Open Radio Access Network (RAN) enables flexible, AI-driven control of mobile networks through disaggregated, multi-vendor components. In this architecture, xApps handle real-time functions, whereas rApps in the non-real-time controller generate strategic policies. However, current rApp development remains largely manual, brittle, and poorly scalable as xApp diversity proliferates. In this work, we propose a multi-agent Agentic AI framework to automate rApp policy generation and orchestration. The architecture integrates three specialized large language model (LLM)-based agents, Perception, Reasoning, and Refinement, supported by retrieval-augmented generation (RAG) and memory-based analogical reasoning. These agents collectively analyze potential conflicts, synthesize intent-aligned control pipelines, and incrementally refine deployment decisions. Experiments across diverse deployment scenarios demonstrate that the proposed system achieves over 70% improvement in deployment accuracy and 95% reduction in reasoning cost compared to baseline methods, while maintaining zero-shot generalization to unseen intents. These results establish a scalable and conflict-aware solution for fully autonomous, zero-touch rApp orchestration in Open RAN.

Industry 5.0 envisions a cyber-physical future where humans and robots collaborate harmoniously, empowered by 6G connectivity and intelligent automation. Central to this vision is the ability to autonomously configure complex production pipelines based on diverse and evolving human intents. Existing orchestration technologies exhibit critical shortcomings in terms of self-learning, validation, error diagnosis, and rectification capabilities. To this end, we propose an Agentic AI orchestration framework that interprets human intents and dynamically assembles optimal technology pipelines using a self-improving, retrieval-augmented Large Language Model (LLM) and a Bayesian contextual-bandit selector. This enables dynamic adaptation in unpredictable factory environments. Our solution is validated in a cyber-physical testbed integrating Digital Twins (DTs), distributed AI, robotics, and real-world network infrastructure. Compared to baseline LLMs, our system reduces orchestration iterations by over 94% for a given intent and by around 90% for an unseen intent, showing rapid convergence and strong generalization. Real-world deployments mirror DT results, confirming both the fidelity of the simulation and the practical value of intent-driven orchestration for human-centric manufacturing.

The rapid development of Artificial Intelligence (AI) has increased the demand for GPU resources, leading to a surge in deep learning tasks on heterogeneous GPUs. As a result, GPU resources in production clusters are often limited. It is essential to execute deep learning tasks efficiently within these resource and time constraints. Reducing task completion time (TCT) and improving GPU utilization are both critical. However, these goals often conflict, and existing solutions remain basic, making joint optimization challenging. We propose BFE, a method that integrates fragmentation awareness and efficiency orientation. BFE improves GPU utilization and reduces TCT by utilizing a cost model that combines average TCT and GPU fragmentation. We introduce a new fragmentation metric that considers both task size and GPU heterogeneity. For heterogeneous environments, we present a mathematical formulation that further enhances GPU efficiency and task scheduling. We also present a sliding window-based strategy to support the online scheduling scenario. We then design a particle swarm optimization-based scheduling algorithm to minimize the combined cost. Experiments on Alibaba's production traces demonstrate that the proposed BFE improves the GPU utilization by up to 51.73% and reduces the average TCT by up to 55.16%, outperforming existing methods.

In-vehicle networks (IVNs) face growing threats from advanced cyber-attacks, particularly stealthy masquerade attacks that mimic legitimate message patterns. This paper proposes STATGRAPH, a fine-grained intrusion detection frame work based on multi-view statistical graph learning over the Controller Area Network (CAN) messages within IVNs. STAT GRAPH constructs two graphs per detection window: a Timing Correlation Graph (TCG) capturing temporal ID dependencies, and a Coupling Relationship Graph (CRG) modeling short term contextual relations. TCG and CRG are further used to generate graph structure encoding payload variations and embedded signal co-occurrence. A lightweight multi-layered Graph Convolutional Network (GCN) is then applied to classify each message, leveraging the expressive representations from TCG and CRG. To ensure effectiveness against diverse attacks, we evaluate STATGRAPH on two real-world CAN datasets featuring five underexplored masquerade attacks. Experimental results show that STATGRAPH significantly improves detection granularity and outperforms state-of-the-art methods, with F1-score gains of 7% and 22%, while maintaining the highest accuracy. Code is available at https://github.com/wangkai-tech23/StatGraph

Multi-access edge computing provides localized resources within mobile networks to address the requirements of emerging latency-sensitive and computing-intensive applications. At the edge, dynamic requests necessitate sophisticated resource management for adaptive network slicing. This involves optimizing resource allocations, scaling functions, and load balancing to utilize only essential resources under constrained network scenarios. However, existing solutions largely assume static slice counts, ignoring the re-optimization overhead associated with management algorithms when slices fluctuate. Moreover, many approaches rely on simplified energy models that overlook intertemporal resource scheduling and are predominantly evaluated through simulations, neglecting critical practical considerations. This paper presents an incremental cooperative Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for resource management in dynamic edge slicing. The proposed approach optimizes long-term slicing benefits by reducing delay and energy consumption while minimizing retraining overhead in response to slice variations. Furthermore, we implement an urban-wide edge computing testbed based on OpenStack and Kubernetes to validate the algorithm’s performance. Experimental results demonstrate that our incremental MADDPG method outperforms benchmark strategies in aggregated slicing utility and reduces training energy consumption by up to 50% compared to the re-optimization approach.

Anomaly detection in microservice systems has attracted extensive attention in both academia and industry. However, two main problems still exist in the literature. First, many studies only consider single-modal data and ignore the useful information offered by other modalities. This may result in missing abnormal cases, leading to false alarms. Secondly, studies that use multimodal data for anomaly detection are still limited in capturing the correlation between different modalities. To address the above problems, we propose TriAnomalyNet, using a multi-stream Transformer encoder for feature extraction and fusion. It can effectively capture the correlation between the data for each modality and other modalities, using the features of other modalities to strengthen the features of the target modality, which can obtain a powerful feature representation. The graph attention network (GAT) is then adopted for anomaly detection. We verify the proposed TriAnomalyNet on two real datasets, and the results show that compared with state-of-the-art anomaly detection methods for microservice systems, TriAnomalyNet shows superiority.

The rise of multimodal fake news threatens reliable information dissemination by exploiting multiple modalities to create deceptive, engaging content, significantly impacting society safety. Existing methods still face challenges in cross-modal alignment (e.g., semantic inconsistencies, complex visual-semantic relations) and are vulnerable to low-quality or noisy samples. To address these, we propose Cross-Modal Alignment with Visual Reasoning Prompting (CMA-VRP) for multimodal fake news detection. Specifically, we model text and image entities with graphs to capture fine-grained semantic interactions and enhance cross-modal consistency through graph contrastive learning. Unlike methods relying on shallow image features (e.g., edges, textures), we leverage large language models (LLMs) and large vision-language models (LVLMs) to capture deep visual-semantic attributes related to reasoning (e.g., actions, scenes). Based on graph modeling and visual reasoning features, we perform graph-based cross-modal semantic fusion to unify textual and visual representations and cross-modal cycle alignment to align modality distributions by reducing semantic discrepancies, filtering modality-specific noise, and extracting invariant representations across domains. These steps enable the model to obtain semantically consistent and modality-invariant features. Extensive experiments demonstrate that our model outperforms existing methods in multimodal fake news detection and shows strong robustness against noisy samples.

In multimodal fact-checking, advanced large multimodal models (LMMs) struggle to capture and integrate the complex relationships between text and images. A potential solution is to generate reasoning support text to optimize reasoning and integrate evidence. However, existing generation approaches rely heavily on high-quality data annotations for training, which are costly and limited in scalability, hindering responsiveness to evolving misinformation. To address these issues, we propose CoReS, a novel zero-shot multimodal fact-checking model based on Conceptual Reasoning Support-leveraging key concepts from evidence to guide the reasoning process and improve decision-making. This model includes a reasoning support text generation module that extracts key concepts (critical elements that significantly impact the judgment outcome) from raw textual evidence via retrieval and filtering. By using a Conceptual Reasoning LM, CoReS generates reasoning support texts framed around core key concepts that are semantically consistent with multimodal evidence, linking key clues, thus replacing redundant and complex evidence for fact-checking. The reasoning support texts generated by CoReS effectively distill complex evidence relationships and integrate important reasoning information, allowing the judgment model to provide clear and accurate judgments. Evaluations on benchmark datasets and the new multi-domain MultiVerify dataset demonstrate that CoReS excels in accuracy, generalization, and scalability.

The integration of Artificial Intelligence (AI) within 6G networks is poised to revolutionize connectivity, reliability, and intelligent decision-making. However, the performance of AI models in these networks is crucial, as any decline can significantly impact network efficiency and the services it supports. Understanding the root causes of performance degradation is essential for maintaining optimal network functionality. In this paper, we propose a novel approach to reason about AI model performance degradation in 6G networks using the Large Language Models (LLMs) empowered Chain-of-Thought (CoT) method. Our approach employs an LLM as a “teacher” model through zero-shot prompting to generate teaching CoT rationales, followed by a CoT “student” model that is fine-tuned by the generated teaching data for learning to reason about performance declines. The efficacy of this model is evaluated in a real-world scenario involving a real-time 3D rendering task with multi-Access Technologies (mATs) including WiFi, 5G, and LiFi for data transmission. Experimental results show that our approach achieves over 97 % reasoning accuracy on the built test questions, confirming the validity of our collected dataset and the effectiveness of the LLM-CoT method. Our findings highlight the potential of LLMs in enhancing the reliability and efficiency of 6G networks, representing a significant advancement in the evolution of AI-native network infrastructures.

Unmanned aerial vehicle base station (UAV-BS) communication networks are considered as a promising solution for temporarily recovering urban telecommunication services interrupted by natural disasters. However, the deployment of UAV-BSs remains a challenge in disaster scenarios where terrestrial base stations may be unavailable, and users' locations (mobility in 3D, both on the ground and in the building) and requirements are constantly changing over time. In this paper, we propose a new digital twin (DT) framework to dynamically optimize UAV-BSs deployment in terms of both quantity and location while ensuring guaranteed network quality of service (QoS) and satisfied user requirements. It leverages a graph neural network (GNN) with new random walk for network modeling, a convolutional neural network (CNN) with online learning for QoS prediction, and deep reinforcement learning (DRL) models for optimizing UAV-BSs quantity and location. These three computing paradigms work collaboratively to respond to the evolving disaster context in an adaptive manner, improving UAV-BSs deployment subject to dynamic user requirements and mobility. Simulation results confirm the DT framework's effectiveness in optimizing UAV-BSs deployment in disaster scenarios.

Network slicing (NS) is a promising solution for media services, such as live streaming in telecom networks. NS enables customised network conditions for different applications and requirements. This customisation granularity is further enhanced through the use of 5G Quality of Service (QoS) flows for intra-slice management. Given the fact that network slices are becoming more dedicated to specific services' quality requirements, it becomes important to find service-aware NS methods that guarantee service quality while minimising resource consumption. To this end, this paper presents a Maximum Likelihood-based Network Slicing (MaxLiNS) method that minimises resource consumption with guaranteed Quality of Experience (QoE) for live low-latency streaming service under playback buffer level estimation and control. We evaluated the MaxLiNS method against existing service-agnostic and emerging service-aware NS methods on our End-to-End (E2E) 5G testbed. The results show that the MaxLiNS outperforms existing NS methods in terms of quality guarantee and resource consumption minimisation.

Caching recommended contents at the network edge can effectively alleviate the traffic pressure of the backbone network and significantly improve user experience. However, highly personalized and precise recommendations often rely on leveraging more user request records, raising serious privacy concerns. Existing recommendation-aware edge caching mechanisms typically apply a fixed level of privacy protection, without considering the personalized privacy of users. This one-size-fits-all approach often introduces significant noise, adversely impacting cache hit ratio (CHR). In this work, we propose a differential privacy-based edge caching framework supporting personalized privacy-preserving to address these challenges. We formulate a CHR maximization problem under personalized privacy constraints and reveal the NP-completeness of the problem with a rigorous mathematical proof. Subsequently, we mathematically model the relationship between personalized privacy and user preference distortion, analyzing its impact on recommendations and user requests. To solve it, we introduce an efficient heuristic algorithm named the Backhaul Traffic-Aware Caching Algorithm. This algorithm utilizes backhaul traffic as a feedback signal to make accurate caching decisions, enabling adaptive optimization of caching decisions by perceiving the impact of noise and low-quality recommendations. Extensive experiments on two typical real-world datasets validate the effectiveness of our framework, demonstrating its ability to enhance privacy protection while simultaneously improving CHR.

