At the edge DT layer, we propose a heuristic-based lightweight scheduling algorithm leveraging DT capabilities for efficient task resource mapping and reduced scheduling complexity. At the cloud DT layer, we apply the Deep Deterministic Policy Gradient (DDPG) algorithm for adaptive resource reservation, dynamically adapting schemes based on task preferences and historical behavior patterns. Simulation and experimental results validate that the proposed SLA-DTNA enables fine-grained and intelligent resource allocation, enhances overall network performance, and effectively satisfies dynamic SLA requirements.

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.

Alzheimer's disease (AD) has been the most common cause of dementia making cognitive score prediction and important feature identification crucial for its diagnosis. Although sparse linear regression has been used for this purpose due to its simplicity, it often selects an excessive number of features to track the disease and assumes a linear relationship between input and output, which might not always hold. To address these limitations, genetic programming-based symbolic regression (GPSR) algorithms have been proposed. GPSR can select the important features by exploring the feature space and learning a regression model without any assumption of model structure. However, the generalization ability of existing GPSR methods still needs to be improved. Considering the multiple related prediction tasks in AD studies, this work proposes a new method called linear scaled GPSR with multi-task feature selection (LSGPMTFS), to promote the prediction performance of each task by knowledge-sharing among multiple tasks. LSGPMTFS has two stages. The first stage learns a specific feature subset for each task. In the second stage, the model for each task is searched on the union of feature subsets selected from the first stage. The experimental results on authentic AD datasets demonstrate that the proposed algorithm can select a small set of important features with better learning and generalization performance compared with other GPSR methods.

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.

Multi-objective integer nonlinear programming (MOINLP) problems are multi-objective integer programming problems with at least one nonlinear objective function or constraint. To date, MOINLP problem has not been exactly solved. Although traditional ε-constraint method can be used to solve MOINLP problem, the obtained solution may not be Pareto-optimal. To overcome this shortcoming, a basic ε-constraint method (BEM) is proposed to solve MOINLP problem exactly. However, the time complexity of BEM is as high as O((p−1)M2N), where p, M, and N are the numbers of objectives, Pareto-optimal solutions, and feasible solutions, respectively. For this reason, an improved BEM (IBEM) is developed whose time complexity is O(M2N). That is, the time complexity of IBEM for solving MOINLP problem is equal to solving the single-objective one. Finally, to avoid using all the feasible solutions (N) in obtaining each Pareto-optimal solution, three methods to eliminate dominated solutions effectively are used before performing IBEM. The test results illustrate that our method can not only solve MOINLP problem exactly but also has high efficiency.

With the development of intelligent logistics technology, some companies began to use robots instead of humans to load cargo. This paper studies a novel container loading problem based on robotic loader system (CLP-RLS). Different from the existing robot-packable pattern in the literature, the robotic loader system in this paper consists of a depalletizing robot, an automatic telescopic roller line and a loading robot, in which the loading robot will enter the carriage along with the automatic telescopic roller line. In CLP-RLS, it is necessary to consider not only many practical constraints already in the literature, including load balancing, orientation, stability, and multi-drop but also two new constraints related to robotic loader system: pallet continuity constraint (the loading sequence of cargo on the same pallet is continuous) and robot position constraint (the robot can only load cargo incrementally from the front to the back of the truck). Due to the difficulty in modeling CLP-RLS and the large scale of the real-case instances, we present a tree search approach based on wall-building to solve CLP-RLS, intending to find a feasible loading scheme for the RLS to minimize the length required to load all cargo. The effectiveness of the proposed approach is verified through both real-case instances and numerical instances.

Social networks currently serve as one of the primary sources from which people obtain news, with the spread of rumors emerging as a major concern. The goal of rumor suppression is to minimize the number of individuals affected by rumors through various methods, such as blocking and disseminating the truth. Although this problem has evolved into a popular research topic, existing solutions often overlook the temporal impact of rumor-refuting information and the influence of user opinions on rumor spreading. In the study, we first investigate the two-stage rumor minimization problem. The problem primarily considers two situations about only the propagation of rumors and the simultaneous propagation of rumor and rumor-refuting information, aiming to minimize the impact of rumors. We propose the two-stage user opinion rumor propagation model (TSUORP), which fully incorporates the timing of official releases of rumor-refuting information and their influence on the generation of rumors propagation. Based on this, we propose an approach using the knowledge graph convolutional network (KGCN) algorithm to rapidly and effectively select rumor-refuting information seed nodes based on user opinions. To assess the validity of our proposed approach, we perform experiments on three authentic datasets, showcasing its notable advantages.

