Spatio-temporal estimation plays a vital role in numerous scientific and engineering tasks, particularly for novel or unobserved locations lacking historical references. Many areas remain unobserved by sensors due to their non-core location or pending development status. The states of these areas can only be estimated through similar nodes in the geospace, rather than through historical data with temporal trends. Estimating these unobserved node states is crucial for city-wide spatio-temporal sensing and urban development, extending beyond simple point or block data imputation. In this study, we introduce a diffusion point process in high-frequency space to develop a robust spatio-temporal diffusion transformer for urban estimation where partial historical reference data is lacking. Our approach decomposes spatio-temporal data into high and low-frequency components through wavelet transform, and trains a diffusion model of spatial temporal data with a transformer that operates on high frequency signals. We incorporate low-frequency signals as diffusion conditions in the transformer architecture to capture overall spatio-temporal profiles and gradual trends. To enhance the learning of each step, we design an diffusion model featuring a spatio-temporal attention module that adaptively captures interdependencies between time and space. Extensive experiments across diverse domains including traffic, economics, and environment demonstrate that our method significantly outperforms state-of-the-art baselines.

The advancement of large language models (LLMs) has enhanced tabular question answering (Tabular QA), yet they struggle with opendomain queries exhibiting underspecified or uncertain expressions.To address this, we introduce the ODUTQA-MDC task and the first comprehensive benchmark to tackle it.This benchmark includes: (1) a large-scale ODUTQA dataset with 209 tables and 25,105 QA pairs; (2) a fine-grained labeling scheme for detailed evaluation; and (3) a dynamic clarification interface that simulates user feedback for interactive assessment.We also propose MAIC-TQA, a multi-agent framework that excels at detecting ambiguities, clarifying them through dialogue, and refining answers.Experiments validate our benchmark and framework, establishing them as a key resource for advancing conversational, underspecification-aware Tabular QA research.The data and code are available at https://github.com/jensenw1/ ODUTQA-MDC.Q3: What is the greenery rate of the Vanke Light of the Future community?Q1: What is the greenery rate of the Vanke Light of the Future community in Bao'an District, Shenzhen?Q2: How is the environment of the Vanke Light of the Future community in Bao'an District, Shenzhen?Q4: What is the greenery rate of the Vanke community in Bao'an District, Shenzhen?

Developing agents capable of navigating fragmented, multi-source information remains challenging, primarily due to the scarcity of benchmarks reflecting hybrid workflows combining database querying with external APIs.To bridge this gap, we introduce ReCoQA, a largescale benchmark of 29,270 real-estate instances featuring machine-verifiable supervision for intermediate steps, including structured intent labels, SQL queries, and API calls.Complementarily, we propose HIRE-Agent, a hierarchical framework instantiating an understand-plan-execute architecture as a strong baseline.By orchestrating a Front-end parser, a planning Supervisor, and execution Specialists, HIRE-Agent effectively integrates heterogeneous evidence.Extensive experiments demonstrate that HIRE-Agent constitutes a strong baseline and substantiates the necessity of hierarchical collaboration for complex, real-world reasoning tasks.The benchmark and source code are available at: https://github.com/ Husky-989/ReCoQA

In multimedia applications, Federated Learning (FL) has emerged as an effective training paradigm, enabling distributed clients to collaboratively train a shared model without transmitting raw data. However, FL remains vulnerable to privacy threats such as data reconstruction and membership inference attacks, which has motivated the adoption of secure aggregation protocols to protect client model updates. Existing secure aggregation schemes that combine homomorphic encryption with secret sharing predominantly follow a sharing-decryption paradigm, requiring additional client interaction during decryption and thereby incurring substantial computational, synchronization, and communication overhead. In this paper, we propose Threshold Vector Aggregation (TVA), a novel secure aggregation protocol that adopts a sharing-encryption and aggregation paradigm. Under TVA, each client encrypts its local update only once using a unique private key and uploads a single ciphertext to the server, without any further interaction. The cipher-texts are directly aggregatable yet individually undecryptable, and only the final aggregated result can be correctly recovered by the server. Extensive experiments demonstrate that TVA significantly outperforms state-of-the-art secure aggregation schemes, achieving up to 33× lower server-side aggregation time, 132× faster client-side encryption, and 170× reduction in communication overhead, while preserving model accuracy comparable to plaintext federated learning.

Serverless computing, underpinned by an event-driven approach with transient stateless containers, significantly enhances resource efficiency and simplifies function development. To maintain an acceptable Quality of Service (QoS) agreed in the Service Level Agreement (SLA), service providers need to improve the response latency while considering resource efficiency. However, cold-start delays in container initialization often lead to considerable latency in these applications. Existing mitigation strategies, such as pre-warming and function caching, are inadequate due to workload skewness and oscillation across edge nodes. These limitations are particularly critical in resource-constrained edge environments. We must jointly consider multiple factors, such as node status, function resource requirement and function popularity. To overcome these limitations, this paper presents FaasOrc , a bi-level function orchestration framework to mitigate workload skewness and oscillation across edge nodes. FaasOrc uses a cluster-level scheduler to schedule requests and a node-level manager to detect popular functions. Our comprehensive evaluation, consisting of two parts: simulations and a real-system prototype over Knative , benchmarks the proposed solution against existing scheduling and caching strategies. The findings highlight our method’s capability to reduce the response latency by 31.4%.

Programmable switches have revolutionized networking by enabling a new spectrum of applications, such as network telemetry, in-network computation, and machine learning. These applications heavily utilize register memory but their performance is significantly constrained by the scarcity of on-chip resources, such as the 15 MB of SRAM available on a Tofino switch. To effectively accommodate increasingly memorydemanding applications, we aim to pool register resources across multiple switches, creating a larger unified register memory space. This resource pooling approach addresses the limitations of existing single-switch Virtual Register Memory (VRM) solutions, which cannot meet the demands of these applications in distributed environments. To achieve this, we propose dVRM, a distributed VRM deployment framework that enables crossswitch memory sharing and self-dynamic memory allocation on the data plane. dVRM introduces three innovations: (1) Grouped Multi-Switch Registers (GMRs), virtualizing distributed pipeline stages into a unified memory pool; (2) a self-adaptive, bit-width allocation mechanism driven by real-time data-plane feedback; and (3) lightweight heuristics for concurrent application deployment with distributed VRM, formulated as a mixed-integer linear programming (MILP) problem. We have implemented dVRM on both P4 hardware switches (with Intel Tofino ASIC) and BMv2. Experimental results show that dVRM significantly reduces hash unit consumption by up to 26% and achieves an improvement in accuracy (ARE) of up to 57.3% across various workloads.