The emergence of beyond 5G and 6G networks is set to revolutionise telecommunications, addressing the demands of emerging applications through advanced capabilities. At the core of this transformation lies next-generation intelligent service orchestration, which is essential for meeting future Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) such as ultra-low latency, efficient power consumption and resource utilisation. These capabilities require multi-objective, seamless end-to-end service delivery across complex, distributed environments. Achieving such delivery requires scalable and modular system design approaches that support dynamic service composition and adaptability. Cloud-native technologies, underpinned by microservices architectures, plays a pivotal role, but also will introduce challenges in orchestrating resources efficiently across heterogeneous domains. To address these challenges, this paper proposes a solution, Federated Intelligent multi-objective Service function chain Orchestration (FISO) that integrates multi-objective federated profiling to preserve privacy while ensuring efficient end-to-end service delivery. FISO integrates Federated Learning (FL) and Reinforcement Learning (RL). FL is used to collaboratively learn from distributed edge profiling clients without sharing raw data, while RL dynamically guides optimal decision making for resource allocation and Service Function Chain (SFC) placement based on feedback from the federated models. FISO predicts optimal computing and network resources for SFCs, enabling the selection of appropriate edge locations, efficient resource allocation, placement of SFCs, and lifecycle management. Experimental results demonstrated on a pragmatic testbed validate the effectiveness of FISO in efficiently placing requested SFCs within an administrative domain with multiple edge/cloud nodes, predicting optimal CPU, memory, and link capacity resources, and minimising end-to-end latency and energy consumption.

The data consistency protocol is a core component of distributed data services that provide fault-tolerance and data consistency across distributed data centers and even edge networks. Raft is a popular approach due to its ease of implementation and superior performance. However, Raft adopts a sequential log entry processing strategy, where log entries without dependencies are not allowed to be processed in parallel, limiting system performance in high-concurrency scenarios. To address this challenge, researchers propose Raft-based protocols that supportout-of-orderapply(OOApply), which is called OORaft. Existing OORaft protocols adopt the Paxos-style election and replication process to merge missing entries on leader candidates. It leads to problems such as extra overhead on dependency analysis, availability when the network is partitioned, and incomplete correctness verification. This paper proposes aconciseparalleledRaftprotocol called CP-Raft, which is the first OORaft protocol to focus on dependency analysis overhead and to use full TLA$^+$validation for the leader election process. Specifically, 1) CP-Raft proposes a Raft-aligned three-step election method that significantly simplifies the difficulty of understanding and solves the availability problem when the network is partitioned. 2) CP-Raft applies a leader-side bitmap-based dependency analysis and representation method to break through the performance bottleneck caused by the high overhead of dependency analysis. 3) CP-Raft discusses why existing methods cannot achieve OORaft correctness verification using TLA$^+$in a limited time and uses phased verification methods to ensure its correctness. Finally, we implement CP-Raft based on an open-source Raft protocol and discuss its potential performance bottlenecks in various scenarios. The experimental results under high dependency strength workloads demonstrate that CP-Raft achieves 1.5× transaction per second (TPS) performance of DP-Raft and 2× of ParallelRaft-CE. It also provides better availability than state-of-the-art OORaft protocols.

The significance of traffic prediction in modern urban life has become increasingly prominent. Accurate traffic forecasting improves urban traffic management and enhances road resource utilization. In recent years, many models have introduced spatio-temporal contextual embeddings to distinguish between different time steps and spatial nodes. However, these models often overlook anomalous fluctuations in traffic data due to data imbalance. Consequently, performance declines when encountering uncommon situations, especially those caused by unexpected traffic accidents. To maintain overall performance while being aware of anomalous fluctuations, we propose STPDN, a dual-branch Spatio-Temporal Pattern Decomposition Graph Neural Network. Specifically, We introduce latent variables to characterize the distribution of latent patterns in traffic sequences, enabling the model to distinguish regular patterns and anomalous fluctuations without supervised information specifically targeting anomalies. Subsequently, we develop a resilient graph generator capable of producing dynamic spatio-temporal graphs, facilitating the propagation of impacts caused by dynamic fluctuations. Finally, we achieve more comprehensive and robust predictions by fusing regular patterns and anomalous fluctuations. Evaluation of real-world and simulated datasets shows that our model outperforms others, offering more reliable prediction solutions for urban traffic management systems, particularly in handling unforeseen traffic events. The code can be found at https://github.com/dhxdla/PyTorch-implementation-of-the-STPDN.git.

After trading the hardware cost and deployment complexity of inter-microservice communication architecture, the data plane of service mesh has gradually changed from sidecar mode to sidecarless mode. In sidecarless mode, the traffic of microservices need to be processed by zero-trust tunnel, so that the confidentiality, integrity and authentication of application data can be achieved. Thus, the efficiency of zero trust tunnel has a great impact on the performance of inter-microservices communication. However, current zero trust tunnel schemes require additional data transmission and processing, resulting in serious performance degradation. Thus, in this paper, we propose an efficient zero-trust tunnel which is called EZTunnel. Based on a programmable kernel, EZTunnel can execute L4 traffic management and security policies during system calls, thereby improving the communication performance between microservices. Through experiments under different traffic characteristic, we show that EZTunnel can achieve better inter-microservice request response delay, flow completion time and communication bandwidth than current zero-trust tunnel.

In recent years, the integration of Large Language Models (LLMs) with graph neural networks (GNNs) has opened new avenues in handling Text-Attributed Graphs (TAGs). This paper presents a novel approach leveraging LLMs for tackling TAG node classification problems, focusing on text augmentation and structural information enhancement through neighbor information integration. Our method employs a supervised fine-tuning process for LLMs with generated structure-ware rationales that involve structural information from TAGs. Through a combination of structure-aware rationale generation and alignment training, we enhance the learning and integration of graph structural information. We demonstrate the effectiveness of our approach across multiple datasets, showcasing improvements in node classification accuracy. Our contributions include the development of a self-guided approach to generate high-quality rationale text and the fine-tuning of a text embedding model for enhanced graph information understanding, ultimately feeding enriched features into a GNN for final node classification.

The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application. Compared to numerous debiasing methods targeting word level, there has been relatively less attention on biases present at phrase level, limiting the performance of debiasing in discipline domains. In this paper, we propose an automatic multi-token debiasing pipeline called General Phrase Debiaser, which is capable of mitigating phrase-level biases in masked language models. Specifically, our method consists of a phrase filter stage that generates stereotypical phrases from Wikipedia pages as well as a model debias stage that can debias models at the multi-token level to tackle bias challenges on phrases. The latter searches for prompts that trigger model’s bias, and then uses them for debiasing. State-of-the-art results on standard datasets and metrics show that our approach can significantly reduce gender biases on both career and multiple disciplines, across models with varying parameter sizes.

The adoption of Artificial Intelligence (AI) based Virtual Network Functions (VNFs) has witnessed significant growth, posing a critical challenge in orchestrating AI models within next-generation 6G networks. Finding optimal AI model placement is significantly more challenging than placing traditional software-based VNFs, due to the introduction of numerous uncertain factors by AI models, such as varying computing resource consumption, dynamic storage requirements, and changing model performance. To address the AI model placement problem under uncertainties, this paper presents a novel approach employing a sequence-to-sequence (S2S) neural network which considers uncertainty estimations. The S2S model, characterized by its encoding-decoding architecture, is designed to take the service chain with a number of AI models as input and produce the corresponding placement of each AI model. To address the introduced uncertainties, our methodology incorporates the orthonormal certificate module for uncertainty estimation and utilizes fuzzy logic for uncertainty representation, thereby enhancing the capabilities of the S2S model. Experiments demonstrate that the proposed method achieves competitive results across diverse AI model profiles, network environments, and service chain requests.

The Transport Layer Security (TLS) protocol has been widely used in software-defined perimeter (SDP) to establish secure, encrypted connections between distributed SDP components. To improve communication efficiency of its handshake protocol, the latest TLS standard (i.e., TLS 1.3) introduces a zero round-trip-time (0-RTT) handshake. However, traditional 0-RTT handshake protocols lack a forward secure key exchange scheme, so encrypted data that have already been transmitted could be potentially leaked to attackers after the pre-shared key (PSK) is compromised. To achieve secure TLS handshake with minimal communication cost, several forward secure 0-RTT key exchange schemes based on puncturable encryption were proposed. However, they are not applicable to real world SDP environments, because they either need to pre-store a large number of secret keys in the host onboard phase, or require a large number of complex cryptography operations (e.g., bilinear-pairing) in the access phase. Therefore, to avoid high computational overhead while still maintaining communication efficiency and forward security, a novel 0-RTT key exchange scheme based on efficient puncturable key encapsulation mechanism is proposed in this paper. Experimental results show that, with reasonable (and configurable) memory consumption, the latency performance of the proposed scheme is about 30% better than FFDHE3072, which is a practical 1-RTT key exchange scheme in TLS 1.3.

As Large Language Models (LLMs) continue to enhance their capabilities in multi-agent collaborative applications, the unpredictability of the generative content risks has intensified. Particularly in ongoing interaction scenarios with users, it remains unclear whether there is generative content risk drift over time. In this context, "drift risk" refers to the trend of progressively intensified content risk that emerges during sustained adversarial interactions among LLM agents. Additionally, the high cost associated with constructing complex adversarial environments for agents impedes the transferability of current assessment methods for LLMs to multi-agent adversarial scenarios. In this paper, we introduce a low-cost and lightweight framework for assessing content risk drift of LLMs, named CRDA. This framework, bypassing the need for constructing complex adversarial environments, offers a method that integrates roles and responses memory to guide automatically multi-round adversarial interactions among LLM agents, that is, multiple agents as avatars of a single LLM. In this approach, LLM agents enable the analysis of content risk drift of this LLM. Moreover, we explore the impact of restricted roles and the unsafe content with negative viewpoints in responses memory on the content risk drift of LLMs. Considering the rapid advancement of Chinese LLM capabilities, this study selects real adversarial topics in Chinese and assesses content risk drift of five representative Chinese LLMs. The research finds that these LLMs exhibit significant content risk drift even after a certain safety alignment, showing an initial increase followed by a gradual decrease. As the adversarial process progresses, under restricted roles, agents more effectively breach the model's safety alignment, leading to content risk drift of the LLM. The content drift risk assessment can be quantified specifically by measuring the deterioration rate at which LLM agents deteriorate from positive to negative and analyzing the underlying trends during the automatically multi-round adversarial interactions. In restricted and general roles adversarial interactions, all agents of five Chinese LLMs exhibit an overall average increase of 31.5% and 16.38% in the cumulative deterioration rate respectively by the 10th round, compared to the baseline no-roles adversarial interactions. Finally, we hope that the framework and findings presented in this paper will offer valuable insights for research on safety alignment in LLM agents during adversarial processes.

Graph anomaly detection aims to identify graph components, e.g., nodes and edges, that deviate significantly from normal distribution. However, we observe that there exist two challenges that degrade the detection performance, i.e., over-smoothing and over-fitting. First, as anomalies are only a small percentage of the entire dataset, the neighboring nodes of anomalous nodes are mostly normal. As a result, after aggregating multi-hops of neighboring nodes, the representations of anomalies are more similar to normal nodes, making them less distinguishable and causing the over-smoothing problem. Second, over-fitting arises from the lack of labels, caused by the high cost of labeling real-world data. In consequence, the supervision information could be insufficient, leading to model over-fitting. To deal with these two challenges, we propose a framework, named CL-GAD, that leverages cross-layer representations for graph anomaly detection. By utilizing information from different hops of neighboring nodes, we could capture the cross-layer information of anomalies, which is distinct from normal nodes, to solve the over-smoothing aggregation problem. To deal with the over-fitting problem, we introduce an alignment training design that enables the utilization of the vast amount of unlabeled data during training. We conduct extensive experiments on real-world datasets to evaluate our framework, and the results demonstrate its superiority over state-of-the-art baselines.

In the medical research domain, limited data and high annotation costs have made efficient classification under few-shot conditions a popular research area. This paper proposes a meta-learning framework, termed MedOptNet, for few-shot medical image classification. The framework enables the use of various high-performance convex optimization models as classifiers, such as multi-class kernel support vector machines, ridge regression, and other models. End-to-end training is then implemented using dual problems and differentiation in the paper. Additionally, various regularization techniques are employed to enhance the model's generalization capabilities. Experiments on the BreakHis, ISIC2018, and Pap smear medical few-shot datasets demonstrate that the MedOptNet framework outperforms benchmark models. Moreover, the model training time is also compared to prove its effectiveness in the paper, and an ablation study is conducted to validate the effectiveness of each module.