In multi-objective evolutionary neural architecture search (NAS), existing predictor-based methods commonly suffer from the rank disorder issue that a candidate high-performance architecture may have a poor ranking compared with the worse architecture in terms of the trained predictor.To alleviate the above issue, we aim to train a Pareto-wise end-to-end ranking classifier to simplify the architecture search process by transforming the complex multi-objective NAS task into a simple classification task. To this end, a classifier-based Pareto evolution approach is proposed, where an online classifier is trained to directly predict the dominance relationship between the candidate and reference architectures. Besides, an adaptive clustering method is designed to select reference architectures for the classifier, and an α-domination assisted approach is developed to address the imbalance issue of positive and negative samples. The proposed approach is compared with a number of state-of-the-art NAS methods on widely-used test datasets, and computation results show that the proposed approach is able to alleviate the rank disorder issue and outperforms other methods. Especially, the proposed method is able to find a set of promising network architectures with different model sizes ranging from 2M to 5M under diverse objectives and constraints.

The rapid adoption of IPv6 has increased network access scale while also escalating the threat of Distributed Denial of Service (DDoS) attacks. By the time a DDoS attack is recognized, the overwhelming volume of attack traffic has already made mitigation extremely difficult. Therefore, continuous network monitoring is essential for early warning and defense preparation against DDoS attacks, requiring both sensitive perception of network changes when DDoS occurs and reducing monitoring overhead to adapt to network resource constraints. In this paper, we propose a novel DDoS incident monitoring mechanism that uses macro-level network traffic behavior as a monitoring anchor to detect subtle malicious behavior indicative of the existence of DDoS traffic in the network. This behavior feature can be abstracted from our designed traffic matrix sample by aggregating continuous IPv6 traffic. Compared to IPv4, the fixed-length header of IPv6 allows more efficient packet parsing in preprocessing. As the decision core of monitoring, we construct a lightweight Binary Convolution DDoS Monitoring (BCDM) model, compressed by binarized convolutional filters and hierarchical pooling strategies, which can detect the malicious behavior abstracted from input traffic matrix if DDoS traffic is involved, thereby signaling an ongoing DDoS attack. Experiment on IPv6 replayed CIC-DDoS2019 shows that BCDM, being lightweight in terms of parameter quantity and computational complexity, achieves monitoring accuracies of 90.9%, 96.4%, and 100% when DDoS incident intensities are as low as 6%, 10%, and 15%, respectively, significantly outperforming comparison methods.

Sentiment and sarcasm are intimate and complex, as sarcasm often deliberately elicits an emotional response in order to achieve its specific purpose. Current challenges in multi-modal sentiment and sarcasm joint detection mainly include multi-modal representation fusion and the modeling of the intrinsic relationship between sentiment and sarcasm. To address these challenges, we propose a single-input stream self-adaptive representation learning model (SRLM) for sentiment and sarcasm joint recognition. Specifically, we divide the image into blocks to learn its serialized features and fuse textual feature as input to the target model. Then, we introduce an adaptive representation learning network using a gated network approach for sarcasm and sentiment classification. In this framework, each task is equipped with its dedicated expert network responsible for learning task-specific information, while the shared expert knowledge is acquired and weighted through the gating network. Finally, comprehensive experiments conducted on two publicly available datasets, namely Memotion and MUStARD, demonstrate the effectiveness of the proposed model when compared to state-of-the-art baselines. The results reveal a notable improvement on the performance of sentiment and sarcasm tasks.

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%.

In recent years, the number of network applications is constantly increasing, and network congestion often occurs. To ensure the network Quality of Service (QoS), different types of traffic are classified according to their requirements, and similar traffic is transmitted to the same queue for scheduling. The switch generally uses fair queuing and its extension schemes to schedule traffic. These schemes achieve different bandwidth allocation by configuring different queue weights, so as to obtain a lower packet loss rate. However, the switch can provide us with very few statistical parameters, so using a large number of statistical parameters for adaptive weight adjustment is challenging in implementation. At the same time, the weight range supported by the switch is large, but the action space supported by reinforcement learning is limited, which cannot represent the entire queue weight space. Although deep reinforcement learning can solve the problem with large space, the existing switches can not well support the calculation of neural network model. In this paper, we propose a scheduling optimization mechanism combining Q-learning and genetic algorithm, called QGSO, which is used to schedule traffic in real switches. Firstly, we model the scheduling optimization problem as a Markov decision process (MDP) and use Q-learning to solve it in order to select the optimal queue weights according to the state of the environment. Secondly, we use genetic algorithm to filter out a group of optimal queue weights from the weight space, achieving compression of the solution space. Finally, we use a hardware testbed to test and verify the effectiveness of the algorithm. The experimental results show that our algorithm can effectively schedule traffic and achieve a lower packet loss rate.