In Mobile Crowdsensing (MCS), incentive mechanisms are crucial for encouraging mobile users to join tasks while users selfishly pursue personal benefit maximization. While most existing studies focus on the interaction between the requester and users, the internal value of socially aware user relationships remains underexplored. Users naturally form social connections, assisting or collaborating on tasks, but current mechanisms often neglect asymmetric social effects, which can lead to unequal willingness to cooperate and eventual breakdowns in collaboration (e.g., less profitable users refusing to cooperate). To end this, we propose an integrated incentive mechanism that models the interaction between the requester and users as a two-stage Stackelberg Game (SG) while accounting for pairwise asymmetric social effects. Pairwise cooperation is governed by the Iterated Prisoner’s Dilemma (IPD), with users employing Zero-Determinant (ZD) strategies to ensure cooperation despite unequal payoffs. Additionally, a plug-and-play sub-algorithm is introduced to filter low-quality or malicious users simultaneously and evaluate task redundancy, enhancing system robustness. We rigorously prove the existence of the Nash equilibrium, design an efficient iterative algorithm for our proposed mechanism, and validate its effectiveness through extensive experiments on real-world social datasets, which demonstrate that our method significantly improves system utility and cooperation stability while ensuring quality of service requirements.

Large language models (LLMs) have made notable advancements across various dimensions of human experience, while prompt engineering has further refined their capability and efficiency in generating relevant, high-quality outputs. Nonetheless, the rise of prompt engineering has also led to an increase in prompt attacks, resulting in critical issues such as privacy breaches, increased latency, and inefficient resource utilization. Existing security mechanisms based on Reinforcement Learning from Human Feedback often fall short in addressing the complexities posed by these diverse prompt attacks, emphasizing the pressing need for effective prompt security mechanisms. The prompt detection model serves as an effective supplementary measure to ensure the safety of large model outputs. However, the current detection mechanism checks all prompts, which increases system resource consumption and user latency. In this paper, we present a holistic analysis of prompt security, service latency, and resource optimization within Edge-Cloud LLM (EC-LLM) systems confronted with various prompt attack scenarios. To strengthen prompt security, we introduce a novel, lightweight attack detection mechanism that uses vector databases. We conceptualize the intricate interplay between prompt detection, latency, and resource optimization within a 2-stage dynamic Bayesian game framework. A reproducible equilibrium strategy is derived by estimating the potential number of malicious tasks and updating beliefs iteratively at each stage using Bayesian methods. Our proposed solution has been rigorously evaluated in a practical EC-LLM system, yielding results that highlight significant enhancements in security, reductions in service latency for legitimate users, and decreased overall resource consumption compared to current leading algorithms.

The rapid integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has led to the pro liferation of AIoT applications, significantly escalating demands for computing and communication resources in multi-user, multitask scenarios. A critical challenge lies in efficiently managing resource allocation to ensure Quality of Service (QoS) for diverse Deep Neural Network (DNN) inference tasks. Existing edge cloud collaborative inference approaches partially address this by hierarchical resource management; however, these methods often overlook the joint optimization of computation, communication, and data quality, and neglect long-term system stability in dynamic environments. To address these limitations, we propose EdgeManager, an online adaptive resource management frame work for hierarchical DNN inference in collaborative heterogeneous edge environments. Specifically, we formulate a Mixed Integer Nonlinear Programming (MINLP) optimization problem aimed at balancing inference accuracy and latency. Leveraging Lyapunov optimization, we transform the complex, multi-stage dynamic optimization problem into manageable deterministic sub-problems for each time slot, ensuring long-term stability. Furthermore, we introduce HyDRL-MO, a hybrid approach integrating model-free Deep Reinforcement Learning (DRL) and model-based multi-decision optimization techniques to achieve efficient and stable resource allocation. Extensive experimental evaluations demonstrate that EdgeManager significantly improves system performance, achieving up to 42.08% enhancement in average system benefits compared to state-of-the-art solutions.

Advancements in edge computing and container technology have made it increasingly popular and convenient to deploy Large Language Models (LLMs) through containers at the edge. However, the limited GPU resources of edge servers make it impractical to retain the model in GPU memory for long periods due to the high memory cost, especially when they remain idle without user requests. Existing work unloads the entire idle models to reduce memory costs on edge servers, but reloading them introduces significant loading delays that affect task Quality of Service (QoS). Therefore, efficient management of idle models is a critical issue that has been largely neglected in existing research and requires urgent attention. To address this gap, this paper studies the problem of idle model management from the perspective of the trade-off between memory cost and loading delay under the QoS constraint. A novel layer-granularity model unloading method is proposed, which leverages the layered characteristics of the model. We formulate an online joint optimization problem to determine which layers to unload and when, and present a layer-granularity unloading strategy inspired by the ski rental problem to solve it. We implement a real system with layer-granularity unloading for LLMs on NVIDIA GPUs and validate the effectiveness of the proposed method. Experimental results show it effectively trades off memory cost and loading delay, improving overall performance by up to 39.6%.

With the rapid growth of IoT devices and their diverse workloads, container-based microservices deployed at edge nodes have emerged as a lightweight, scalable solution. However, existing microservice scheduling algorithms often assume static resource availability, which is unrealistic when multiple containers are assigned to an edge node. Besides, containers suffer from cold-start inefficiencies during early-stage training in currently popular reinforcement learning (RL) algorithms. In this paper, we propose a hybrid learning framework that combines offline imitation learning (IL) with online Soft Actor-Critic (SAC) optimization to enable cold-start-aware microservice scheduling with dynamic resource allocation. We first formulate a delay-and-energy-aware scheduling problem and construct a rule-based expert to generate demonstration data for behavior cloning. Then, a GRU-enhanced policy network is designed within the policy network to extract correlations among multiple decisions by separately encoding slow-evolving node states and fast-changing microservice features, and an action selection mechanism is provided to speed up convergence. Extensive experiments show that our method significantly accelerates convergence and achieves superior final performance. Compared with baselines, our algorithm improves the total objective by 50% and convergence speed by 70%, and demonstrates the highest stability and robustness across various edge configurations.

Learning network representations, also known as network embeddings, has attracted significant attention in recent years. Real-world scenarios often involve networks with multiple views, where each view captures a distinct aspect of the network’s structure. Existing network embedding methods mainly focus on the global information from each view, neglecting the implied relations among multiple views. Additionally, maintaining the scalability of node embeddings while adapting to changes in network topology remains a major challenge. To this end, this article proposes a Cloud-edge Collaboration Network (CC-Net) to learn robust node embeddings in multi-view networks. Specifically, we design a decomposition and regrouping module to capture implied relations within multi-view networks, enabling the generation of comprehensive node representations that integrate information from all sub-networks. Besides, by leveraging the hybrid approach of cloud and edge computing, our proposed CC-Net can efficiently handle the complexities and dynamics of multi-view networks without retraining the entire network. Extensive experiments and analyses on real-world Twitter and YouTube datasets demonstrate the superiority of our approach compared to several benchmark methods, and validate its effectiveness in capturing implied relations and generating robust node embeddings.