With the unprecedented scalability issues rising in vehicular edge computing (VEC), we argue in this paper that the scalability, along with the remarkable growth of demands for offloading, should be integrated into the modelling for effective offloading decision-making strategies requested by a large number of vehicles. A two-stage game-theory model can depict offloading decision-making strategies by considering both the revenue of network operators and the cost of VEC users. However, heuristic processes of solving such models show significant limitations in terms of high computational complexity and energy consumption due to the changing VEC environment. Therefore, our objective in this study is to solve the game-theory model efficiently and achieve scalable offloading for the changing VEC environment. We first develop a two-stage game-theory model for the offloading decision-making strategy for VEC, by which an operator’s revenue, energy consumption and latency are considered. Then a neural network (NN) model is designed to learn the predicted behaviours of the established game-theory model for offloading decisions in a more efficient manner. After that, a feature-based transfer learning algorithm is proposed for scalable offloading optimization under unseen VEC environments. Experimental results show that the proposed NN can significantly improve the efficiency of solving the game theory model, and the developed transfer learning approach can effectively achieve the scalability of offloading decisions in a changing VEC environment. The results demonstrate that the accuracy of the proposed transfer learning approach is 37% higher than that of several state-of-the-art algorithms, and the runtime halves.

In order to address the problem of log anomaly detection in scenarios with limited labeled log datasets, this paper proposes Log-MatchNet, a novel few-shot log anomaly detection method. To tackle issues such as unstructured log data, diversity, and evolution over time, we employ structured processing and log parsing to convert log content information and template ID into vectors. Feature extraction is performed using the BERT model. Additionally, by integrating multiple datasets and conducting post-training on the BERT model for domain adaptation, we obtain BERT_Post, a module with universal feature extraction capabilities in the log domain. Compared to BERTbase and CyBERT, our method demonstrates superior performance in log anomaly detection, especially in situations with limited labeled datasets. With only 2 annotated normal logs and 2 annotated abnormal logs, BERT_Post achieves a remarkable 16.14% increase in F1-score. Addressing the challenge of imbalanced data, we introduce a matching network that learns the similarity scores between input and prototype vectors, showcasing strong generalization capabilities with an average accuracy of 99.6%. In few-shot scenarios, our method, Log-MatchNet outperforms traditional methods and Proto-Siamese network in terms of F1-score. In an unstable log evolution environment, our method exhibits robustness against noisy data, achieving an F1-score of 81.2% even with 20% injected noise. Compared to LogAnMeta, our approach yields a 31.71% increase in F1-score. Experimental results demonstrate the effectiveness of Log-MatchNet in detecting anomalies in the presence of limited labeled log data and its robust performance in log evolution scenarios.

Existing studies for rumor detection rely heavily on a large number of labeled data to operate in a fully-supervised manner. However, manual data annotation in realistic cases is very expensive and time-consuming. In this paper, we propose a novel self-supervised Relation-Event based Contrastive Learning (RECL) framework for rumor detection to address the above issue. Specifically, we present both the relation-level and event-level augmentation strategies to generate contrastive samples, which capture both the semantics revealed by repost relations and the structural features of rumor events. Moreover, contrastive learning tasks are devised to generate informative graph representations by utilizing self-supervision signals of unlabeled data. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model, especially with limited labeled data.

In the era of big data, network data increase rapidly in a distributed manner, giving birth to the network big data. Network big data with the extra features such as distributed and decentralized data collection and storage, distributed and parallel data processing, more complex and evolving relationships among data, and heterogeneous data representation, pose opportunities together with challenges to the traditional network analysis algorithms. A hopeful solution is combining machine learning techniques with network big data analysis. In this paper, we proposed a novel time series clustering method which effectively combines machine learning techniques with network big data analysis for fault diagnosis task based on network logs. Verification experimental results on classic HDFS dataset demonstrate the outstanding performance of the proposed method.

Erasure codes have been widely applied to in-memory key-value storage systems for high reliability and low redundancy. In distributed in-memory key-value storage systems, update operations are relatively frequent, especially the partial-stripe update, which makes data update more challenging. Recently, existing research has been based on appending logs to accelerate parity data write. However, its logs are stored on disks, which decreases the system performance significantly. Therefore, we propose a novel in-memory key-value storage architecture, DNVPL, which utilizes NVRAM to log parity data. Our main idea is to design an appending-only update scheme to tradeoff the memory cost and the update overhead. We implement DNVPL with an in-memory key-value storage prototype, called LogKV. We evaluate it with different workloads. The experiments show that our scheme achieves high update performance from different metrics. Our scheme can reduce update latency by up to 49% and save storage space by 48% compared to the state-of-the-art schemes.

Data authorization is the basis for the orderly sharing of medical data. Most of the applied decentralized authorization mechanisms rely on blockchain, but face the problems of privacy leakage and low efficiency. To solve these problems, we design a policy-driven decentralized authorization system named BAA, which protects user's behavior privacy in medical data sharing and improves efficiency of both on-chain and off-chain. In order to achieve these goals, BAA uses authorization tokens to represent permissions, protects privacy of the authorization process through hiding user behaviors, realizes batch data accessing by proposing a two-tier Merkle tree, and saves authorization data in a two-tier blockchain to improve on-chain efficiency. Extensive experimental results show that the overhead of cryptographic operations in BAA is acceptable compared to that in traditional authorization systems. In addition, throughput and latency of each operation in BAA can meet the efficiency needs of medical data authorization. The results also show that the preset authorization in blockchain is effective for reducing data user's waiting time and improving the efficiency of authorization.

Due to cloud security concerns, there is an increasing interest in information retrieval systems that can support private queries over public documents. It is desirable for oblivious document ranking and retrieval in public cloud at lower cost and faster speed without revealing query-related information. Currently, the term frequency-inverse document frequency (TF-IDF) and private information retrieval (PIR) techniques are used to solve this problem, but the encryption operation time is over dominant. Motivated by the observation of the sparsity of the TF-IDF matrix, we propose an efficient approach for oblivious document ranking and retrieval, called E-Coeus. It takes advantage of the high sparsity of the TF-IDF matrix to rearrange the matrix. Our method accelerates the speed of PIR inadvertently retrieving documents and reduces the user retrieval delay time. In a stand-alone experiment for a TF-IDF matrix of 1.2M rows and 64K columns with the sparsity of 10%, E-Coeus improves the document ranking and retrieval performance by 23% over the state-of-the-art approach, Coeus. With cluster of 64 machines, E-Coeus improves the performance by 34% over Coeus when the TF-IDF matrix sparsity is 30%.

Benefit to the advantages of low cost, strong security, flexibility and high line-of-sight (LoS), UAVs constitute a promising platform to accomplish data collection for the IoT networks. However, it has to be admitted that the constrained energy consumption of UAVs has become the main challenge for the UAV-assisted IoT data collection. The existed works are mainly based on the assumption that the amount of data uploaded by different devices are the same, which makes the energy consumption of UAVs deviating from the real situation. Therefore, in this paper, for the sake of practical consideration, the amount of data uploaded varies from different devices in a multi-UAV-assisted IoT data collection scenario. To solve the problem of minimizing the total energy consumption of UAVs, we decouple it into two subproblems, devices clustering and UAVs trajectory planning, and propose an iterative optimization algorithm with energy and data volume constraints. Numerical results show that the performance of the proposed method outperforms the comparison schemes significantly in terms of saving total UAVs energy consumption and reducing the standard deviation of UAVs energy consumption.

With the rapid development of the Internet of Things (IoT), millions of IoT devices are constantly generating massive amounts of data. The development of artificial intelligence (AI) and edge computing makes it possible to conduct data analysis and knowledge mining efficiently among IoT edge devices. Knowledge, including intermediate results and training models obtained by large amounts of redundant data, is the core and foundation of edge intelligence. However, the sharing and utilization of knowledge still face a series of security and privacy issues, such as illegal knowledge access, knowledge tampering, and privacy leakage. To address these issues, in this article, we propose a blockchain-based knowledge storage and sharing architecture that enables secure knowledge management in intelligent IoT. We first design a permissioned blockchain-based decentralized and trusted knowledge storage scheme, which includes the on-chain encrypted knowledge storage and an improved Delegated Proof of Stake (DPoS) consensus protocol. Besides, we propose a lightweight attribute-based searchable encrypted knowledge-sharing mechanism, in which fine-grained and privacy-preserving knowledge collaboration is achieved through smart contracts and keyword search. Moreover, we reduce the computing overhead of edge devices through the design of partial outsourcing decryption. Finally, we analyze the security performance of our system as well as verify its practicality and ability to reject dishonest servers by simulation.

Due to advancements in semiconductor technologies, Internet of Things (IoT) applications have penetrated into a wide spectrum of aspects of human lives. This widespread penetration is also thanks to significant contributions from many emerging technologies, e.g., artificial intelligence (AI) and blockchain[1],[2]. The fast development of AI technologies like deep learning is a promising approach for extracting accurate information from massive raw sensor data in IoT applications[3]. In addition, due to its tamper-proof characteristic and distributed nature, blockchain has received increasing attentions in emerging IoT applications to tackle security and privacy issues[4],[5]. AI and blockchain have become killer technologies to advance the fast development of IoT ecosystems with incredible growth, impact, and potential.

Online reviews play a significant role in purchase decisions of consumers by providing feedback information from buyers of products. In order to mislead consumers, opinion spammers are hired to write fake reviews to promote or demote specific products for illegitimate benefits. Existing methods for spam review detection mainly focused on designing manual features, which highly rely on expert knowledge. Although recent works utilized deep learning methods to automatically learn the semantics of reviews through the inherent user-review-product strong relation, they fail to capture the weak relations between reviews at the content, sentiment and temporal levels, which provides various semantic information to expose fake reviews. Moreover, the imbalanced class distribution in spam detection issues makes this work even more challenging. To address the above problems, we propose a novel Weak-Strong Unified Network (WSUN) for opinion spam detection. Multi-level weak relation graphs are constructed to reveal the abnormal behavioral patterns of spammers, which aggregates the semantics of strong relations by graph convolutional networks and extracts comprehensive review representation by utilizing relation-level attention mechanism. In addition, a graph-based over-sampling method is devised to mitigate the impact of imbalanced class distribution. Extensive experimental results on real-world datasets show that our model is more effective than the state-of-the-art methods.

Synthetic aperture radar (SAR) images often suffer from speckle noise, which can degrade their visual quality and affect downstream applications. Recently, supervised and self-supervised methods have been proposed by virtue of deep learning with synthetic “noisy-clean” image pairs or only real noisy images as training data, respectively. Among these methods, by avoiding artifact problems in real SAR image denoising, self-supervised methods solve the domain gap problem of supervised methods and hence have attracted significant attention. However, existing self-supervised denoising methods essentially rely on pixel information of images and ignore corresponding semantic information, which makes them challenging to remove speckle noise while retaining detailed features. To this end, we propose a segmentation-guided semantic-aware self-supervised denoising method for SAR images, namely SARDeSeg, where a segmentation network is incorporated with a denoising network and guides it to learn and be aware of the semantic information of the input noisy SAR images. Additionally, a wavelet transform-based connector is introduced to efficiently transmit semantic information between the denoising network and the segmentation network, together with an edge-aware smoothing loss to improve speckle noise suppression while preserving edge features. Experimental results demonstrate that the proposed SARDeSeg outperforms state-of-the-art denoising methods for SAR images, particularly in preserving detailed edge features.

Today's Industrial Internet of Things (IIoT) have achieved excellent manufacturing efficiency and automation results by leveraging machine learning (ML) and deep learning (DL). However, trustworthiness of ML/DL brings significant challenges to IIoT. This article proposes an evolving deep multiple kernel learning network through genetic algorithm (KNGA). Our KNGA method uses genetic algorithm (GA) to find the best deep multiple kernel learning structure, including the weights and the topology of the model. Compared with the current well-known models, KNGA has advantages in three aspects: 1) It can achieve good results without using many samples during model training; 2) the model can evolve in the process of training, including self-growth, and self-pruning; and 3) its trustworthiness and reliability can be guaranteed. Moreover, the whole model ensures excellent performance and requires manual adjustment of only a few parameters. Extensive experiments on the UCI, KEEL, Caltech256, and MNIST datasets demonstrate the effectiveness and trustworthiness of the proposed method.