Cross-silo federated learning (FL) is a privacy-preserving distributed machine learning where organizations acting as clients cooperatively train a global model without uploading their raw local data. Recently, the cross-silo FL in multiaccess edge computing (MEC) is used in increasing industrial applications. Most existing research on cross-silo FL pays attention to the performance aspect, ignoring the incentive mechanism for high-quality client selection and long participation in model training for efficient and stable FL, which has prevented the widespread adoption of cross-silo FL in MEC. In this article, we propose an incentive mechanism with quality-Aware and reputation-Aware based on the infinitely repeated game for cross-silo FL named VARF. VARF selects high-quality and high-reputation edge nodes (ENs) as candidates for model training in the cross-silo FL by a heuristic algorithm and then motivates the selected ENs to actively contribute their resources. VARF also models the long-term behavior of ENs in cross-silo FL as an infinitely repeated game and derives a stable and long-term cooperative strategy for clients while maximizing the amount of local data for model learning in cross-silo FL. Extensive simulations with real-world data sets demonstrate that the performance of VARF is more beneficial than other benchmarks. Meanwhile, experimental results show that cloud platforms (CPs) and ENs eventually form a long and stable cooperative relationship under the trigger strategy.

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.

Misinformation and rumors can spread rapidly and widely through online social networks, seriously endangering social stability. Therefore, rumor blocking on social networks has become a hot research topic. In the existing research, when users receive two opposing opinions, they tend to believe the one arrives first. In this article, we argue that users will dialectically trust the information based on their own opinions rather than the rule of first-come-first-listen. We propose a confidence-based opinion adoption (CBOA) model, which considers the opinion and confidence according to the traditional linear threshold (LT) model. Based on this model, we propose the directed graph convolutional network (DGCN) method to select the$k$most influential positive cascade nodes to suppress the propagation of rumors. Finally, we verify our method on four real network datasets. The experimental results show that our method can sufficiently suppress the propagation of rumors and obtains smaller number of rumor nodes than the baseline algorithms.

This article investigates how to enhance the Multi-access Edge Computing (MEC) systems performance with the aid of device-to-device (D2D) communication computation offloading. By adequately exploiting a novel computation offloading mechanism based on D2D collaboration, users can efficiently share computational resources with each other. However, it is challenging to distinguish valuable information that truly promotes a collaborative decision, as worthless information can hinder collaboration among users. In addition, the transmission of large volumes of information requires high bandwidth and incurs significant latency and computational complexity, resulting in unacceptable costs. In this article, we propose an efficient D2D-assisted MEC computation offloading framework based on Attention Communication Deep Reinforcement Learning (ACDRL), which simulates the interactions between related entities, including device-to-device collaboration in the horizontal and device-to-edge offloading in the vertical. Second, we developed a distributed cooperative reinforcement learning algorithm that includes an attention mechanism that skews computational resources towards active users to avoid unnecessary resource wastage in large-scale MEC systems. Finally, to improve the effectiveness and rationality of cooperation among users, we introduce a communication channel to integrate information from all users in a communication group, thus facilitating cooperative decision-making. The proposed framework is benchmarked, and the experimental results show that the proposed framework can effectively reduce latency and provide valuable insights for practical design compared to other baseline approaches.

The outbreak of COVID-19 pandemic has spread rapidly and severely affected all aspects of human lives. Recent researches has shown artificial intelligence and deep learning based approaches have achieved successful results in detecting diseases. How to accurately and quickly detect COVID-19 has always been the core topic of research. In this paper, we propose a novel approach based on prompt learning for COVID-19 diagnosis. Different from the traditional “pre-training, fine-tuning” paradigm, we propose the prompt-based method that redefine the COVID-19 diagnosis as a masked predict task. Specifically, we adopt an attention mechanism to learn the multi-modal representation of medical image and text, and manually construct a cloze prompt template and a label word set. Selecting the label word corresponding to the maximum probability by pre-training language model. Finally, mapping the prediction results to the disease categories. Experimental results show that our proposed method obtains obvious improvement of 1.2% in terms of Mi-F1 score compared with the state-of-the-art methods.

In the data center network, mixed flows which have contradictory service requirements are transmitted simultaneously. Switches usually aggregate similar flows to the same queue after flow classification and schedule them using fair queuing and its extension schemes capable of flow isolation. These schemes implement diverse bandwidth allocation by assigning different weights to queues. Existing solutions rely on rich statistics such as packet arrival rate and delay to realize dynamic bandwidth allocation. However, many statistics are difficult to accurately measure or even obtain in real switches due to resource limitations. Providing differentiated services for mixed flows under such restrictions is still a challenge. To solve this issue, this paper proposed a reinforcement learning-based scheduling optimization (RLSO) mechanism. First, mixed flows scheduling is modeled as the Markov decision process (MDP) and Q-learning is used to find the approximate optimal solution with a few statistics. Second, the solution space is compressed to reduce the complexity of the algorithm and adapt to the limited performance of switches. Finally, the performance of the proposed mechanism is evaluated on a hardware testbed with workloads that include coarsegrained and fine-grained flows. The results show that RLSO can effectively schedule mixed flows.