Hierarchical Federated Learning (HFL) alleviates communication bottlenecks by organizing the system into multiple layers: client, intermediate aggregator, and server. Clients and aggregation layers form groups based on connection patterns, and the methods used for grouping and aggregation directly affect convergence performance. Currently, some studies have proposed grouping algorithms to address the non-independent and identically distributed (non-IID) characteristics of client data to improve performance. However, these methods do not account for network heterogeneity, such as clients using different quantization levels or adaptive quantization strategies to minimize communication overhead. Moreover, most methods rely on heuristics that blindly explore the combinatorial grouping space, incurring substantial computational overhead. In this paper, we conduct a rigorous convergence analysis and frame the dual challenges of heterogeneous data and quantization heterogeneity in HFL as the joint optimization of aggregation weights and grouping strategy. Specifically, we derive the optimal closed-form solution for the aggregation weights and propose an Alternating Optimization Hierarchical Optimal Weights (AO-HOW) algorithm to compute these weights. Building on this result, we propose DyGHFL—Dynamic Exclusion–Reallocation Grouping for Hierarchical Federated Learning, a structu-reaware and efficient greedy algorithm that reorganizes groups by maximizing structured gain and updating weight coefficients according to current system conditions. Experiments on multiple datasets show that DyGHFL consistently outperforms existing baselines, demonstrating its effectiveness in HFL.

Edge intelligence is rapidly evolving to support computation-intensive applications, yet scheduling containerized microservices in GPU-CPU heterogeneous edge environments remains challenging due to highly dynamic resource availability. Two critical bottlenecks hinder performance: the distinct hardware preferences of microservices, particularly between CPU and GPU architectures, which lead to significant performance gaps when placements are mismatched, and the non-trivial latency of container image downloads. To address these coupled challenges, we propose CHES, a Container-aware Heterogeneous Edge Scheduling framework. Unlike conventional heuristics that evaluate node resources and task sequences independently, our method leverages spatiotemporal learning to capture their joint impact on system performance. Specifically, we design a lightweight CNN to extract spatial correlations from heterogeneous node features, including compute capacity and bandwidth, and employ a GRU to model temporal dependencies among sequential microservice arrivals, where earlier scheduling decisions directly affect resource availability and image cache states for subsequent tasks. These representations are integrated into a Soft Actor-Critic (SAC) agent, enabling an adaptive policy that balances immediate execution latency with long-term image availability costs. Extensive experiments demonstrate that CHES significantly outperforms baselines by effectively aligning hardware-sensitive microservices with optimal nodes and mitigating cold-start overheads in dynamic workloads.

The inference services of Large Language Models (LLMs) play a crucial role in many fields. Deploying LLMs at the network edge can effectively reduce response latency and enhance domain-specific knowledge. However, due to the dynamic and unpredictable nature of edge user requests and the limited resources of edge servers, improper request scheduling can lead to server load imbalance and increased inference latency. To address this challenge, we model edge LLM inference under dynamic workloads and constrained edge resources by fully considering mutual influences and trade-offs between inference performance, inference latency, and edge resource utilization. We propose a Workload Prediction and Dynamic Request Scheduling (WPDS) algorithm for edge computing environments. The WPDS algorithm first evaluates and prioritizes heterogeneous requests by extracting features and importance scores from user requests. A Transformer-based encoder is used to extract load characteristics of edge servers over different time periods to predict future GPU resource demands. Then, a soft actor-critic reinforcement learning model dynamically schedules requests to appropriate LLM instances, capturing the dynamic relationship between request processing and edge resource utilization from complex state spaces. We evaluate our algorithm in a real Kubernetes-based edge inference prototype system. Experimental results indicate that our approach reduces average inference time by approximately 16.6% compared to the best-performing heuristic baseline algorithm, while also achieving more balanced GPU utilization across edge servers.

The real estate market relies heavily on structured data, such as property details, market trends, and price fluctuations. However, the lack of specialized Tabular Question Answering datasets in this domain limits the development of automated question-answering systems. To fill this gap, we introduce RETQA, the first large-scale open-domain Chinese Tabular Question Answering dataset for Real Estate. RETQA comprises 4,932 tables and 20,762 question-answer pairs across 16 sub-fields within three major domains: property information, real estate company finance information and land auction information. Compared with existing tabular question answering datasets, RETQA poses greater challenges due to three key factors: long-table structures, open-domain retrieval, and multi-domain queries. To tackle these challenges, we propose the SLUTQA framework, which integrates large language models with spoken language understanding tasks to enhance retrieval and answering accuracy. Extensive experiments demonstrate that SLUTQA significantly improves the performance of large language models on RETQA by in-context learning. RETQA and SLUTQA provide essential resources for advancing tabular question answering research in the real estate domain, addressing critical challenges in open-domain and long-table question-answering.

Time series forecasting is critical across various domains, such as weather, finance and real estate forecasting, as accurate forecasts support informed decision-making and risk mitigation. While recent deep learning models have improved predictive capabilities, they often overlook time-lagged cross-correlations between related sequences, which are crucial for capturing complex temporal relationships. To address this, we propose the Time-Lagged Cross-Correlations-based Sequence Prediction framework (TLCCSP), which enhances forecasting accuracy by effectively integrating time-lagged cross-correlated sequences. TLCCSP employs the Sequence Shifted Dynamic Time Warping (SSDTW) algorithm to capture lagged correlations and a contrastive learning-based encoder to efficiently approximate SSDTW distances. Experimental results on weather, finance and real estate time series datasets demonstrate the effectiveness of our framework. On the weather dataset, SSDTW reduces mean squared error (MSE) by 16.01% compared with single-sequence methods, while the contrastive learning encoder (CLE) further decreases MSE by 17.88%. On the stock dataset, SSDTW achieves a 9.95% MSE reduction, and CLE reduces it by 6.13%. For the real estate dataset, SSDTW and CLE reduce MSE by 21.29% and 8.62%, respectively. Additionally, the contrastive learning approach decreases SSDTW computational time by approximately 99%, ensuring scalability and real-time applicability across multiple time series forecasting tasks.

Existing full-reference image quality assessment (FR-IQA) methods often fail to capture the complex causal mechanisms that underlie human perceptual responses to image distortions, limiting their ability to generalize across diverse scenarios. In this paper, we propose an FR-IQA method based on abductive counterfactual inference to investigate the causal relationships between deep network features and perceptual distortions. First, we explore the causal effects of deep features on perception and integrate causal reasoning with feature comparison, constructing a model that effectively handles complex distortion types across different IQA scenarios. Second, the analysis of the perceptual causal correlations of our proposed method is independent of the backbone architecture and thus can be applied to a variety of deep networks. Through abductive counterfactual experiments, we validate the proposed causal relationships, confirming the model’s superior perceptual relevance and interpretability of quality scores. The experimental results demonstrate the robustness and effectiveness of the method, providing competitive quality predictions across multiple benchmarks. The source code is available at https://anonymous.4open.science/r/DeepCausalQuality-25BC.