The continuous development of Healthcare 4.0 has brought great convenience to people. Through the Internet of Things technology, doctors can analyze patients’ health data and make timely diagnosis. However, behind the high efficiency, the mobile crowdsensing technology used for data transmission still has the risk of leaking the privacy of task and patient information. To this end, this article proposes a privacy-enhanced multi-area task assignment strategy, named PMTA. Specifically, we use deep differential privacy to add noise to patient data, and then put the noise-added dataset into a deep Q-network for training, combined with a spectral clustering algorithm, to obtain an optimal classification strategy. Further, in order to address the problem of data silos, we adopt federated learning to jointly train the classification models of different hospitals to obtain a global model and realize data sharing among different hospitals. Finally, we use the optimal classification of patients for task deployment on the blockchain, and limit patients to only apply for tasks of the corresponding level through the smart contract technology, so as to protect task privacy. Experimental results show that our strategy can not only effectively protect task and patient privacy, but also achieve better system performance.

In recent years, the concept of Zero Trust Networks (ZTN) has been proposed to overcome unrealistic security assumptions, e.g., what lies in private networks (such as data centers) is always trusted and safe. In ZTN, no device or user is assumed to be secure, instead all connections have to be authenticated and authorized before being established. Software Defined Perimeter (SDP) is one of the most promising solution for ZTN, where the gateway allows clients to access services only after receiving legitimate Single Packet Authorization (SPA) data. However, existing SDP solutions either (1) need to decouple the SPA from the connection request, resulting in redundant communication processes and impersonation attacks; or (2) need to copy the SPA data to the user space from sniffers, causing the packets to enter the protocol stack repeatedly. Due to the large number of short-lived streams in the data center, inefficiency and insecurity of the SPA process lead to severe connection delays and network attacks (e.g., DDoS). To this end, we propose an eBPF-based Dynamic Perimeter (EDP) to enhance the security and performance of SDP. By using EDP, authentication data can be efficiently embedded into every packet and checked before entering the receiver's protocol stack. Experimental results show that the connection delay of EDP is 80% less than that of the existing state-of-the-art solutions.

Urban anomaly adversely affects the quality of human life and even the security of urban residents. Effective prediction of urban anomalies is crucial to public safety, traffic management, urban planning, etc. However, the region division, the complex spatial-temporal correlations, and the cross interaction of multiple dynamics make this difficult. In this paper, urban anomaly data collected by crowdsourcing is used to perform anomaly prediction together with external factors. To this end, a Spatial-Temporal Cross Interaction Networks (STCIN) model is proposed, with a novel urban graph construction method and a new mechanism to build the spatial-temporal correlation between regions with multiple dynamics. STCIN is able to exploit both current region features and historical urban information for urban anomaly prediction. Experimental results validate the superiority of the proposed method in comparison with five related works on four real-world datasets. Using the dataset of Street Condition, STCIN can increase the F1-score of anomaly prediction by 5.7% comparing with the state-of-the-art WaveNet (Graph wavenet for deep spatial-temporal graph modeling).

Edge computing is the next-generation computing paradigm that brings the processing capability closer to the location where it is needed. 5G and beyond 5G aim to achieve substantial improvement for the performance of edge computing in terms of e.g. higher throughput and lower latency. Smart base stations are often attached with edge datacenters consisting of many edge servers equipped with computing and storage capabilities. These servers are used to execute offloaded tasks from edge equipment such as Internet of Things. It is important to have an efficient offloading algorithm that can guarantee specific service-level objectives (SLOs) by assigning tasks to appropriate edge servers. Traditional offloading schemes such as static and learning-based algorithms either have limited performance or result in high overhead for task assignment to servers. In this paper, we propose an efficient game-theoretical scheduling algorithm for offloaded tasks at edge datacenters. The core contribution of the algorithm is to design a public goods investment model for edge servers. Based on the model, we design a lightweight scheduling algorithm to reduce the average load of edge servers and enhance the stability of edge datacenter systems. Experimental results demonstrate the significant benefits of the proposed algorithm in reducing the response latency of tasks and balancing the workload of edge servers.

Distributed data services usually rely on consensus protocols like Paxos and Raft to provide fault-tolerance and data consistency across global and local-distributed data centers. Erasure coding replication has appealing storage and network cost saving compared with full copy replication, which helps consensus protocols achieve low latency, high fault tolerance, and high throughput for data access. Applying erasure coding in consensus protocols directly will degrade the liveness level when the number of failure servers reaches a certain level. To address the challenge, CRaft just stores full copy replication instead of erasure coding replication when the number of failed servers reaches a certain threshold. In such situation, CRaft will be downgraded sharply to the same storage and network costs as Raft. To overcome the shortcoming of CRaft, we propose a protocol, called HRaft, which can adapt the placement of data blocks in order to always have enough blocks to recover the stored value when servers fail. By replenishing some coded blocks in healthy servers instead of full copy replication, it can avoid switching to the full replication when a certain threshold on the number of failures is reached. We designed and implemented a key-value (KV) storage prototype to validate the proposed protocol and evaluate its performance. The experimental results show HRaft can significantly reduce storage and network costs and improve write performance while keeping the liveness level compared to CRaft.

The Digital Twin Network (DTN) is a key enabling technology for efficient and intelligent network management in modern communication networks. Learning dynamic net-work behaviors at the flow granularity is a core element for realizing DTN with accurate network modelling. However, it is challenging due to the complexity of network architectures and the proliferation of emerging network applications. In this paper, we devise a Packet-Action Sequence Model to represent all possible packets behaviors in a unified way. Besides, we propose a novel and effective algorithm to assess whether the behavior pattern is time dependent or independent by using the temporal characteristics of packets in a network flow, so as to learn the key factors of packets that contribute to network behaviors. Based on two typical scenarios, i.e., packet caching and routing, the experimental results verify that the proposed algorithm can identify network behavior patterns and learn key factors affecting the behaviors with over 99 % accuracy.

Video cameras have been deployed widely today. Although existing systems aim to optimize live video analytics from a variety of perspectives, they are agnostic to the workload dynamics in real-world. We propose EC-MASS, an edge computing-based video scheduling system achieving both cost and performance optimization with multiple cameras and edge data centers . The intuition behind EC-MASS is to adaptively map cameras to different edge data centers according to dynamically updated configurations of cameras. We prove that generating the optimal mapping scheduling scheme is NP-Complete, and develop the scheduling algorithm by leveraging the insights of the economy consideration of camera allocation. Using the algorithm, EC-MASS is able to balance the workload among edge data centers while reducing the cost of video analytics system . We evaluate EC-MASS with datasets of video configurations from real-world cameras which randomly generate configurations for cameras, with a testbed that consists of 60 cameras and 4 edge data centers . Our results show that EC-MASS consistently outperforms the status quo in terms of cost and performance stability.

The Industrial Internet of Things (IIoT) plays an important role in digital transformation of traditional industries toward Industry 4.0. By connecting sensors, instruments, and other industry devices to the Internet, IIoT facilitates the data collection, data analysis, and automated control, thereby improving the productivity and efficiency of the business as well as the resulting economic benefits. Due to the complex IIoT infrastructure, anomaly detection becomes an important tool to ensure the success of IIoT. Due to the nature of IIoT, graph-level anomaly detection has been a promising means to detect and predict anomalies in many different domains, such as transportation, energy, and factory, as well as for dynamically evolving networks. This article provides a useful investigation on graph neural networks (GNNs) for anomaly detection in IIoT-enabled smart transportation, smart energy, and smart factory. In addition to the GNN-empowered anomaly detection solutions on point, contextual, and collective types of anomalies, useful data sets, challenges, and open issues for each type of anomalies in the three identified industry sectors (i.e., smart transportation, smart energy, and smart factory) are also provided and discussed, which will be useful for future research in this area. To demonstrate the use of GNN in concrete scenarios, we show three case studies in smart transportation, smart energy, and smart factory, respectively.

As a powerful architecture for large-scale computation, cloud computing has revolutionized the way that computing infrastructure is abstracted and utilized. Coupled with the challenges caused by Big Data, the rocketing development of cloud computing boosts the complexity of system management and maintenance, resulting in weakened trustworthiness of cloud services. To cope with this problem, a compelling method, i.e., Support Vector Data Description (SVDD), is investigated in this paper for detecting anomalous performance metrics of cloud services. Although competent in general anomaly detection, SVDD suffers from unsatisfactory false alarm rate and computational complexity in time series anomaly detection, which considerably hinders its practical applications. Therefore, this paper proposes a relaxed form of linear programming SVDD (RLPSVDD) and presents important insights into parameter selection for practical time series anomaly detection in order to monitor the operations of cloud services. Experiments on the Iris dataset and the Yahoo benchmark datasets validate the effectiveness of our approaches. Furthermore, the comparison of RLPSVDD and the methods obtained from Twitter, Numenta, Etsy and Yahoo, shows the overall preference for RLPSVDD in time series anomaly detection.

Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network management due to virtualized infrastructure and stringent quality-of-service requirements. Digital twin (DT) technology paves a way for achieving cost-efficient and performance-optimal management, through creating a virtual representation of slicing-enabled networks digitally to simulate its behaviors and predict the time-varying performance. In this article, a scalable DT of network slicing is developed, aiming to capture the intertwined relationships among slices and monitor the end-to-end (E2E) metrics of slices under diverse network environments. The proposed DT exploits the novel graph neural network model that can learn insights directly from slicing-enabled networks represented by non-Euclidean graph structures. Experimental results show that the DT can accurately mirror the network behaviour and predict E2E latency under various topologies and unseen environments.

introduction Share on Introduction to the Special Section on Resiliency for AI-enabled Smart Critical Infrastructures for 5G and Beyond Authors: Laizhong Cui Shenzhen University, China Shenzhen University, ChinaView Profile , Yulei Wu University of Exeter, UK University of Exeter, UKView Profile , Ryan Ko University of Queensland, Australia University of Queensland, AustraliaView Profile , Alex Ladur CTEK - Combined Technologies Ltd, New Zealand CTEK - Combined Technologies Ltd, New ZealandView Profile , Jianping Wu Tsinghua University, China Tsinghua University, ChinaView Profile Authors Info & Claims ACM Transactions on Sensor NetworksVolume 18Issue 319 September 2022Article No.: 40epp 1–3https://doi.org/10.1145/3538515Published:19 September 2022Publication History 0citation78DownloadsMetricsTotal Citations0Total Downloads78Last 12 Months78Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Many anomaly detection techniques have been adopted by Industrial Internet of Things (IIoT) for improving self-diagnosing efficiency and infrastructures security. However, they are usually associated with the issues of computational-hungry and “black box.” Thus, it becomes important to ensure that the detection is not only accurate but also energy-efficient and trustworthy. In this article, we propose an Energy-efficient And Trustworthy Unsupervised anomaly detection framework (EATU) for IIoT. The framework consists of two levels of feature extraction: (1) Autoencoder-based feature extraction and (2) Efficient DeepExplainer-based explainable feature selection. We propose an Efficient DeepExplainer model based on perturbation-focused sampling, which demonstrates the most computational efficiency among state-of-the-art explainable models. With the important features selected by Efficient DeepExplainer, the rationale of why an anomaly detection decision was made is given, enhancing the trustworthiness of the detection as well as improving the accuracy of anomaly detection. Three real-world IIoT datasets with high-dimensional features are used to validate the effectiveness of the proposed framework. Extensive experimental results demonstrate that in comparison with the state-of-the-art, our framework has the attributes of improved accuracy, trustworthiness (in terms of correctness and stability of the explanation), and energy-efficiency (in terms of wall-clock-time and resource usage).

Offloading decisions in mobile edge computing have been extended with multiple objectives, such as revenue maximization, energy conservation and latency reduction. Revenues of network/service operators, as the realistic and ultimate goal at intensive competitive markets, have not been thoroughly studied under a pricing scheme in combination with offloading decisions, especially with the aims of reducing and restricting energy consumption and latency. To bridge this important gap, this paper studies the revenue maximization of network operators through a pricing scheme in mobile edge computing, by explicitly formulating energy consumption and latency into the offloading strategy. A two-stage game-theory framework based on the Stackelberg game is established, through which the optimal price for both the network operator and the customer can be reached. The offloading data size can be dynamically adjusted according to the agreed price. The existence of equilibrium in the Stackelberg game is proved, and experiments are conducted to verify the effectiveness of our proposed model.

Measuring the similarity between trajectories is fundamental to many location-aware applications. However, the trajectory data collected in the real world suffer from the low-quality problem caused by non-uniform sampling rates and noises, which significantly affects the accuracy of similarity measurement. Traditional pairwise point-matching methods are susceptible to non-uniform sampling rates inherently, since they assume the consistent sampling rate. Although the recurrent neural network (RNN) based methods have addressed this problem by complementing trajectory data, they have the drawback of predicting positions conditioned on the historical data generated by the model itself, which could lead to accumulated bias during the inference stage. In this paper, we propose a novel generative model to address the important issue of low-quality trajectory data based on single image super-resolution. The trajectory similarity is thus computed using trajectory images instead of the trajectory sequential data. By utilizing the images to represent trajectories, we effectively overcome the issues encountered by existing methods. Extensive experimental results using real-world datasets demonstrate that our method outperforms existing methods in terms of accuracy.