Multiple types of flows with contradictory service requirements, namely mixed flows, coexist in the data center network. Similar flows will be aggregated into the same queue after flow classification and are scheduled in switches by using fair queueing and its extension schemes which are capable of flow isolation. These schemes allocate different bandwidths by adjusting weights to realize differentiated services. However, existing solutions only focus on the requirements of some flows, which leads to the failure to satisfy the requirements of other flows. Therefore, it is necessary to make a trade-off between the service requirements of different flows when allocating bandwidth. In this paper, a max-min fairness based scheduling optimization (MMFSO) mechanism is proposed to schedule mixed flows. First, the bandwidth requirements of each queue are calculated by statistics of the switch. To reduce the influence of sampled statistics while forecasting bandwidth requirements, we introduce the exponentially weighted moving average for bandwidth requirements computation. Second, the bandwidth is allocated to each queue according to the max-mix fairness. The queue weight is determined by the allocated bandwidth of the queue. Finally, the performance of the proposed mechanism is evaluated on the hardware testbed in which workloads include coarse-grained flows and fine-grained flows. The results show that MMFSO can effectively schedule mixed flows.

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.

This article proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large-scale multiobjective and many-objective optimization problems (MaOPs). Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large-scale multiobjective and MaOPs.

The performance of decomposition-based algorithms is sensitive to the Pareto front shapes since their reference vectors preset in advance are not always adaptable to various problem characteristics with no a priori knowledge. For this issue, this article proposes an adaptive reference vector reinforcement learning (RVRL) approach to decomposition-based algorithms for industrial copper burdening optimization. The proposed approach involves two main operations, that is: 1) a reinforcement learning (RL) operation and 2) a reference point sampling operation. Given the fact that the states of reference vectors interact with the landscape environment (quite often), the RL operation treats the reference vector adaption process as an RL task, where each reference vector learns from the environmental feedback and selects optimal actions for gradually fitting the problem characteristics. Accordingly, the reference point sampling operation uses estimation-of-distribution learning models to sample new reference points. Finally, the resultant algorithm is applied to handle the proposed industrial copper burdening problem. For this problem, an adaptive penalty function and a soft constraint-based relaxing approach are used to handle complex constraints. Experimental results on both benchmark problems and real-world instances verify the competitiveness and effectiveness of the proposed algorithm.

Mobile edge computing (MEC) emerges as an appealing paradigm to provide time-sensitive computing services for industrial Internet of Things (IIoT) applications. How to guarantee truthfulness and budget-balance under locality constraints is an important issue to the allocation and pricing design of the MEC system. In this paper, we propose a truthful combinatorial double auction mechanism, which integrates the padding concept and the efficient pricing strategy to guarantee desirable properties in constrained MEC environments. This mechanism takes into account the locality characteristics of the MEC systems, where mobile devices (MDs) only offload tasks to edge servers (ESs) in the proximity with various requirements, and ESs only serve their neighboring MDs with limited resources. To be specific, for allocation, a linear programming (LP)-based padding method is used to obtain the near-optimal solution in the polynomial time. For pricing, a critical-value-based pricing strategy and a VCG-based pricing strategy are designed for MDs and ESs to achieve truthfulness and budget-balance. Our theoretical analysis confirms that TCDA is able to hold a set of desirable economic properties, including truthfulness, individual rationality, and budget-balance. Furthermore, simulation results validate the theoretical analysis, and verify the effectiveness and efficiency of TCDA.

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 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).

The safety driving-related content demands of vehicle users increase rapidly, especially with the development of autonomous driving. It is significantly necessary to obtain the safety-related transportation information of an area when vehicles are drove there, whether or not they are controlled by human being. However, vehicular content caching can bring issues in distributed-fashion, such as high response delay and low content response ratio because of the poor traffic condition and the obstructions of buildings. As a consequence, we adopt UAVs (Unmanned Aerial Vehicles) to assist the driving safety-related content caching for vehicles. Besides, since the power energy and the caching storage of UAVs are limited, it is needed to design an optimal caching scheme to guarantee the driving safety-related content demands of vehicle users as well as reduce the energy consumption of UAVs. In this article, we propose a novel deep Q-learning based air-assisted vehicular caching scheme to respond to the driving safety-related content requests of vehicle users. First, a three-layered content response architecture is introduced, where an airship is leveraged to take charge of the scheduling of UAVs to improve the content response. Then, a multi-objective mathematical model is built to describe the specific problem of the proposed scheme. Finally, deep Q-learning is applied to solve the multi-objective problem by learning from the history content requests of vehicle users. Extensive experiments have been conducted which show the proposed scheme outperforms its counterparts in terms of content hit ratio, response delay, being scheduling probability and packet buffering time.