The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets. This study explores the potential of large language models (LLMs) as automated data annotators to improve efficiency and consistency in annotation tasks. By employing rationale-driven collaborative few-shot prompting techniques, we aim to improve the performance of LLMs in text annotation. We conduct a rigorous evaluation of five LLMs across four benchmark datasets, comparing seven distinct methodologies. Our results demonstrate that collaborative methods consistently outperform traditional few-shot techniques and other baseline approaches, particularly in complex annotation tasks. Our work provides valuable insights and a robust framework for leveraging collaborative learning methods to tackle challenging text annotation tasks.

Large Language Models (LLMs) have demonstrated amazing capabilities in language generation, comprehension, and knowledge reasoning. However, relying on a single model can result in biased and unstable outcomes in many tasks. Multi-model collaboration has been introduced to enhance reasoning abilities on various tasks, but obtaining the correct answer from multiple candidate responses remains a challenge. To address this issue, we propose multi-model brainstorming based on prompt. It incorporates different models into a group for brainstorming, to reach a consensus answer after multiple rounds of reasoning elaboration and re-inference. Our experiments on diverse datasets demonstrate that the brainstorming can substantially improve the effectiveness in logical reasoning. Further, we observe that two small-parameter models can achieve accuracy comparable to a larger-parameter model through brainstorming, presenting a novel approach for the distributed deployment of LLMs.

Diversity in demonstration selection is critical for enhancing model generalization by enabling broader coverage of structures and concepts. Constructing appropriate demonstration sets remains a key research challenge. This paper introduces the Relevance-Diversity Enhanced Selection (RDES), an innovative approach that leverages reinforcement learning (RL) frameworks to optimize the selection of diverse reference demonstrations for tasks amenable to in-context learning (ICL), particularly text classification and reasoning, in few-shot prompting scenarios. RDES employs frameworks like Q-learning and a PPO-based variant to dynamically identify demonstrations that maximize both diversity (quantified by label distribution) and relevance to the task objective. This strategy ensures a balanced representation of reference data, leading to improved accuracy and generalization. Through extensive experiments on multiple benchmark datasets, including diverse reasoning tasks, and involving 14 closed-source and open-source LLMs, we demonstrate that RDES significantly enhances performance compared to ten established baselines. Our evaluation includes analysis of performance across varying numbers of demonstrations on selected datasets. Furthermore, we investigate incorporating Chain-of-Thought (CoT) reasoning, which further boosts predictive performance. The results highlight the potential of RL for adaptive demonstration selection and addressing challenges in ICL.

Effective modelling of uncertain information is crucial for quantifying uncertainty. Dempster–Shafer evidence (DSE) theory is a widely recognized approach for handling uncertain information. However, current methods often neglect the inherent a priori information within data during modelling, and imbalanced data lead to insufficient attention to key information in the model. To address these limitations, this paper presents a dynamic learning strategy based on nonuniform splitting mechanism and Hilbert space mapping. First, the framework uses a nonuniform splitting mechanism to dynamically adjust the weights of data subsets and combines the diffusion factor to effectively incorporate the data a priori information, thereby flexibly addressing uncertainty and conflict. Second, the conflict in the information fusion process is reduced by Hilbert space mapping. Experimental results on multiple tasks show that the proposed method significantly outperforms state-of-the-art methods and effectively improves the performance of classification and low-light image enhancement (LLIE) tasks. The code is available at https://anonymous.4open.science/r/Third-ED16.

The Internet of Vehicles offers a comprehensive perception of environment, which enhance transportation efficiency. To handle the large amount of collected data, distributed edge intelligence is a promising paradigm in which the edge server share data and computing resources with each other, providing low-latency services for local devices. However, offloading the computing-intensive application fully to one edge server might lead to a large latency as the computing resource of edge servers are usually limited. To solve this problem and elevate Quality of Service (QoS) to new heights, existing methodologies merely partition applications into modules, overlooking the crucial fact that these modules harbor distinct input requirements, posing a pivotal challenge in scheduling optimization. In this article, we study dependent task offloading by partitioning applications and dividing modules into two categories: 1) stateful modules and 2) statelss modules. The stateful modules necessitate the incorporation of previous calculation results, while stateless modules operate independently. We subsequently frame this intricate dependent task offloading challenge as an optimization problem, boldly acknowledging its NP-hard nature. Considering this, we unveil an innovative online collaborative dependent task offloading (OCDTO) algorithm, grounded in a two-layer collaborative edge computing architecture. This algorithm meticulously minimizes the make-span, redefining the benchmarks for efficiency. Our rigorous experimentation not only validates but also showcases the superiority of our approach, consistently achieving the lowest average system cost compared to the state-of-the-art, which verifies the effectiveness of our proposed approach in latency-sensitive and computing-intensive scenarios.

The integration of edge computing with deep neural networks (DNNs) is crucial for intelligent industrial cyber-physical systems. Typically, deploying DNNs on heterogeneous edge devices relies on methods like model compression and partitioning. However, these approaches often result in homogeneous models across devices. This homogeneity limits the collective capability of edge computing systems, particularly in terms of generalization to diverse data distributions and adaptation to dynamic industrial environments. In this work, we propose to treat each DNN on an edge device as an independent model, aggregating their capabilities via ensemble learning to enhance generalization and dynamic adaptability. To realize this, we introduce the Adaptive Ensemble Knowledge Distillation Framework (AEKDF), combining cloud-based model training with edge computing based collaborative inference. In the cloud, AEKDF develops an enhanced Born Again Network that generates diverse, lightweight models tailored to specific edge devices through knowledge distillation. This process ensures model diversity which is critical to effective ensemble learning. On the edge, AEKDF employs an adaptive ensemble technique that aggregates prediction logits across devices, enabling rapid adaptation to changing environments and maintaining inference efficiency. Our extensive evaluations conducted on a realistic prototype demonstrate the substantial boost in predictive performance achieved by our AEKDF, showcasing a 4% to 10% accuracy improvement on the CIFAR-100 compared to conventional single-model approaches, while maintaining low latency.

The new paradigm of In-network computing (INC) permits service computation to be executed within network paths, rather than solely on dedicated servers. Although the programmable data plane has showcased notable performance advantages for INC application deployments, its effectiveness is constrained by resource limitations, potentially impeding the expressiveness and scalability of these deployments. Conversely, delegating computational tasks to the control plane, supported by general-purpose servers with abundant resources, offers increased flexibility. Nonetheless, this strategy compromises efficiency to a considerable extent, particularly when the system operates under heavy load. To simultaneously exploit the efficiency of data plane and the flexibility of control plane, we proposeCarlo, a cross-plane collaborative optimization framework to support the network-wide deployment of multiple INC applications across both the control and data plane.Carlofirst analyzes resource requirements of various INC applications across different planes. It then establishes mathematical models for resource allocation in cross-plane and automatically generates solutions using proposed algorithms. We have implemented the prototype ofCarloon Intel Tofino ASIC switches and DPDK. Experimental results demonstrate thatCarlocan effectively trade off between computation time and deployment performance while avoiding performance degradation.