Large-scale software systems are generally deployed on distributed machines. Logs are usually collected from those machines for comprehensive and accurate system fault analysis. However, there are potential challenges during log transmission from distributed machines to third-party data analytics services. First, uploading massive raw logs causes tremendous bandwidth consumption. Moreover, user privacy contained in logs is easy to get leaked during transmission. To address these issues, we introduce federated learning for anomaly detection using distributed log data. However, gradient updates of model parameters transmitted between the server (third-party data analytics services) and participants (distributed machines) in federated learning have been proved of possible recovery by attackers, so encryption of gradient updates is necessary for enhanced privacy protection. Considering that encryption time is proportional to the number of parameters, we propose a lightweight federated learning method for anomaly detection, named FLOGCNN, using distributed log data. The sever in FLOGCNN aggregates gradient updates according to the sample size of participants to generate an integrated model. For local training, participants apply an anomaly detection model based on one-dimensional convolution with much fewer parameters. Extensive experiments are conducted for FLOGCNN using open log datasets. Results demonstrate that FLOGCNN outperforms baseline methods on anomaly detection and reduces 97.08% parameters in comparison with one baseline method. Furthermore, we perform exploratory experiments on lightweight models and results manifest that logs with simple semantic information are suitable for lightweight anomaly detection models.

Due to the ever-increasing complexity of modern communication networks, network operators are making tremendous efforts on achieving objectives for the network to meet the diversified requirements of many real-world applications. However, network operators are repeatedly taking a lot of time on some common tasks shared by different networks. In order to reduce repetitive human efforts on network management, advanced machine learning paradigms, such as deep reinforcement learning, has received numerous attention in the networking community. Nevertheless, it encounters great difficulty in transferring learned policies to new environments, resulting in new model training and testing for each changed environment setting. To tackle this important issue, in this paper we propose a new framework that is the first of its kind to enable an agent to have transferable knowledge for network management, specifically, for network path selection tasks. Through this framework, an agent can efficiently learn and express the transferable network knowledge for achieving task objectives. Extensive experimental results show that the learned knowledge through the proposed framework can realize some common objectives of path selection tasks across different network environments. In addition, the knowledge learned from one network task can significantly improve the learning performance of another similar but different task.

The Internet of Things (IoT) has been widely adopted in a range of verticals, e.g., automation, health, energy, and manufacturing. Many of the applications in these sectors, such as self-driving cars and remote surgery, are critical and high stakes applications, calling for advanced machine learning (ML) models for data analytics. Essentially, the training and testing data that are collected by massive IoT devices may contain noise (e.g., abnormal data, incorrect labels, and incomplete information) and adversarial examples. This requires high robustness of ML models to make reliable decisions for IoT applications. The research of robust ML has received tremendous attention from both academia and industry in recent years. This article will investigate the state of the art and representative works of robust ML models that can enable high resilience and reliability of IoT intelligence. Two aspects of robustness will be focused on, i.e., when the training data of ML models contain noises and adversarial examples, which may typically happen in many real-world IoT scenarios. In addition, the reliability of both neural networks and reinforcement learning framework will be investigated. Both of these two ML paradigms have been widely used in handling data in IoT scenarios. The potential research challenges and open issues will be discussed to provide future research directions.

The Internet of Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing, and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralized and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This article first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.

Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to the ever-increasing number of Internet-connected Internet-of-Things (IoT)/Industrial IoT (IIoT), and the high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this article, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on the state of the art for security and privacy and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

Online social networks (OSNs) have become so popular that it has changed the Internet to a more collaborative environment. Now, a third of the world's population participates in OSNs, forming communities, and producing and consuming media in different ways. The recent boom of artificial intelligence technologies provides new opportunities to help improve the processing and mining of social data. In this article, an algorithm that can detect communities in the OSNs using the concepts of granular computing in rough sets is proposed. In this information model, a social network as a rough set granular social network (RGSN) is modeled. A new community detection algorithm named granular-based community detection (GBCD) is implemented. This article also defines and uses two measures, namely, a granular community factor and an object community factor. The proposed algorithm is evaluated on four real-world data sets as well as computer-generated data sets. The model is compared with other state-of-the-art community detection algorithms for the values of modularity, normalized mutual information (NMI), Omega index, accuracy, specificity, sensitivity, and F1-measure. The cumulative performance of the GBCD algorithm is found to be 3.99, which outperforms other state-of-the-art community detection algorithms.

It is agreed that portfolio selection models are of great importance for the financial market. In this article, a constrained multiperiod multiobjective portfolio model is established. This model introduces several constraints to reflect the trading restrictions and quantifies future security returns by fuzzy random variables to capture fuzzy and random uncertainties in the financial market. Meanwhile, it considers terminal wealth, conditional value at risk (CVaR), and skewness as tricriteria for decision making. Obviously, the proposed model is computationally challenging. This situation gets worse when investors are interested in a larger financial market since the data they need to analyze may constitute typical big data. Whereafter, a novel intelligent hybrid algorithm is devised to solve the presented model. In this algorithm, the uncertain objectives of the model are approximated by a simulated annealing resilient back propagation (SARPROP) neural network which is trained on the data provided by fuzzy random simulation. An improved imperialist competitive algorithm, named IFMOICA, is designed to search the solution space. The intelligent hybrid algorithm is compared with the one obtained by combining NSGA-II, SARPROP neural network, and fuzzy random simulation. The results demonstrate that the proposed algorithm significantly outperforms the compared one not only in the running time but also in the quality of obtained Pareto frontier. To improve the computational efficiency and handle the large scale securities data, the algorithm is parallelized using MPI. The conducted experiments illustrate that the parallel algorithm is scalable and can solve the model with the size of securities more than 400 in an acceptable time.

This special section aims at soliciting original research and practical contributions from both industry and academia to advance the IIoT, including network modeling and architecture, AI algorithms for various layers, intelligent resource management, big data driven edge systems, orchestration of edge, and cloud servers. Through a rigorous peer-review process, nine articles have been accepted. In the following, we summarize the accepted articles in this editorial.

Nowadays, surveillance cameras have been pervasively equipped with vehicles in public transport systems. For the sake of public security, it is crucial to upload recorded surveillance videos to remote servers timely for backup and necessary video analytics. However, continuously uploading video content generated by tens of thousands of vehicles can be extremely bandwidth consuming. In this work, we investigate the video uploading problem for moving buses by proposing to deploy dedicated access points (AP) at bus stops to facilitate video uploading. We define the harmonic objective for our problem, which includes minimizing the video uploading delay and minimizing the AP deployment cost. This problem is with two fundamental challenges. Firstly, it is difficult to balance the bandwidth capacity allocated to many buses because a bus obtains bandwidth resource from a series of APs deployed at stops along its route. Secondly, due to the randomness of bus movement and the complexity of bus routes, it is hard to predict the workload of an AP. Hence, it is challenging to estimate the delay of uploading video content through an AP. To cope with these challenges, we propose a water filling placement (WFP) algorithm, aiming to balance the aggregated bandwidth allocated to each bus. A queuing model is established to analyze the uploading delay of video content. We further resort to machine learning models to factor the influence of bus routes into our queuing model. Finally, a convex problem is formulated to optimize the harmonic objective, which can be optimally solved with the gradient descent (GD) based algorithm. We validate the correctness of our theoretical analysis and demonstrate the effectiveness of our method by carrying out extensive experiments using bus traces collected in Shenzhen city of China. In comparison with benchmark algorithms, our solution can always achieve the best performance.

Virtual network embedding (VNE) that instantiates virtualized networks on a substrate infrastructure, is one of the key research problems for network virtualization. Most existing VNE approaches, however, focus on the current virtual network request (VNR) and treat all VNRs equally, which disregard the long-term impact and waste many resources on the process of embedding infeasible VNRs (i.e., VNRs that cannot be embedded completely). To address these problems, a proactive virtual network embedding algorithm based on hierarchical reinforcement learning, VNE-HRL, is proposed in this paper. Within our framework, the VNE task is performed by a two-level agent that considers both the long-term impact of a VNR and the short-term effect of an embedding action. For each processing, a high-level agent aims to select a currently feasible VNR with the maximum long-term reward from a window-based batch, and a low-level agent is assigned to embed the selected VNR on a substrate infrastructure by performing a series of embedding actions. Extensive simulation results indicate that our algorithm best performance on most metrics compared with existing state-of-the-art solutions, with up to 9.92% and 33.03% improvement on acceptance ratio and average revenue.

Distributed peer-to-peer power energy markets are emerging quickly. Due to central governance and lack of effective information aggregation mechanisms, energy trading cannot be efficiently scheduled and tracked. We devise a new distributed energy transaction system over the energy Industrial Internet of Things based on predictive analytics, blockchain, and smart contract technologies. We propose a solution for scheduling distributed energy sources based on the Minimum Cut Maximum Flow theory. Blockchain is used to record transactions and reach consensus. Payment clearing for the actual power consumption is executed via smart contracts. Experimental results on real data show that our solution is practical and achieves a lower total cost for power energy consumption.

Mobile social networks (MSNs) provide real-time information services to individuals in social communities through mobile devices. However, due to their high openness and autonomy, MSNs have been suffering from rampant rumors, fraudulent activities, and other types of misuses. To mitigate such threats, it is urgent to control the spread of fraud information. The research challenge is: how to design control strategies to efficiently utilize limited resources and meanwhile minimize individuals' losses caused by fraud information? To this end, we model the fraud information control issue as an optimal control problem, in which the control resources consumption for implementing control strategies and the losses of individuals are jointly taken as a constraint called total cost, and the minimum total cost becomes the objective function. Based on the optimal control theory, we devise the optimal dynamic allocation of control strategies. Besides, a dynamics model for fraud information diffusion is established by considering the uncertain mental state of individuals, we investigate the trend of fraud information diffusion and the stability of the dynamics model. Our simulation study shows that the proposed optimal control strategies can effectively inhibit the diffusion of fraud information while incurring the smallest total cost. Compared with other control strategies, the control effect of the proposed optimal control strategies is about 10% higher.

The fast growth of encrypted traffic puts forward burning requirements on the efficiency of traffic classification. Although deep learning models perform well in the classification, they sacrifice the efficiency to obtain high-precision results. To reduce the resource and time consumption, a novel and lightweight model is proposed in this paper. Our design principle is to “maximize the reuse of thin modules A thin module adopts the multi-head attention and the 1D convolutional network. Attributed to the one-step interaction of all packets and the parallelized computation of the multi-head attention mechanism, a key advantage of our model is that the number of parameters and running time are significantly reduced. In addition, the effectiveness and efficiency of 1D convolutional networks are proved in traffic classification. Besides, the proposed model can work well in a real time manner, since only three consecutive packets of a flow are needed. To improve the stability of the model, the designed network is trained with the aid of ResNet, layer normalization and learning rate warm up. The proposed model outperforms the state-of-the-art works based on deep learning on two public datasets. The results show that our model has higher accuracy and running efficiency, while the number of parameters used is 1.8% of the 1D convolutional network and the training time halves.

Aiming at the problem of trace anonymization performance of backbone networks, we propose a real-time anonymization method for the IP address of backbone network packets based on flow tables (named AFT-Anon). This method can dynamically build an anonymous flow table based on the captured data packets. The first data packet of a network flow is encrypted according to a specific encryption algorithm, and the encrypted fields are stored in the flow record. Subsequent data packets can obtain the encrypted fields by searching flow records and replace the corresponding fields of the original data packets to achieve anonymization of data packets. Based on the proposed method, a high-speed network anonymization system is developed and deployed on the backbone links of an Internet service provider. Experimental results show that the proposed method can improve the anonymization performance by more than 20 times, compared with the existing methods such as Crypto-Pan, and it can meet the requirements for online anonymization of 10G link.