Previous work has shown that machine learning algorithms lend themselves to clinical decision-making and are a valuable tool for physicians. For clinical data, it is often necessary to assign multiple labels to a patient record by choosing from a large number of potential labels. A key problem in learning from multi-labelled data is how to exploit the information contained in the correlations between labels. The hypergraph-based multi-label learning method learns from data by exploiting the spectral property of the hypergraph that encodes the correlation structure of labels. However, the problem with this method is the difficulty with which interpretations can be made. This is mainly due to its inability to recognize the importance of key features in the original feature space. Moreover, it is hard to comprehensively capture the complex structure of the correlations between labels. To overcome these difficulties and improve interpretability, we propose an l21-norm regularized Graph Laplacian multi-label learning to perform feature selection and label embedding simultaneously. In-depth experimental studies, using the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database, validate the effectiveness of our approach.

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Predicting cognitive performance of subjects from neuroimage measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Multi-task based feature learning (MTFL) have been widely studied to select a discriminative feature subset from MRI features, and improve the performance by incorporating inherent correlations among multiple clinical cognitive measures. It is known that the brain imaging measures are often correlated with each other, and AD is closely related to the inter-correlation among different brain regions. However, the multi-task based feature learning (MTFL) method neglects the inherent correlation among brain imaging measures. We present a novel regularized multi-task learning approach via a joint sparsity-inducing regularization to effectively incorporate both a relatedness among multiple cognitive score prediction tasks and a useful inherent correlation between brain imaging measures by exploiting correlations among features. It allows the simultaneous selection of a common set of biomarkers for all tasks and the preservation of the inherent structure of imaging measures. The reported experiments on the ADNI dataset show that the proposed method is effective and promising.

Traffic optimization in Information Centric Networks (ICN) inevitably involves making full use of in-network storages. Although ICN provides efficient content delivery and traffic offloading by exploiting nearby cached content and reducing redundant transmissions for same content requests, it inherently follows an opportunistic fashion to utilize in-network caches. In this paper, we improve network traffic distribution for ICN by leveraging multicasting. Specifically, introducing Software-Defined Networking (SDN), a virtual point-based algorithm is proposed to achieve many-to-many multicasting with global optimization. Furthermore, we propose a tunnel-based bandwidth allocation mechanism to improve the scalability of the system. Extensive simulation results show that multicast tree can be constructed optimally and meanwhile our proposal significantly improves network performance in terms of load balancing and network utilizazation.

Emerging technological innovations introduce the possibility to reconfigure the data center topology at runtime. The development of reconfigurable architectures can adapt their topology to account for changing demands, e.g., using Flyways-like augmented links. However, there is no common notion established in this area of how to evaluate the topology, the underlying theoretical analysis is not yet well studied. In this paper, we present the upper bound of the network diameter using spectral graph theory, to the best of our knowledge, which is a first theoretical attempt on understanding the nature of the reconfigurable data center networks. We further prove the algebraic connectivity is insensitive to the weight changes. Finally, based the algebra-connectivity λ2, we give a comprehensive link-augmentation validity, which can be implemented in current DCNs potentially.

Radio frequency identification (RFID) networks planning (RNP) is a challenging task on how to deploy RFID readers under certain constraints. Existing RNP models are usually derived from the flat and centralized-processing framework identified by vertical integration within a set of objectives which couple different types of control variables. This paper proposes a two-level RNP model based on the hierarchical decoupling principle to reduce computational complexity, in which the costefficient planning at the top levels is modeled with a set of discrete control variables (i.e., switch states of readers), and the quality of service objectives at the bottom level are modeled with a set of continuous control variables (i.e., physical coordinate and radiate power). The model of the objectives at the two levels is essentially a multiobjective problem. In order to optimize this model, this paper proposes a specific multiobjective artificial bee colony optimizer called H-MOABC, which is based on performance indicators with reinforcement learning and orthogonal Latin squares approach. The proposed algorithm proves to be competitive in dealing with two-objective and three-objective optimization problems in comparison with state-of-the-art algorithms. In the experiments, H-MOABC is employed to solve the two scalable real-world RNP instances in the hierarchical decoupling manner. Computational results shows that the proposed H-MOABC is very effective and efficient in RFID networks optimization.