In the field of no-reference image quality assessment (NR-IQA), the visual masking effect has long been a challenging issue. Although existing methods attempt to alleviate the interference caused by masking by generating pseudoreference images, the quality of these images is often constrained by the accuracy and reconstruction capabilities of image restoration algorithms. This can introduce additional biases, thereby affecting the reliability of the evaluation results. To address this problem, we propose a novel generative “noise” estimation framework (GNE-Vim) that eliminates the need for pseudoreference images. Instead, it deeply decouples the distortion components from degraded images and performs quality-aware modelling of these components. During the training phase, the model leverages both reference images and distortion components to guide the learning of the true distortion distribution. In the inference phase, quality prediction is conducted directly on the basis of the decoupled distortion components, making the evaluation results more aligned with human subjective perception. The experimental results demonstrate that the proposed method achieves strong performance across datasets containing various types of distortions. The source code is publicly available at the following website: https://github.com/opencodelxt/GNE-Vim.

Detecting small objects in remote sensing images poses significant challenges to the field of computer vision, primarily stemming from the complexity of backgrounds, limitations in pixel resolution, and information loss during the feature fusion process. While general object detection has significantly advanced in recent years, remote sensing small object detection remains an unsolved problem, with existing frameworks struggling to achieve high performance at small scales. In this article, we propose a novel framework called the boundary-aware feature fusion network (BAFNet), which significantly enhances the model’s ability to represent and locate small objects precisely within complex remote sensing scenarios. First, a dual-stream attention fusion module captures complementary foreground and background cues through bidirectional context modeling. Jointly attending to objects and their surroundings enhances discriminative power for distinguishing small objects. Additionally, we incorporate a boundary-aware branch to better preserve crucial detailed information vital for small-scale objects. This auxiliary component supervises the fusion of contextual semantics and spatial information, aiding in retaining critical boundary details that are prone to loss during cross-layer feature fusion. We conducted experiments on the challenging AI-TOD, VisDrone, DIOR, and LEVIR-Ship datasets. The results demonstrate the superiority of our approach over other state-of-the-art (SOTA) object detection methods, particularly in terms of precisely identifying small objects within remote sensing images. The code is available athttps://github.com/ooo1128/BAFNet.

Vibration isolators are essential for sensitive and precise industrial applications working on instrumentation and control systems since they isolate microvibration, prevent error propagation, and enhance processing/image quality. This article proposes a novel gravity compensator (GC) for vibration isolation in satellite applications based on permanent magnets. Owing to the new magnetic circuit topology, the proposed system features a lower natural frequency and higher force density, enabling superior low-frequency isolation performance and bearing capacity compared with traditional isolators. Owing to its low natural frequency, the proposed system is superior to conventional GCs in isolating low-frequency disturbances. Moreover, the high force density enables the GC to exploit a heavier payload with less magnetic material, which is economical and space-saving. In addition, well-designed electromagnets are utilized to adjust the static levitation force, which can improve the ability to satisfy the requirements of different loads without drastically changing the stiffness of the entire system. The results suggest that the proposed GC could be utilized for satellite applications with heavy loads and limited mounting space.

Multispectral object detection has shown great promise in security and industrial applications. RGB images offer rich texture but are limited by lighting, whereas IR images excel in low light but lack texture. Current methods face challenges in accurately capturing information differences and achieving effective feature fusion across modalities. To address these issues, we propose a graph aggregation alignment network (GAANet) for multispectral object detection. GAANet consists of two key modules: the graph interaction fusion module (GIFM) and the information alignment module (IAM). GIFM uses graph representation learning to effectively process single-modality features, and the direct connection information flow mechanism guides and references low-level multimodal features, ensuring the global and comprehensive fusion of node information in the graph space. The results are then refined through the IAM for secondary calibration and alignment of corresponding local regions, ensuring accurate fusion. We also introduce an information reconstruction path (IRP) and reconstruction loss to prevent the loss of single-modality information due to multiple IAM calculations. GAANet achieves excellent fusion detection capability and significantly reduces the number of parameters, reducing the model size by 61.2% compared with that of representative baselines such as CALNet. GAANet achieves state-of-the-art results on the DroneVehicle, LLVIP, and FLIR datasets, with superior object detection accuracy. It also performs well on the unaligned DVTOD dataset, effectively capturing feature offsets across modalities through global graph perception.

Although many existing schemes were proposed to solve the time delay, energy consumption and system architecture issues of task offloading in internet of vehicles (IoVs), few task offloading schemes focus on the location and mobility of vehicle nodes, the reliability of vehicle nodes and communication links, and the workload of fog servers. In this paper, we propose a location-aware reliable task cooperative-computation scheme under fog computing-based IoVs. In comparison to many existing vehicular cooperative computing schemes, our scheme introduces a dual-layer (single-hop and multi-hop) cooperative vehicle assessment and selection mechanism based on the location-aware principle (such as proximity). The proposed mechanism involves a detailed evaluation for cooperative vehicles from three perspectives: the reliability of cooperative vehicle nodes, the reliability of communication links, and the availability of idle computing resources. The comprehensive evaluation is designed to further facilitate the task allocation between the required tasks and the cooperative vehicles. Under the condition of meeting task requirements, the fog server can select closer (single-hop), more reliable vehicles with greater available computing resources to accomplish related sub-tasks for customer vehicles. Additionally, we further propose an enhanced Hungarian algorithm for task allocation among required sub-tasks and selected cooperative vehicles, which can effectively assign corresponding sub-tasks to suitable cooperative vehicles to reduce the fragmentation of vehicular resources and enhance the utilization of vehicular resources. Experimental results demonstrate the effectiveness of our location-aware reliable task cooperative-computation scheme under fog computing-based IoVs.

Vehicle trajectory prediction is gaining significant attention from academia and industry because of its vital role in autonomous driving. However, current theories face two challenges. First, they generally underperform when faced with longer historical trajectory inputs, especially in large-scale scenarios. Second, they usually ignore the temporal continuity of the target vehicle itself. To address these issues, our study proposed a novel extended temporal sequence fusion attention (ETSFA) network. This network can fully capture the information from the historical trajectory and the dynamic influences of adjacent agents. In addition, the novel dual-channel decoupled model can precisely characterize the intricate spatiotemporal interplay of on-road vehicles. Specifically, the proposed network consists of two main parts. For temporal analysis, the linear inference network (LIN) is reparameterized into complex diagonal forms at the state–space model (SSM) layer to express the linear recurrence capability, thus effectively mining the long-term historical trajectory temporal features of the target vehicle. For spatial analysis, an advanced spatial perception module (SPM) based on graph attention networks (GATs) is proposed to aggregate vehicle and intervehicle interaction features. In addition, a spatial inference module (SIM) based on a convolutional linear inference unit (CONVLIN) is customized for spatiotemporal graph features. Finally, the proposed ETSFA is trained and validated across diverse public datasets, including HighD and NGSIM, demonstrating a marked improvement in the prediction accuracy of the proposed ETSFA over existing methods.