With the rapid development of hardware and software technologies, an increasing number of mobile devices, eg, smartphones and tablets get connected to the Internet, which results in a large proportion of traffic attributed to mobile devices.1 The mobile users have therefore increasingly high demands to access the Internet with guaranteed Quality-of-Experience.2, 3 Software-Defined Networking (SDN)4, 5 is an emerging network architecture where network control is decoupled from data forwarding and provides a powerful tool for fine-grained network management. The SDN-based network virtualization techniques have been widely used by Internet Service Providers to facilitate the service delivery with such performance guarantees, where network routers and switches are dynamically coordinated under the SDN APIs in wired networks.6 With the rapid advancement of wireless network technologies, many last mile connections go for wireless. However, the SDN-based wireless network virtualization is not straightforward. Many research challenges still need to be resolved before practical large-scale deployment. The accepted eight papers in this special issue are devoted to addressing the state-of-the-art technologies related to SDN, wireless network, network virtualization, security issues, and performance improvements. These papers can be organized under the following key themes: trust model and management, SDN security, and network performance optimization. The contributions of these papers are outlined below. There are two accepted papers aiming at establishing frameworks for trust models, management, and methodologies. The paper “A Trust Management Framework for Software-Defined Network Applications” proposed a trust management framework for SDN applications.7 It evaluates the applications' trust values according to their impact on the network performance. With the prototype system based on a floodlight controller, the framework is shown to be accurate and effective. Different from the above work, the paper “Graph Encryption for All-path Queries”8 aimed at providing the ability to store sensitive graph data to untrusted servers. A searchable symmetric encryption scheme is proposed to support all-path queries. The proposed scheme is proved to be able to adaptively semantically secure in the semihost settings. The scheme can be widely applied in the network virtualisation as graph data structures are commonly used to represent the topology of substrate and virtual networks. Security is one of the most important topics in SDNs. There is one accepted paper in this issue focusing on this topic. The paper “A Comprehensive Survey of Security Threats and Their Mitigation Techniques for Next-Generation SDN Controllers”9 provided a comprehensive survey of the security threats and corresponding mitigation techniques for SDN controllers. In this survey, a detailed classification for various security attacks on the control plane has been provided. Besides, the latest corresponding mitigation techniques are also presented and summarized. Based on the survey, future directions on a number of security issues such as policy violation and malware injection are discussed. DDoS attack is one of the most popular and threatening attacks for SDNs. Two accepted works are focused on this specific topic and have made contributions from different aspects. In the paper “An Intelligent Trust Model for Hybrid DDoS Detection in Software Defined Networks”,10 a trust evaluation and management model for hybrid DDoS detection in SDNs is proposed, where the extreme learning machine is applied. The proposed model can monitor the trust values of the OpenFlow switches in real time and thus can respond to different types of DDoS attacks. The model provides a more efficient alternative for DDoS detection in SDNs. The paper “Machine Learning Algorithms to Detect DDoS Attacks in SDN”11 managed to exploit different kinds of machine learning algorithms to avoid three kinds of DDoS attacks (controller attack, flow table attack, and bandwidth attack). Compared to the above work on trust model, this paper deals with the problem in a different way and considers more kinds of attacks. SDN and NFV have attracted increasing research attentions in recent years. The various network resources, traffic patterns, user demands, and protocol policies have significant impact on the network performance, which makes the performance optimization a challenging problem. The paper “The Optimization of Virtual Resource Allocation in Cloud Computing Based on RBPSO”12 considered the task requirements from different users to manage virtual machines. A novel algorithm with resampled binary particle swarm optimization is proposed. Compared with the existing works, the efficiency of the proposed algorithm is improved, the population diversity is maintained, and redundant calculations are reduced. The paper “Traffic Modeling and Performance Evaluation of SDN-Based NB-IoT Access Network”13 specifically considered the impact of network traffic in the SDN-based Narrowband Internet of Things (NB-IoT) network. SDN-enabled NB-IoT is discussed in this paper. Besides, the network performance is evaluated and modeled using various network parameters in the NB-IoT networks. The analysis and simulation results can be used in the SDN controller to dynamically allocate resources and make network management decisions to satisfy different performance requirements of NB-IoT applications. The paper “LBBESA: An Efficient Software-Defined Networking Load-Balancing Scheme Based on Elevator Scheduling Algorithm”14 dealt with the problem of load balancing in SDNs, based on the elevator scheduling algorithm. According to the real-time measurement of the server loads, the regional elevator allocation is applied to coordinate the connection of the clients' requests. The throughput and scalability are improved. The articles presented in this special issue have provided insights in fields related to SDN, wireless networks, network virtualization, and network security, including the trust models and methodologies, SDN DDoS attacks, and performance optimization for various SDN-enabled networks. We wish the readers can benefit from the insights of these papers and contribute to these rapidly growing areas. We would like to express our deep thanks to the Editor-in-Chief, Professor Geoffrey Fox, for providing us with the opportunity to host this special issue in Concurrency and Computation: Practice and Experience. We also thank all the authors who submitted their papers. Last but not least, we thank the thoughtful work of the many reviewers who have provided invaluable evaluations and recommendations.

The Internet of Things (IoT) increases the number of connected devices and supports the ever-growing complexity of applications. Owing to the constrained physical size, the IoT devices can significantly enhance the computational capacity by offloading computation-intensive tasks to the resource-rich edge servers deployed at the base station (BS) via wireless networks. However, how to achieve optimal resource scheduling remains a challenge due to stochastic task arrivals, time-varying wireless channels, and imperfect estimation of channel state information (CSI). In this article, by virtue of the Lyapunov optimization technique, we propose the toward optimal resource scheduling algorithm under imperfect CSI (TORS) to optimize resource scheduling in an IoT environment. A convex transmit power and subchannel allocation problem in TORS is formulated. This problem is then solved via the Lagrangian dual decomposition method. We derive analytical bounds for the time-averaged system throughput and queue backlog. We show that TORS can arbitrarily approach the optimal system throughput by simply tuning an introduced control parameter $\beta $ without prior knowledge of stochastic task arrivals and the CSI of wireless channels. Extensive simulation results confirm the theoretical analysis on the performance of TORS.

With the rapid development of Internet of Things (IoT), mobile crowdsensing (MCS), i.e., outsourcing sensing tasks to mobile devices or vehicles, has been proposed to address the problem of data collection in the scenarios such as smart city. Despite its benefits for a wide range of applications, MCS lacks an efficient incentive mechanism, restricting the development of IoT applications, especially for Internet of Vehicles (IoV)-a typical example of IoT applications; this is because vehicles are usually reluctant to participate these sensing tasks. Moreover, in practice, some sensing tasks may arrive suddenly (called an emergent task) in the IoV environment, but the resources of a single vehicle may be insufficient to handle, and thus multivehicles collaboration is required. In this case, the incentive mechanisms for the participation of multiple vehicles and the task scheduling for their collaborations are collectively needed. To address this important problem, we first propose a new model for the scenario of two vehicles collaboration, considering the situation of the emergent appearance of a task. In this model, for a general sensing task, we propose a bidding mechanism to better encourage vehicles to contribute their resources, and the tasks for those vehicles are scheduled accordingly. Second, for an emergent task, a novel time-window-based method is devised to manage the tasks among vehicles and to incent the vehicles to participate. Finally, we develop a blockchain framework to achieve the secured information exchange through smart contract for the proposed models in IoV.

With the explosive network data due to the advanced development of the Internet of Things (IoT), the demand for multiparty computation is increasing. In addition, with the advent of future digital society, data have been gradually evolving into an effective virtual asset for sharing and usage. With the nature of the sensitivity, massiveness, fragmentation, and security of multiparty data computation in the IoT environment, we propose a secure data collaboration framework (FDC) based on federated deep-learning technology. The proposed framework can realize the secure collaboration of multiparty data computation on the premise that the data do not need to be transmitted out of their private data center. This framework is empowered by public data center, private data center, and the blockchain technology. The private data center is responsible for data governance, data registration, and data management. The public data center is used for multiparty secure computation. The blockchain paradigm is responsible for ensuring secure data usage and transmissions. A real IoT scenario is used to validate the effectiveness of the proposed framework.

Network slicing, as a key 5G enabling technology, is promising to support with more flexibility, agility, and intelligence towards the provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and large-dimensioned. This contradicts the dominant network slicing solutions that only customize immediate performance over one snapshot of the system in the literature. Instead, this paper first presents a two-stage slicing optimization model with time-averaged metrics to safeguard the network slicing in the dynamical networks, where prior environmental knowledge is absent but can be partially observed at runtime. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. Therefore, we propose a learning augmented optimization approach with deep learning and Lyapunov stability theories. This enables the system to learn a safe slicing solution from both historical records and run-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, we demonstrate up to 2.6× improvement in the simulation when compared with three state-of-the-art algorithms.

Virtual network embedding arranges virtual network services onto substrate network components. The performance of embedding algorithms determines the effectiveness and efficiency of a virtualized network, making it a critical part of the network virtualization technology. To achieve better performance, the algorithm needs to automatically detect the network status which is complicated and changes in a time-varying manner, and to dynamically provide solutions that can best fit the current network status. However, most existing algorithms fail to provide automatic embedding solutions in an acceptable running time. In this paper, we combine deep reinforcement learning with a novel neural network structure based on graph convolutional networks, and propose a new and efficient algorithm for automatic virtual network embedding. In addition, a parallel reinforcement learning framework is used in training along with a newly-designed multi-objective reward function, which has proven beneficial to the proposed algorithm for automatic embedding of virtual networks. Extensive simulation results under different scenarios show that our algorithm achieves best performance on most metrics compared with the existing state-of-the-art solutions, with upto 39.6% and 70.6% improvement on acceptance ratio and average revenue, respectively. Moreover, the results also demonstrate that the proposed solution possesses good robustness.

The dramatic spread of online social network services, such as Facebook, Twitter, Instagram, and Google+, has led to increasing awareness of the power of incorporating social elements into a variety of data-centric applications. These applications, in recent years, apply various sensors with social media platforms to continuously collect massive data that can be directly associated with human interactions. This phenomenon has led to the creation of numerous social sensing systems, such as Biketastic, BikeNet, CarTel, and Pier, which use social sensors (i.e., users) for a variety of social sensing systems and applications. Social sensing has become an emerging and promising sensing paradigm that relies on the voluntary cooperation of users equipped with embedded or integrated sensors.

The four papers in this special section focus on the use of blockchain in the healthcare field. With the development of society, health has received increasing attentions. The development of science and technology has also promoted the protection of health. In recent years, the rapid development of computing and networking technologies has improved the ability to collect, measure, and analyze health-related data, and thus tremendous opportunities have opened up for healthcare computing. Meanwhile, these technologies have also brought new challenges and issues.

With increasing demands for more flexible services, the routing policies in enterprise networks become much richer. This has placed a heavy burden to the current router forwarding plane in support of the increasing number of policies, primarily due to the limited capacity in TCAM, which further hinders the development of new network services and applications. The scalable forwarding table structures for enterprise networks have therefore attracted numerous attentions from both academia and industry. To tackle this challenge, in this paper we present the design and implementation of a new forwarding table structure. It separates the functions of TCAM and SRAM, and maximally utilizes the large and flexible SRAM. A set of schemes are progressively designed, to compress storage of forwarding rules, and maintain correctness and achieve line-card speeds of packet forwarding. We further design an incremental update algorithm that allows less access to memory. The proposed scheme is validated and evaluated through a realistic implementation on a commercial router using real datasets. Our proposal can be easily implemented in the existing devices. The evaluation results show that the performance of forwarding tables under the proposed scheme is promising.

The ten papers in this special section are devoted to the most recent developments and research outcomes addressing the related theoretical and practical aspects of computational intelligence solutions in sustainable computing and aims at presenting latest innovative ideas targeted at the corresponding key challenges, either from a methodological or from an application perspective.

Flow watermarking is a promising active traffic tracking technology. It helps to establish the correspondence between the sender and the receiver, by embedding watermarks into packets with certain features of active interference traffic. Existing watermarking technologies have several drawbacks, such as the vulnerability to multi-flow attacks, low robustness and invisibility. This paper proposes a new traffic tracking technique based on hidden Markov model, called Hidden Markov State-based Flow Watermarking (HMSFW). HMSFW divides the observation range, i.e., inter-packet time, as Markov states and uses the State Transition Probability Mean (STPM) as the watermark carrier. It then adjusts the STPM of a given time interval according to historical traffic characteristics. The proposed HMSFW not only improves the robustness of embedded watermarks, but also effectively enhances their invisibility. The feasibility and invisibility of HMSFW are validated via extensive experimental results.

The primary focus of Next Generation Networks (NGN) is towards improving the performance by reducing latency in the network, increasing peak throughput and improving spectral efficiencies. Even though Fifth Generation (5G) network set its standard to lower the latency, the existing TCP/IP protocol suite imparts significant overhead to Next Generation Transport Layer. For example, the web contents are hosted across multiple content servers redundantly, which can be accessed using different network interfaces available in the client device. These mirror servers connected with different network interfaces will have different network path quality. The network path quality for any server changes based on user mobility, type of network interface (Wi-Fi/Cellular) and congestion in the path. In general, this network path quality impacts the RTT of the network which affects the content downloading time for all applications. At any situation, the client application does not know the best network path available at the moment. Hence, we propose a novel solution called Redundant TCP Connector (RTC) which establishes simultaneous connection using multiple network interfaces available and dynamically connects to the best estimated network path at any moment. RTC is a lightweight client only software solution which is prototyped in Samsung flagship models with Android O. RTC significantly improves content downloading time by 10% to 15% consistently.