The quality of experience (QoE) of users has become an important factor concerned by service providers to keep users for their services. The measurable quality of service (QoS) refers to technical performance, not the satisfaction of users, but it is closely related to user's QoE. Most existing researches mainly focus on studying the QoE-QoS relationship for services in terrestrial network with rare attention on satellite networks. Multimedia services delivery over satellite networks is a promising service in emerging future Internet, thus it is appealing to analyze how service QoE depend on QoS in satellite networks. In this paper, we build a simulated satellite network based on OPENT software to measure the QoS parameters and obtain the distorted videos/voices. Then, based on the original and distorted video/voice sequences, we perform a subjective test to obtain the subjective opinion scores representing user's QoE. Finally, on the basis of the collected data, the influence of single QoS parameter on QoE is analyzed and the QoS parameter thresholds to different QoE levels are provided.

MSN (Mobile Social Network) enables nodes (mobile devices) to realize packet delivery by leveraging social relationships of mobile users. However, MSN has to adapt with the daily increasing content (e.g., video) requirement requested by mobile users. Based on the fact that ICN (Information-Centric Networking) supports mobility naturally, we propose an MSN content-centric routing scheme. It is inspired by the in-network caching and named-content properties of Named Data Networking. By the analysis of the historical requested content names of MSN users, a novel interest distance metric is proposed, based on which, the forwarding node is selected for an interest packet. Meanwhile, by the analysis of the historical encounters of MSN nodes, a novel encounter regularity metric is proposed, based on which, the forwarding node is selected for a data packet. Furthermore, in order to respond the forth-coming requests rapidly, nodes preferentially cache the content which with high requested probability. By comparing with the existed schemes, simulation experiments represent that our scheme is effective and feasible.

Software-defined satellite networks (SDSN) move the control logic off the forwarding devices and into the external controller, which achieves network flexibility, programmability, evolvability, and visibility. The controller placement in SDSN, as the foundation of SDSN implementation, has been seldom studied. In this paper, we proposed a three-layer hierarchical controller architecture for software-defined geostationary earth orbit/low earth orbit (GEO/LEO) satellite networks. Specifically, network operations control center (NOCC) is deployed as super controller, GEO satellites are domain controllers, and a part of LEO satellites are slave controllers. Based on this architecture, we further proposed a slave controller selection strategy to facilitate cost reduction and stability enhancement. The feasibility of this controller architecture is validated in terms of the maximum switch to controller and controller to controller delays. Besides, the influences of service request distribution on these two control delays are analyzed.

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.

In-network caching is an important feature of ICN (Information Centric Networking). There are prior arts focusing on designing a highly efficient caching strategy by exploiting either node data or content data respectively. However, simply exploiting these data itself is not enough to reduce the cost of network operation and increase the quality of user experience, as there is no consideration on supplementary action of these data. In this paper, a multidimensional data learning based strategy (MDDL) is proposed to cache the selected content in a few suitable nodes. To understand the current state of node and content, a multidimensional state attribution data model including network, node and content data is proposed. Based on the model, the mapping relationship between the attribution data and the matching relationship value is mined. Utilizing this mapping function, a matching algorithm to predict the matching relationship between the node and the content in the next time period is proposed. In order to improve the accuracy of the matching algorithm, a safe semi supervised support vector machine (S4VM) algorithm is introduced. Simulation results show that MDDL outperforms CEE, BETW and LCD when looking at cost reduction of network operation and enhancement in quality of user experience.

A conflict over the fourth party logistics development is modeled, and a stability analysis is carried out based on the graph model for conflict resolution. In this conflict, corresponding DMs and their options are identified, infeasible states of this conflict are removed, and DMs' preferences over the feasible states are estimated based on option prioritization. Then, the directed graph for the fourth party logistics conflict is generated by using the decision support system entitled GMCRplus. Based on the calculation results, the interpretation on these calculated equilibria are provided, and the evolution of the conflict is analyzed.

This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms.

In this paper, we design an LAP based routing algorithm in General Networks (GN) to solve the problem of information consistency of the full network under OSPF with the following operations: (i) decomposing GN into one or more Single-link Networks (SNs) with the approach of depth-first walk, (ii) re-composting the SNs to a network with regular topology structure by adding links, (iii) searching the undirected complete graph of three nodes round by round until it converges to a simple network topology based on region binding, and (iv) processing different converged network topologies with different LAP based routing algorithms. The proposed algorithm is compared with Dijkstra algorithm over some random network topologies. Simulation results show that the proposed algorithm can solve the problem of information consistency of the full network under OSPF and has better performance than Dijkstra algorithm.