Containers have become popular for deploying applications in Edge Computing (EC) for their seamless integration and easy deployment. Frequent container updates are essential to enhance performance and introduce new challenges for cutting-edge applications such as large language models and digital twins. However, traditional container update methods result in substantial download costs and task interruptions, which are unacceptable for latency-sensitive tasks in resource-constrained EC. Existing work has largely overlooked the layered structure of container images. By leveraging this layered structure, duplicate downloads can be reduced, and various layers can be transferred from other edges, reducing burden on the remote cloud. In this paper, we model the layer-aware container update problem with edge-cloud collaboration to minimize update and scheduling costs. We present the Layer-aware Edge-cloud collaborative Container Update (LECU) algorithm based on reinforcement learning to make container update decisions. Moreover, a task scheduling algorithm is devised to schedule tasks affected by container updates to other edges, minimizing the impact of task interruptions. We implement our LECU algorithm on an edge system with real-world data traces to demonstrate its effectiveness and conduct larger-scale simulations to evaluate its scalability. Results demonstrate that our algorithms reduce container update and task scheduling costs by 14% and 19%, respectively, compared to baselines.

Integrating wireless-powered Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) leverages computation offloading services for mobile devices, significantly enhancing the mobility and control of MEC networks. However, current research has not focused on customizing system designs for Terahertz (THz) communication networks. When dealing with THz communication, one must account for blockage vulnerability due to severe THz wave propagation attenuation and insufficient diffraction. The Intelligent Reflecting Surface (IRS) can effectively address these limitations in the model, enhancing spectrum efficiency and coverage capabilities while reducing blockage vulnerability in THz networks. In this paper, we introduce an upgraded MEC system that integrates IRS and UAVs into THz communication networks, focusing on a binary offloading policy for studying the computation offloading problem. Our primary objective is to optimize the energy consumption of both UAVs and User Electronic Devices, alongside refining the phase shift of the IRS reflector. The problem is a Mixed Integer Non-Linear Programming problem known as NP-hard. To tackle this challenge, we propose a two-stage deep learning-based optimization framework named Iterative Order-Preserving Policy Optimization (IOPO). Unlike exhaustive search methods, IOPO continually updates offloading decisions through an order-preserving quantization method, thereby accelerating convergence and reducing computational complexity, especially when handling complex problems with extensive solution spaces. The numerical results demonstrate that the proposed algorithm significantly improves energy efficiency and achieves near-optimal performance compared to benchmark methods.

Multispectral object detection, which combines RGB visible light and thermal infrared spectral information, has broad applications in complex environments and varying illumination conditions. However, existing methods face challenges in processing multispectral data, such as inconspicuous object features in spectral images and significant discrepancies between input modality spaces and output detection spaces. To address these issues, we propose an innovative multispectral object detection method that combines contrastive learning and a new cross-modal feature fusion module. We introduce a mask feature contrastive loss that maximizes the similarity between the box-level mask features and modal features while suppressing background responses, enabling effective representative alignment between the input and output spaces. Additionally, we propose a mask-guided attention fusion module that uses a predicted pseudo mask to guide the fusion of different modal features, enhancing object responses and reducing background noise interference. Our extensive experiments on several challenging multispectral datasets demonstrate that our proposed COFNet achieves state-of-the-art performance.

Multi-modal Federated Learning (FL) is a type of FL that considers utilizing multiple modalities of data to improve overall performance. While multi-modal data brings richer information, it also introduces more significant communication overhead. Reducing this overhead hinges on two key strategies: increasing the convergence speed of the training or reducing the communication overhead in each communication round. However, few studies have considered these two strategies simultaneously and formed a unified optimization framework. Thus, we propose a joint client and modality selection framework to reduce communication overhead. Modality selection executed on each client assigns weights to modalities based on their contribution to training potential, aiming at accelerating the convergence. Client selection executed on the server assigns weights to clients by considering different metrics, especially total training potential after the modality selection. We validate our proposed method on the five widely used open-source datasets, achieving satisfactory accuracy while reducing the total communication overhead to 2.43%–14.24% compared to without selection on different datasets, significantly outperforming existing state-of-the-art (SOTA) methods. Code is available at https://github.com/1643204431/OCETPMMFL .

The rapid development of computer vision technology for detecting anomalies in industrial products has received unprecedented attention. In this article, we propose a dual teacher–student-based discrimination (DTSD) model for anomaly detection, which combines the advantages of both embedding-based and reconstruction-based methods. First, the DTSD builds a dual teacher‒student architecture consisting of a pretrained teacher encoder with frozen parameters, a student encoder, and a student decoder. By distillation of knowledge from the teacher encoder, the two teacher‒student modules acquire the ability to capture both local and global anomaly patterns. Second, to address the issue of poor reconstruction quality faced by previous reconstruction-based approaches in some challenging cases, the model employs a feature bank that stores encoded features of normal samples. By incorporating template features from the feature bank, the student decoder receives explicit guidance to enhance the quality of reconstruction. Finally, a segmentation network is utilized to adaptively integrate multiscale anomaly information from the two teacher–student modules, thereby improving segmentation accuracy. Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches. The code of DTSD is publicly available at https://github.com/Math-Computer/DTSD .

Graphs offer a flexible structure for vision tasks, with CNNs and Transformers conditioned as two specific cases of graph structures. In CNNs, the input images are treated as graphs where only neighboring patches are connected, whereas Transformers view images as fully connected graphs. To leverage the potential of graphs in video representation learning, effective graph generation and training methods are crucial. To this end, we propose VideoGNN, which represents the video as a discrete time dynamic graph and learns the dynamic graph efficiently. Given the multitude of frames in videos, we introduce an efficient graph generation module characterized by low complexity and high quality, facilitating the transformation of videos into dynamic graphs. Additionally, we introduce a dual-view graph neural network to capture spatial and temporal information from the generated dynamic graphs. Then, a sequential model is applied to capture the long-term temporal information and generate the final frame embeddings. Experiments demonstrate that VideoGNN can achieve competitive results in terms of graph quality assessment and video downstream tasks. The codes are available at https://github.com/Dodo-D-Caster/VideoGNN .