Recent advances in traffic engineering offer a series of techniques to address the network problems due to the explosive growth of Internet traffic. In traffic engineering, dynamic path planning is essential for prevalent applications, e.g., load balancing, traffic monitoring and firewall. Application-specific methods can indeed improve the network performance but can hardly be extended to general scenarios. Meanwhile, massive data generated in the current Internet has not been fully exploited, which may convey much valuable knowledge and information to facilitate traffic engineering. In this paper, we propose a learning-based network path planning method under forwarding constraints for finer-grained and effective traffic engineering. We form the path planning problem as the problem of inferring a sequence of nodes in a network path and adapt a sequence-to-sequence model to learn implicit forwarding paths based on empirical network traffic data. To boost the model performance, attention mechanism and beam search are adapted to capture the essential sequential features of the nodes in a path and guarantee the path connectivity. To validate the effectiveness of the derived model, we implement it in Mininet emulator environment and leverage the traffic data generated by both a real-world GEANT network topology and a grid network topology to train and evaluate the model. Experiment results exhibit a high testing accuracy and imply the superiority of our proposal.

Network function virtualization (NFV) has been considered as a promising technology for future Internet to increase the network flexibility, accelerate the service innovation, and reduce the Capital Expenditures and Operational Expenditures costs through migrating network functions from dedicated network devices to commodity hardware. Recent studies reveal that although this migration of network function brings the network operation unprecedented flexibility and controllability, NFV-based architecture suffers from serious performance degradation compared with traditional service provisioning on dedicated devices. In order to achieve a comprehensive understanding of the service provisioning capability of NFV, this paper proposes a novel analytical model based on Stochastic Network Calculus (SNC) to quantitatively investigate the end-to-end performance bound of the NFV networks. To capture the dynamic and on-demand NFV features, both the non-bursty traffic, e.g., the Poisson process, and the bursty traffic, e.g., the Markov Modulated Poisson Process, are jointly considered in the developed model to characterize the arriving traffic. To address the challenges of resource competition and end-to-end NFV chaining, the property of convolution associativity and leftover service technologies of SNC are exploited to calculate the available resources of the Virtual Network Function nodes in the presence of multiple competing traffic and transfer the complex NFV chain into an equivalent system for performance derivation and analysis. Both the numerical analysis and extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model. Results demonstrate that the analytical performance metrics match well with those obtained from the simulation experiments and numerical analysis. In addition, the developed model is used as a practical and cost-effective tool to investigate the strategies of the service chain design and resource allocations in the NFV networks.

Assisting traffic control is one of the most important applications on the Internet of Vehicles (IoVs). Traffic information provided by vehicles is desired since drivers or vehicle sensors are sensitive in perceiving or detecting nuances on roads. However, the availability and privacy preservation of this information are critical while conflicted with each other in the vehicular communication. In this paper, we propose a semicentralized mode with attribute-based blockchain in IoVs to balance the tradeoff between the availability and the privacy preservation. In this mode, a method of control-by-vehicles is used to control signals of traffic lights to increase traffic efficiency. Users are grouped their attributes such as locations and directions before starting the communication. The users reach an agreement on determining a temporary signal timing by interacting with each other without leaking privacy. Final decisions are verifiable to all users, even if they have no a priori agreement and processes of consensus. The mode not only achieves the aim of privacy preservation but also supports responsibility investigation for historical agreements via ciphertext-policy attribute-based encryption (CP-ABE) and blockchain technology. Extensive experimental results demonstrated that our mode is efficient and practical.

Depression is becoming a serious global health problem worldwide, with an increasing number of patients suffering from anxiety and other disorders. Our work aims to provide the appropriate social support (SS) to the prevention of depression for highly anxious undergraduates. We used 1425 undergraduates from 18 universities in China via a cluster random sampling method for the survey on the self-rating anxiety scale, the self-rating depression scale, and the SS scale for anxiety and depression. Based on the collected questionnaire data, we first reveal that the distribution of both anxiety data and depression data follows a Gaussian distribution. Then, a Gaussian mixture model is adopted for clustering these data in terms of anxiety index and depression index. According to the observations extracted from the clusters, the correlation among anxiety, depression, and SS is investigated by a correlation analysis method. Finally, the corresponding moderating effect of SS between anxiety and depression is figured out via the hierarchical multiple regression analysis. The detailed analysis indicates that the high-level SS, such as the help and support from individual's friends or family members, could reduce the risk for depression from highly anxious undergraduates.

Webpage segmentation is the basic building block for a wide range of webpage analysis methods. The rapid development of Web technologies results in more dynamic and complex webpages, which bring new challenges to this area. To improve the performance of webpage segmentation, we propose a two-stage segmentation method that can combine visual, logic, and semantic features of the contents on a webpage. Specifically, we devise a new model to measure the similarities of the elements on webpages based on both visual layout and logic organization in the first stage, and we propose a novel block regrouping method using semantic statistics and visual positions in the second stage. This two-stage method can effectively conduct webpage segmentation on complicated and dynamic webpages. The performance and accuracy of the method are verified by comparing with two existing webpage segmentation methods. The experiment results show that the proposed method significantly outperforms the existing state of the art in terms of higher precision, recall, and accuracy.

This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under specific scenarios, and 3) keeps evolving with the growth of anomaly detection experience. Essentially, the anomaly detector is powered by the Recurrent Neural Network (RNN) and adopts the Reinforcement Learning (RL) method to achieve the self-learning process. Our initial experiments demonstrate promising results of using the detector in network time series anomaly detection problems.

Convex hull analysis is a key research tool under the broad umbrella of machine learning and finds applications in various domains. However, due to the fact that traditional convex hull analysis usually targets low-dimensional space and just roughly estimates the shape of a dataset, its capability in describing general datasets is greatly limited. In this paper, we investigate the problem of convex hull approximation in high-dimensional space and propose to approximate the convex hull through Semi-Nonnegative Matrix Factorization (Semi-NMF). The novel problem formulation enables the utilization of the kernel trick and makes convex hull analysis readily applicable to general data description tasks, such as one-class classification and clustering. The empirical experiments show that our method successfully describes the convex hull with the approximated extreme points and achieves competitive results in both one-class classification and clustering tasks.

The recent advances in microelectronics and communications have led to the development of large-scale Internet of Things (IoT) networks, where tremendous sensory data is generated and needs to be processed. To support realtime processing for large-scale IoT, deploying edge servers with storage and computational capability is a promising approach. In this paper, we carefully analyze the impacting factors and key challenges for edge node (EN) deployment. We then propose a novel three-phase deployment approach which considers both traffic diversity and the wireless diversity of IoT. The proposed work aims at providing real-time processing service for the IoT network and reducing the required number of ENs. We conducted extensive simulation experiments, the results show that compared to the existing works that overlooked the two kinds of diversities, the proposed work greatly reduces the number of ENs and improves the throughput between IoT and ENs.

As a key enabling technology for 5G network softwarization, network function virtualization (NFV) provides an efficient paradigm to optimize network resource utility for the benefits of both network providers and users. However, the inherent network dynamics and uncertainties from 5G infrastructure, resources, and applications are slowing down the further adoption of NFV in many emerging networking applications. Motivated by this, in this paper, we investigate the issues of network utility degradation when implementing NFV in dynamic networks, and design a proactive NFV solution from a fully stochastic perspective. Unlike existing deterministic NFV solutions, which assume given network capacities and/or static service quality demands, this paper explicitly integrates the knowledge of influential network variations into a two-stage stochastic resource utilization model. By exploiting the hierarchical decision structures in this problem, a distributed computing framework with two-level decomposition is designed to facilitate a distributed implementation of the proposed model in large-scale networks. The experimental results demonstrate that the proposed solution not only improves 3~5 folds of network performance, but also effectively reduces the risk of service quality violation.

Geographic routing has been considered as an attractive approach for resource-constrained wireless sensor networks (WSNs) since it exploits local location information instead of global topology information to route data. However, this routing approach often suffers from the routing hole (i.e., an area free of nodes in the direction closer to destination) in various environments such as buildings and obstacles during data delivery, resulting in route failure. Currently, existing geographic routing protocols tend to walk along only one side of the routing holes to recover the route, thus achieving suboptimal network performance such as longer delivery delay and lower delivery ratio. Furthermore, these protocols cannot guarantee that all packets are delivered in an energy-efficient manner once encountering routing holes. In this paper, we focus on addressing these issues and propose an energy-aware dual-path geographic routing (EDGR) protocol for better route recovery from routing holes. EDGR adaptively utilizes the location information, residual energy, and the characteristics of energy consumption to make routing decisions, and dynamically exploits two node-disjoint anchor lists, passing through two sides of the routing holes, to shift routing path for load balance. Moreover, we extend EDGR into threedimensional (3D) sensor networks to provide energy-aware routing for routing hole detour. Simulation results demonstrate that EDGR exhibits higher energy efficiency, and has moderate performance improvements on network lifetime, packet delivery ratio, and delivery delay, compared to other geographic routing protocols in WSNs over a variety of communication scenarios passing through routing holes. The proposed EDGR is much applicable to resource-constrained WSNs with routing holes.

One grand challenge in software defined networking is to select appropriate locations for controllers to shorten the latency between controllers and switches in wide area networks. In the literature, the majority of approaches are focused on the reduction of packet propagation latency, but propagation latency is only one of the contributors of the overall latency between controllers and their associated switches. In this paper, we explore and investigate more possible contributors of the latency, including the end-to-end latency and the queuing latency of controllers. In order to decrease the end-to-end latency, the concept of network partition is introduced and a clustering-based network partition algorithm (CNPA) is then proposed to partition the network. The CNPA can guarantee that each partition is able to shorten the maximum end-to-end latency between controllers and switches. To further decrease the queuing latency of controllers, appropriate multiple controllers are then placed in the subnetworks. Extensive simulations are conducted under two real network topologies from the Internet Topology Zoo. The results verify that the proposed algorithm can remarkably reduce the maximum latency between controllers and their associated switches.

With high variability and correlation in arrival rates and packet sizes, multimedia traffic places a great strain on Wireless Local Area Networks (WLANs) towards provision of satisfactory Quality- of-Service (QoS). Most existing performance models for wireless networks are restricted to unrealistic assumptions where specific traffic characteristics of burstiness, correlation and self-similarity are ignored. This paper proposes an original analytical model as a cost-effective tool to evaluate the performance of WLANs in the presence of heterogeneous multimedia traffic, capturing the burstiness, correlation and self- similarity characteristics using Batch Markovian Arrival Process (BMAP). The model derives important QoS metrics in terms of throughput, end- to-end delay and frame loss probability. Analytical results validated through extensive simulations reveal the degrading effect of burstiness, correlation and heterogeneity of traffic sources on the QoS performance of WLANs.

Wireless Rechargeable Sensor Networks (WRSNs) have attracted increasing research attention in recent years. In WRSNs, a mobile charger is often used to charge the network nodes. To reduce the charging delay, the existing works often formulate the problem as a Traveling Salesman Problem (TSP), where the charger is required to visit the specific positions of the network nodes due to the limited charging range. However, we argue that as the charging technology develops, the charging range is becoming increasingly larger. The wireless charging ability is under-utilized using the TSP model. Some other works try to exploit the wireless charging ability by clustering the charging spots, however, the moving path of the charger is not carefully considered. In this paper, we first analyze the optimization opportunity and then propose a novel charging strategy by modeling the problem as TSP with neighborhood (TSPN). The model is able to exploit the wireless charging ability and reduce the moving delay of the charger at the same time. We also propose an adaptive merging process to reduce the stops of the charger for further optimization. We conduct simulation experiments to study the performance of our proposed scheme, the results show that compared to the state-of-the-art works, the proposed work greatly reduce the charging delay and the moving delay of the charger.