In recent years, multiobjective scheduling problems (MOSPs) have gained more and more concerns since many real-world applications always involve in multiple different objectives. In this paper, a multiobjective flow shop scheduling problem is investigated and a species based multiobjective evolutionary algorithm (MOEA), where a new multipopulation scheme is designed based on the mechanism of species that was used in EA for multimodal optimization problems, is proposed as its solution algorithm. Extensive experiments are carried out on a set of randomly-generated test problems in order to examine strongness and weakness of the performance of the proposed MOEA through comparing with two well-known MOEAs for addressing MOSPs.

With Inter-Cloud, distributed cloud and open cloud exchange (OCX) emerging, a comprehensive resource allocation approach is fundamental to highly competitive cloud market. Oriented to infrastructure as a service (IaaS), an intelligent economic approach for dynamic resource allocation (IEDA) is proposed with the improved combinatorial double auction protocol devised to enable various kinds of resources traded among multiple consumers and multiple providers at the same time enable task partitioning among multiple providers. To make bidding and asking reasonable in each round of the auction and determine eligible transaction relationship among providers and consumers, a price formation mechanism is proposed, which is consisted of a back propagation neural network (BPNN) based price prediction algorithm and a price matching algorithm. A reputation system is proposed and integrated to exclude dishonest participants from the cloud market. The winner determination problem (WDP) is solved by the improved paddy field algorithm (PFA). Simulation results have shown that IEDA can not only help maximize market surplus and surplus strength but also encourage participants to be honest.

This paper considers the problem of one buyer procuring multi-items from multiple potential suppliers in the electronic reverse auction, where each supplier can bid on combinations of items. From the perspective of the buyer, by considering multi-attributes of each item, a winner determination problem (WDP) of multi-items single-unit combinatorial reverse auctions was described and a bi-objective programming model was established. According to the characteristics of the model, an equivalent single-objective programming model was obtained. As the problem is NP-hard, an improved ant colony (IAC) algorithm considering the dynamic transition strategy and the Max-Min pheromone strategy is proposed for the problem. Experimental results show the effectiveness of the improved algorithm.

In this paper, a resource allocation model is constructed, based on the Double Multi-Attribute Auction (DMAA) mechanism. Firstly, multiple attributes are taken into account to form the Quality Index (QI), which is used to comprehensively evaluate consumers' and providers' performance in the transactions. Secondly, a Support Vector Machine (SVM) algorithm is adopted to predict the price. Finally, the Mean-Variance Optimization (MVO) algorithm is solved to obtain the optimized resource allocation scheme. Simulation results show that the proposed model can improve the resource utilization while satisfying user needs better.

In this paper, a QoS unicast routing scheme with ABC supported is proposed. With gaming analysis and particle swarm optimization algorithm, it tries to find a QoS unicast path with Pareto optimum under Nash equilibrium on both the network provider utility and the user utility achieved or approached. Simulation results have shown that it is both feasible and effective.

Recently, fourth party logistics (4PL) is receiving more and more attentions in manufacturing and retail industries. However, the research on the fourth party logistics routing problems (4PLRP) has just begun. In this paper, the mathematical model of the point to point single task path optimization of 4PLRP with time windows (4PLRPTW) is established based on multi-graph. The objective is to find minimum cost routes from the start node to the destination node within the pre-specified time windows. A recently-developed meta-heuristic optimization method, harmony search, is suggested for solving 4PLRPTW. The results of the numerical experiments demonstrate that the harmony search is effective and could find near optimal solution within the reasonable amount of time and computation.

Recently, fourth-party logistics (4PL) is receiving considerable attention in the manufacturing and retail industries. However, due to the complexity, the research of routing problem in 4PL is in an initial stage. The existing study does not consider the complicated problem with node-edge property. This paper studies the node-to-node routing problem in 4PL. A mathematical model is set up based on nonlinear integer programming and multigraph. With respect to the problempsilas characteristics a hybrid immune algorithm is designed. The simulation shows that the hybrid immune algorithm is effective for solving the problem and provides an efficient method for making decision on routing in 4PL.

Risk management in a Virtual Enterprise (VE) is an important issue due to its agility and diversity of its members and its distributed characteristics. In this paper, a stochastic programming model of risk management is proposed. More specifically, we consider about the stochastic characters of the risk in VE, and then we build a stochastic programming model to deal with the stochastic characters of the risk. In detail, this is a chance constraint programming model, One of the great advantages of this class of model is that it can exactlly describe the risk preference of the manager. In this model, the risk level of VE is obtained from a composite result of many risk factors. In order to reduce the risk level of VE, the manager has to select effective action for every risk factor. For each risk factor, there are several actions provided. Here we only select one action for a risk factor or do nothing with it. To solve this stochastic programming model, A particle swarm optimization (PSO) algorithm is designed. On the other hand, to deal with those stochastic variables, Monte Carlo simulation is combined with PSO algorithm. Finally, a numerical example is given to illustrate the effectiveness of the PSO algorithm and the result shows that the model is very useful for risk management in VE.