In programmable networks, measurement tasks are placed on programmable switches to monitor network traffic at line rate. These tasks typically require substantial resources (e.g., significant SRAM), while programmable switches are constrained by limited resources due to their hardware design (e.g., Tofino ASIC), making distributed deployment essentially. Measurement tasks must monitor specific network locations or traffic flows, introducing significant complexity in deployment optimization. This target-constrained nature makes task optimization on switches (e.g., task merging) become device-dependent and order-dependent, which can lead to deployment failures or performance degradation if ignored. In this paper, we introduceDisPLOY, a novel target-constrained distributed deployment framework specifically designed for network measurement tasks on the data plane.DisPLOYenables operators to specify monitoring targets—network traffic or device/link—across multiple switches. Given the monitoring targets,DisPLOYeffectively minimizes redundant operations and optimizes deployment to achieve both resource efficiency (e.g., minimizing stage consumption) and high-performance monitoring (e.g., high accuracy). We implement and evaluateDisPLOYthrough deployment on both P4 hardware switches (Intel Tofino ASIC) and BMv2. Experimental results show thatDisPLOYsignificantly reduces stage consumption by up to 66% and improves ARE by up to 78.4% in flow size estimation while maintaining end-to-end performance.

Service function chains (SFCs) are sequences of network functions that provide specific services to meet operators’ needs in today's ISPs and datacenter networks. To improve the performance of SFCs, programmable data planes are used to leverage their low latency and high performance packet processing. However, SFCs need to be adaptable to dynamics such as changes in requirements and attributes. Therefore, the ability to migrate SFCs is essential. Unfortunately, migrating SFCs in distributed programmable data planes is challenging due to the risk of degraded performance and failure to meet SFCs requirements and resource constraints in switches. In this paper, we proposeMonte, which provides an effective SFCs migration scheme in distributed programmable data planes. We build a novel integer programming model to represent the migration process with constraints on resource limitations of switches and SFCs attributes in the distributed data plane. Additionally, an SFCs migration algorithm is designed to optimize the migration cost by deeply analyzing resource allocation in the switch pipeline.Montehas been implemented on both P4 software switches (Bmv2) and hardware switches (Intel Tofino ASIC). Extensive evaluation results show that the migration cost inMonteis 94.03% lower on average than the state-of-the-art deployment scheme, andMontecan effectively save pipeline resources.

In the fast-developing industrial environments, extensive focus on resource management within Mobile Edge Computing (MEC) aims to ensure low-latency QoS, however, some tasks offloaded to the cloud still experience high latency. Additionally, high energy consumption, poor link reliability, and excessive processing delays are intolerable for industrial applications. Compared to general servers, edge computing devices based on Arm architecture exhibit lower latency and higher energy efficiency. This highlights the need for improved heterogeneous Collaborative Edge-Edge Industrial Environments (CEIE) and precise multi-user QoS metrics. Thus, we focus on dynamic resource management within the CEIE architecture to better satisfy diverse industrial applications, formulating a multi-stage Mixed Integer Nonlinear Programming (MINLP) problem to minimize system costs. To reduce the computational complexity of solving the MINLP, we decompose the original problem into multi-user task offloading, Communication Resource Allocation (CmRA), and Computational Resource Allocation (CpRA) problems. These transformed problems are then tackled using DRMQ: an integrated learning optimization approach that combines model-free, priority experience replay-based Double Deep Q-Network (iDDQN) with model-based optimization, accelerating the Q-value function's convergence speed and reducing training time. Extensive simulations show that our proposed optimization scheme can reduce the average weighted system cost by at least 43.168% . Moreover, testbed experiments demonstrate that the proposed algorithm can reduce the average system cost by at least 42.650% in real-world applications, outperforming existing methods.

The rapid growth in demand for large language models (LLMs) has strained cloud-edge infrastructure. While edges offer low latency and clouds provide vast resources, scheduling LLM requests efficiently remains a major challenge due to their unpredictable processing times, which leads to Headof-Line (HOL) blocking that degrades system throughput and responsiveness. To address this, we introduce the Online CloudEdge Collaborative Request Scheduling (OCE-CRS) framework. OCE-CRS models the proactive scheduling of LLM requests as a contextual combinatorial bandit problem. At its core is our novel Combinatorial Neural Delayed Upper Confidence Bound (CN DUCB) algorithm, which learns to predict request processing times from the semantic content of the request prompt alone. This enables an inspired policy based on Shortest Job First (SJF) that prioritizes shorter jobs for edge execution, simultaneously maximizing throughput and mitigating HOL blocking. To prevent time-consuming neural network training from blocking scheduling decisions, we employ an asynchronous mechanism. This decouples model updates from the real-time scheduling loop, effectively handling the resultant delayed feedback where observations from past rounds are used in later training steps. We provide a theoretical sublinear regret bound for our algorithm. Extensive experiments validate that OCE-CRS significantly improves throughput, Job Completion Time (JCT), and queueing delay, demonstrating superior performance and robustness in both static and continuous batching environments.

Containers have emerged as a pivotal tool for service deployment in edge computing. Before running the container, an image composed of several layers must exist locally. Recent strategies have utilized layer-sharing in images to reduce deployment delays. However, existing research only focuses on a single aspect of container orchestration, like container placement, neglecting the joint optimization of the entire orchestration process. To fill in such gaps, this article introduces an online strategy that considers layer-aware container orchestration, encompassing request scheduling, container placement, and resource provision. The goal is to reduce costs, adapt to evolving user demands, and adhere to system constraints. We present an online optimization problem that accounts for various real-world factors in orchestration, including container and server expenses. An online algorithm is proposed, integrating a regularization-based approach and stepwise rounding to address this optimization problem efficiently. The regularization approach separates time-dependent container placement and server wake-up costs, requiring only current information and past decisions. The stepwise rounding process generates feasible solutions that meet system constraints, reducing computational costs. Additionally, a competitive ratio proof is provided for the proposed algorithm. Extensive evaluations demonstrate that our approach achieves about 20% performance enhancement compared to baseline algorithms.

Programmable data plane (PDP) has emerged as a powerful platform for line-rate packet processing, utilizing on-chip register memory to execute stateful applications. Yet most existing efforts concentrate on static approaches for allocating register memory, necessitating switch restarting and service interruption. Despite the availability of research on sharing memory for concurrent applications, the rigid requirement of limiting memory sharing to the same pipeline stages hampers application flexibility and poses scalability challenges. To address this limitation, we presentFlxVRM,a flexible register memory virtualization layerfor data plane P4 programs which supports high-flexibility sharing of register memory for concurrent applications on PDP.FlxVRMenables memory allocation at any stage and location of the pipeline on PDP for each application at run time. To reduce resource usage during virtualization in the data plane pipeline,FlxVRMfurther merges different tables and actions with similar structures within P4 programs. Additionally,FlxVRMprovides a compiler to generate data plane programs for virtualization as well as the control plane API configuration. A prototype ofFlxVRMis implemented based on P4 hardware switches with Intel Tofino ASIC. Our experiment results show thatFlxVRMsignificantly improves the allocatable memory space for applications by up to 50%, while reducing the resource of the table up to 68%.