Summary Although IPv6 has been standardized more than 15 years ago, its deployment is still very limited. China has been strongly pushing IPv6, especially due to its limited IPv4 address space. In this paper, we describe measurements from a large Chinese academic network, serving a significant population of IPv6 hosts. We show that despite its expected strength, China is struggling as much as the western world to increase the share of IPv6 traffic. To understand the reasons behind this, we examine the IPv6 applicative ecosystem. We observe a significant IPv6 traffic growth over the past 3 years, with P2P file transfers responsible for more than 80% of the IPv6 traffic, compared with only 15% for IPv4 traffic. Checking the top websites for IPv6 explains the dominance of P2P, with popular P2P trackers appearing systematically among the top visited sites, followed by Chinese popular services (e.g., Tencent), as well as surprisingly popular third‐party analytics including Google. Finally, we compare the throughput of IPv6 and IPv4 flows. We find that a larger share of IPv4 flows get a high‐throughput compared with IPv6 flows, despite IPv6 traffic not being rate limited. We explain this through the limited amount of HTTP traffic in IPv6 and the presence of Web caches in IPv4. Our findings highlight the main issue in IPv6 adoption, that is, the lack of commercial content, which biases the geographic pattern and flow throughput of IPv6 traffic. Copyright © 2014 John Wiley & Sons, Ltd.

Software-Defined Networking (SDN) is an emerging architecture for the next-generation Internet, providing unprecedented network programmability to handle the explosive growth of big data driven by the popularisation of smart mobile devices and the pervasiveness of content-rich multimedia applications. In order to quantitatively investigate the performance characteristics of SDN networks, several research efforts from both simulation experiments and analytical modelling have been reported in the current literature. Among those studies, analytical modelling has demonstrated its superiority in terms of cost-effectiveness in the evaluation of large-scale networks. However, for analytical tractability and simplification, existing analytical models are derived based on the unrealistic assumptions that the network traffic follows the Poisson process, which is suitable to model nonbursty text data, and the data plane of SDN is modelled by one simplified Single-Server Single-Queue (SSSQ) system. Recent measurement studies have shown that, due to the features of heavy volume and high velocity, the multimedia big data generated by real-world multimedia applications reveals the bursty and correlated nature in the network transmission. With the aim of capturing such features of realistic traffic patterns and obtaining a comprehensive and deeper understanding of the performance behaviour of SDN networks, this article presents a new analytical model to investigate the performance of SDN in the presence of the bursty and correlated arrivals modelled by the Markov Modulated Poisson Process (MMPP). The Quality-of-Service performance metrics in terms of the average latency and average network throughput of the SDN networks are derived based on the developed analytical model. To consider a realistic multiqueue system of forwarding elements, a Priority-Queue (PQ) system is adopted to model the SDN data plane. To address the challenging problem of obtaining the key performance metrics, for example, queue-length distribution of a PQ system with a given service capacity, a versatile methodology extending the Empty Buffer Approximation (EBA) method is proposed to facilitate the decomposition of such a PQ system to two SSSQ systems. The validity of the proposed model is demonstrated through extensive simulation experiments. To illustrate its application, the developed model is then utilised to study the strategy of the network configuration and resource allocation in SDN networks.

Summary OpenFlow has become the key standard and technology for software defined networking, which has been widely adopted in various environments. However, the global deployment of OpenFlow encountered several issues, such as the increasing number of fields and complex structure of flow entries, making the size of flow table in OpenFlow switches explosively grows, which results in hardware implementation difficulty. To this end, this paper presents the modelling on the minimisation for storage space of flow table and proposes a Heuristic Storage space Optimisation algorithm for Flow Table (H‐SOFT) to solve this optimisation problem. The H‐SOFT algorithm degrades the complex and high‐dimensional fields of a flow table into multiple flow tables with simple and low‐dimensional fields based on the coexistence and conflict relationships among fields to release the unused storage space due to blank fields. Extensive simulation experiments demonstrate that the H‐SOFT algorithm can effectively reduce the storage space of flow table. In particular, with frequent updates on flow entries, the storage space compression rate of flow table is stable and can achieve at ~70%. Moreover, in comparison with the optimal solution, the H‐SOFT algorithm can achieve the similar compression rate with much lower execution time. Copyright © 2014 John Wiley & Sons, Ltd.

Advances in

The traffic pattern has significant impact on the performance of network-on-chip. Many recent studies have shown that multimedia applications can be supported in on-chip interconnects. Driven by the motivation of evaluating on-chip interconnects in multimedia embedded systems, a new analytical model is proposed to investigate the performance of the fat-tree based on-chip interconnection network under bursty multimedia traffic and nonuniform message destinations. Extensive simulation experiments are conducted to validate the accuracy of the model, which is then adopted as a cost-efficient tool to investigate the effects of bursty multimedia traffic with nonuniform destinations on the network performance.

Mobile IPv6 suffers various limitations, e.g., lack of business model and management of enormous and discrete home agents, preventing it from being deployed in large-scale commercial environments. Recently, the identification/locator (ID/Locator) split architecture has demonstrated its significant predominance in next generation mobile networks. With the aim of pushing the global deployment of mobility support over IPv6, this study makes an effort to design and evaluate an operational mobility model over IPv6 (OMIPv6) based on ID/Locator split architecture. Instead of the home agents adopted in the standard MIPv6, a cloud mobility management center is employed to be responsible for maintaining the identification and locations of mobile hosts, as well as providing the name resolution services to the mobile hosts. Moreover, this paper develops an analytical model considering all possible costs required for the operation of OMIPv6.

A new analytical model is proposed to investigate the delay and throughput in cognitive mesh networks. The validity of the model is demonstrated via extensive simulation experiments. The model is then used to evaluate the effects of the number of licensed channels and channel utilisation on the network performance.

Multicluster systems have emerged as a promising infrastructure for provisioning of cost-effective high-performance computing and communications. Analytical models of communication networks in cluster systems have been widely reported. However, for tractability and simplicity, the existing models are based on the assumptions that the network traffic follows the nonbursty Poisson arrival process and the message destinations are uniformly distributed. Recent measurement studies have shown that the traffic generated by real-world applications reveals the bursty nature in both the spatial domain (i.e., nonuniform distribution of message destinations) and temporal domain (i.e., bursty message arrival process). In order to obtain a comprehensive understanding of the system performance, a novel analytical model is developed for communication networks in multicluster systems in the presence of the spatio-temporal bursty traffic. The spatial traffic burstiness is captured by the communication locality and the temporal traffic burstiness is modeled by the Markov-modulated Poisson process. After validating its accuracy through extensive simulation experiments, the model is used to investigate the impact of bursty message arrivals and communication locality on network performance. The analytical results demonstrate that the communication locality can relieve the degrading effects of bursty message arrivals on the network performance.

The cognitive radio (CR) is an emerging technique for increasing the utilisation of communication resources by allowing the unlicensed users to employ the under-utilised spectrum. In this paper, a new analytical performance model is developed to evaluate the QoS of multi-hop CR networks. After validating its accuracy through extensive simulation experiments, the analytical model is adopted as a cost-effective tool to investigate the effects of the primary users' activities on the network performance. Moreover, the model can be used to study the strategy of employing under-utilised spectra so as to maximise the overall resource utilisation and network performance.

The k-ary n-cube which has an n-dimensional grid structure with k nodes in each dimension has been a popular topology for interconnection networks. Analytical models for k-ary n-cubes have been widely reported under the assumptions that the message destinations are uniformly distributed over all network nodes and the message arrivals follow a non-bursty Poisson process. Recent studies have convincingly demonstrated that the traffic pattern in interconnection networks reveals the bursty nature in the both spatial domain (i.e., non-uniform distribution of message destinations) and temporal domain (i.e., bursty message arrival process). With the aim of capturing the characteristics of the realistic traffic pattern and obtaining a comprehensive understanding of the performance behaviour of interconnection networks, this paper presents a new analytical model for k-ary n-cubes in the presence of spatio-temporal bursty traffic. The accuracy of the model is validated through extensive simulation experiments of an actual system.

The increasing demand for the coverage of high-speed wireless local area networks (WLANs) is driving the installation of a very large number of access points. Wireless mesh networks (WMNs) have emerged as a promising technology in next generation networks to provide economical and scalable broadband access to wireless interconnection of multiple access points which manage individual WLANs in order to extend the coverage of conventional single WLANs. Due to various types of applications with particular purposes running at different WLANs, the traffic generated by stations in different WLANs possess a high degree of heterogeneity. To the best of our knowledge, there is hardly any analytical model reported in the current literature to handle heterogeneous network traffic in the integrated WMNs and WLANs. To fill this gap, we develop a new analytical model to investigate the Quality-of-Service (QoS) performance metrics in WMNs interconnecting multiple WLANs with heterogeneous stations. The Poisson process is employed to model the traffic of non-bursty data applications and the Markov modulated Poisson process (MMPP) is used to model the traffic of bursty multimedia applications. Extensive simulation experiments are conducted to validate the accuracy of the analytical model.

Cluster-based systems have emerged as a promising technology for providing cost-effectiveness in high-performance computing and communication systems. Performance studies on communication networks in cluster-based systems have been reported based on the simplified assumptions that the traffic follows the non-bursty Poisson process and the message destinations are uniformly distributed over all network nodes. However, the uniform distribution of message destinations is not always realistic in practice. Moreover, the communication locality, a typical example of the non-uniform destination distribution, has been shown to be an important phenomenon in the communication networks of cluster systems. Many recent measurement studies have revealed that the traffic generated by many real-world applications exhibits a high degree of burstiness. In order to have a comprehensive understanding of the system performance, this paper proposes a new analytical model for communication networks in multi-cluster systems under the bursty message arrivals with communication locality. The model is validated through extensive simulation experiments.

Wireless mesh networks (WMNs) are emerging as a key technology for the next-generation wireless networks owing to its attractive properties, such as dynamic self-organisation, quick deployment, easy maintenance, low cost, and high scalability. In this paper, we develop an analytical model to investigate the end-to-end delay in a random-access two-tier (i.e., the backhaul tier and access tier) WMN. In the backhaul tier, mesh routers are uniformly distributed in a grid placement. The access tier is formed by a series of wireless local area networks. After validating the accuracy of the analytical model through simulation experiments, we use the model to tune the system parameters in order to minimise the end-to-end delay.

Wireless mesh networks (WMNs) have been proposed to provide rapid deployment and easy reconfiguration of wireless broadband communications. WMNs can interoperate with WiMAX, Wi-Fi, sensor, or cellular networks in the hybrid working environments to relay packets robustly among these heterogeneous networks and significantly extend the coverage of individual wireless access networks. Many recent studies have shown that the packet arrival process in wireless networks exhibits the batch arrival nature and the communication locality has an important impact on the network capacity. With the aim of obtaining an effective performance evaluation tool of wireless networks, this paper proposes an analytical model for heterogeneous wireless networks integrated by WMNs in the presence of batch arrival traffic with communication locality. The validity of the analytical model is demonstrated through extensive comparison between analytical and simulation results.

Pipelined circuit switching which combines the advantages of both circuit switching and wormhole switching is an efficient method for reconciling the conflicting demands of communication performance and fault-tolerance in interconnection networks of multi-computers. The arrival process and destination distribution of messages generated by real-world parallel applications can markedly affect the performance of communication networks. This paper presents an analytical performance model to calculate the message latency in hypercube interconnection networks with pipelined circuit switching in the presence of bursty and correlated traffic coupled with hot-spot message destinations. The accuracy of the analytical model is validated through extensive simulation experiments.

Traffic loads have a significant impact on the performance of routing algorithms. Many analytical models for adaptive routing in interconnection networks have been reported. However, most existing studies are based on the assumption that the arrivals of traffic follow a non-bursty Poisson process and the message destinations are uniformly distributed over the network. With the aim of obtaining a deep understanding of network performance under more realistic working conditions, this study develops an analytical performance model for adaptive-routed hypercubic networks under hot-spot and batch arrival traffic. This model adopts the Compound Poisson Process (CPP) to capture the properties of the batch arrival traffic. Extensive simulation experiments are conducted to validate the accuracy of the analytical model.

In this paper, we propose a flow-level simulator called FSIM (Fluid-based SIMulator) for performance evaluation of large-scale networks, and verify its effectiveness using our FSIM implementation. The notable features of our flow-level simulator FSIM are its accuracy and fast simulation execution compared with conventional flow-level simulators. For improving simulation accuracy, our flow-level simulator FSIM utilizes accurate fluid-flow models. For accelerating simulation execution speed, our flow-level simulator FSIM adopts an adaptive numerical computation algorithm for ordinary differential equations. Another notable feature of our flow-level simulator FSIM is its compatibility with the existing network performance analysis tool. In this paper, through several experiments using our FSIM implementation, we evaluate the effectiveness of our flow-level simulator FSIM in terms of simulation speed, accuracy and memory consumption. Consequently, we show that our flow-level simulator FSIM outperforms a conventional flow-level simulator; i.e., it realizes approximately 100% faster simulation with higher accuracy and less memory consumption than a conventional flow-level simulator.