In this paper, a QoS multicast routing scheme with ABC (Always Best Connected) supported is proposed based on the beehive algorithm. To deal with the inaccurate network status and the imprecise user QoS requirement, the proposed scheme uses the range to describe them, introduces the edge bandwidth p

QoS (Quality of Service) routing is essential in NGI (Next Generation Internet). Due to difficulty on the exact expression of the user QoS requirements, the flexible QoS should be supported. In addition, with gradual commercialization of network operation, the benefit conflicts between the network provider and the user ask the so called win-win to be supported. In this paper, the knowledge of the fuzzy mathematics, game theory and artificial life computing method is introduced to design a flexible QoS unicast routing scheme. Based on BCC (Bacteria Colony Chemotaxis), it searches for a QoS unicast path with Pareto optimum under Nash equilibrium between the network provider utility and the user utility achieved or approached. Simulation results have shown that the proposed scheme is both feasible and effective with better performance.

Determination of optimal control strategy is one of the key factors for a successful supply chain management. This paper focuses on the research of an inventory control strategy of a serial supply chain. First, it proposes an optimization model of an inventory control that is based on the combination of a nonlinear integer programming model and a general push/pull control model. Then, the optimal control strategy is obtained by the combination of pheromone evolutionary algorithm and simulation analysis. Case studies demonstrated the effectiveness of the method.

Recently, fourth-party logistics (4PL) is receiving considerable attention in the manufacturing and retail industries. However, due to the complexity, the research of routing problem in 4PL is in an initial stage. The existing study does not consider the complicated problem with node-edge property. This paper studies the node-to-node routing problem in 4PL. A mathematical model is set up based on nonlinear integer programming and multi-graph. With respect to the problem's characteristics a mechanism for simplification is designed. To solve the problem model a hybrid algorithm is designed, in which Dijkstra algorithm is embedded. The simulation shows that the hybrid algorithm embedded with Dijkstra algorithm is effective.

In this paper, a game theory based fuzzy QoS multicast routing scheme is proposed and has been implemented by simulation. It consists of three parts: edge evaluation, game analysis, and multicast tree construction. It does comprehensive evaluation on candidate edges based on adaptability membership degree functions for edge parameters, determines whether Nash equilibrium between network provider utility and user utility has been achieved on candidate edges by gaming analysis, and attempts to construct a multicast routing tree with not only user QoS requirements satisfied but also Pareto optimum under Nash equilibrium on network provider utility and user utility achieved or approached by the proposed algorithm. Simulation results have shown that the proposed scheme is effective

Taking the characteristics of difficulty on exact measurement and complete expression of NGI (next generation Internet) into account, a flexible QoS multicast routing algorithm based on DE (differential evolution) is presented with introduction of principle of fuzzy mathematics. The corresponding model and its mathematical description are introduced. Under inaccurate information of QoS parameters and users' flexible QoS constraints, the proposed algorithm tries to find the multicast tree with maximum reliability degree and user's QoS satisfaction degree. Simulation results have shown that the proposed algorithm is both feasible and effective

This paper investigates flexible QoS multicast routing in IP/DWDM optical Internet. Given a multicast request, an algorithm is proposed to find a flexible QoS multicast routing tree based on artificial immune algorithm (AIA), and assigns wavelengths to the tree with the help of the wavelength graph. It integrates routing and wavelength assignment into one single process that supports load balancing as well as flexible QoS. Simulation results show that the AIA based scheme is both feasible and effective.

In this paper, a QoS multicast routing algorithm in IP/DWDM optical Internet is proposed. Given a user QoS multicast request, a bandwidth, delay, delay jitter and error rate bounded and cost optimized QoS multicast routing tree is constructed based on the shrinking-chaotic-mutation evolutionary algorithm with the network load balance considered. Simulation results have shown that the proposed algorithm is both feasible and effective to the QoS multicast routing in IP/DWDM optical Internet with the improved search ability and convergence speed to the optimal solution over its counterpart based on the traditional genetic algorithm.

Taking the characteristics of multi-constrained QoS (Quality of Service) routing in NGI (Next Generation Internet) into account, a hybrid intelligent multicast QoS routing algorithm based on PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) is presented. In this paper, the corresponding model and its mathematical description are introduced. Combining fast searching ability of PSO and global optimization ability of GA, the multi-constrained QoS (such as bandwidth, delay, delay jitter and error rate) multicast routing problem is solved. Simulation research and performance evaluation have been done over some actual and virtual network topologies. It has been shown that the proposed algorithm is both feasible and effective.