Mobile edge computing (MEC) is emerging as a critical paradigm to meet the growing computational demands of wireless devices. However, edge servers, wireless devices, and service types in MEC networks are usually time-varying due to configurations, traffic patterns, and operational status, which results in inaccurate state estimations. Therefore, existing Deep Reinforcement Learning (DRL)-based offloading algorithms often fail to effectively handle dependency-aware applications. Furthermore, traditional reward functions adopted in DRL-based algorithms fail to decouple historical dependencies among offloading decisions for subtasks, hindering accurate state updates. To address these challenges, we propose a Status Correction-empowered Deep Reinforcement Learning (SC-DRL) algorithm for making the dependency-aware application offloading decisions in this paper. Specifically, we first adopt the State-Adjusted Bellman Equation to ensure accurate updates of DRL state values. Then, we introduce the dynamic estimate equation to enable DRL agents to estimate system states accurately. Furthermore, we mathematically model device load to extend the dynamic estimate equation to handle real-world complexities. Finally, we propose the Reapplying Reward Technology to reduce reward inaccuracy due to historical dependencies. Both simulations and real-world tests show that the SC-DRL improves the ratio of applications completed within their deadlines by an average of 3.36% and 41.94% compared to the state-of-the-art algorithms, such as Advantage Actor-Critic (A2C), Deep Q-Learning (DQN), and Proximal Policy Optimization (PPO).

Large Language Models (LLMs) are widely used across various domains, but deploying them in cloud data centers often leads to significant response delays and high costs, undermining Quality of Service (QoS) at the network edge. Although caching LLM request results at the edge using vector databases can greatly reduce response times and costs for similar requests, this approach has been overlooked in prior research. To address this, we propose a novelVector database-assisted cloud-Edge collaborativeLLM QoSOptimization (VELO) framework that caches LLM request results at the edge using vector databases, thereby reducing response times for subsequent similar requests. Unlike methods that modify LLMs directly, VELO leaves the LLM's internal structure intact and is applicable to various LLMs. Building on VELO, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and design an algorithm based on Multi-Agent Reinforcement Learning (MARL). Our algorithm employs a diffusion-based policy network to extract the LLM request features, determining whether to request the LLM in the cloud or retrieve results from the edge's vector database. Implemented in a real edge system, our experimental results demonstrate that VELO significantly enhances user satisfaction by simultaneously reducing delays and resource consumption for edge users of LLMs. Our DLRS algorithm improves performance by 15.0% on average for similar requests and by 14.6% for new requests compared to the baselines.

Federated learning (FL) model usually needs to forget what it has learned from a certain client for various considerations, which gives birth to the federated unlearning (FU) technique. Due to the distributed nature of FL, removing a specific client’s contribution from the global model potentially requires the cooperation of all participants, making FU difficult to apply in real-world scenarios. This paper proposes a simple-yet-effective client-free FU algorithm that runs solely on the central server. The algorithm utilizes the cached historical updates of the initial training from clients to rebuild the training after excluding the target client. To circumvent the issue of adaptivity, which is the key challenge for training reconstruction, we leverage the low-dimensional structure of gradient space in deep networks. Specifically, we propose to project the historical gradients to a low-dimensional subspace, which is given by the top gradient eigenspace on a small public dataset. According to experiments on three canonical datasets, our method achieves efficient unlearning while also preserving a high-level model utility.

Serverless computing is a promising approach for edge computing since its inherent features, e.g., lightweight virtualization, rapid scalability, and economic efficiency. However, there are two challenges existing in serverless edge computing: significant cold start latency and request blocking. Previous studies have not successfully resolved these challenges, which affect the Quality of Experience. In this paper, we formulate the Serverless Function Scheduling (SFS) problem in resource-limited edge computing, aiming to minimize the average response time. To solve this intractable scheduling problem, we first consider a simplified offline form of the SFS problem and design a polynomial-time optimal scheduling algorithm. Inspired by this optimal algorithm, we propose an Enhanced Shortest Function First (ESFF) algorithm, including function creation and function replacement. To avoid frequent cold starts, ESFF selectively decides the initialization of new function instances when receiving requests. To deal with request blocking, ESFF judiciously replaces serverless functions based on the function weight at the completion time of requests. Extensive simulations based on real-world serverless request traces are conducted, and the results show that ESFF consistently and substantially outperforms existing baselines under different settings.

The Large Language Model (LLM) has gained significant popularity and is extensively utilized across various domains. Most LLM deployments occur within cloud data centers, where they encounter substantial response delays and incur high costs, thereby impacting the Quality of Services (QoS) at the network edge. Leveraging vector database caching to store LLM request results at the edge can substantially mitigate response delays and cost associated with similar requests, which has been overlooked by previous research. Addressing these gaps, this paper introduces a novel Vector database-assisted cloud-Edge collaborative LLM QoS Optimization (VELO) framework. Firstly, we propose the VELO framework, which ingeniously employs vector database to cache the results of some LLM requests at the edge to reduce the response time of subsequent similar requests. Diverging from direct optimization of the LLM, our VELO framework does not necessitate altering the internal structure of LLM and is broadly applicable to diverse LLMs. Subsequently, building upon the VELO framework, we formulate the QoS optimization problem as a Markov Decision Process (MDP) and devise an algorithm grounded in Multi-Agent Reinforcement Learning (MARL) to decide whether to request the LLM in the cloud or directly return the results from the vector database at the edge. Moreover, to enhance request feature extraction and expedite training, we refine the policy network of MARL and integrate expert demonstrations. Finally, we implement the proposed algorithm within a real edge system. Experimental findings confirm that our VELO framework substantially enhances user satisfaction by concurrently diminishing delay and resource consumption for edge users utilizing LLMs.

In-network computing (INC) is a new paradigm that allows applications to be executed within the network, rather than on dedicated servers. Conventionally, INC applications have been exclusively deployed on the data plane (e.g., programmable ASICs), offering impressive performance capabilities. However, the data plane’s efficiency is hindered by limited resources, which can prevent a comprehensive deployment of applications. On the other hand, offloading compute tasks to the control plane, which is underpinned by general-purpose servers with ample resources, provides greater flexibility. However, this approach comes with the tradeoff of significantly reduced efficiency, especially when the system operates under heavy load. To simultaneously exploit the efficiency of data plane and the flexibility of control plane, we propose Carlo, a cross-plane collaborative optimization framework to support the network-wide deployment of multiple INC applications across both the control and data plane. Carlo first analyzes resource requirements of various INC applications across different planes. It then establishes mathematical models for resource allocation in cross-plane and automatically generates solutions using proposed algorithms. We have implemented the prototype of Carlo on Intel Tofino ASIC switches and DPDK. Experimental results demonstrate that Carlo can compute solutions in a short time while avoiding performance degradation caused by the deployment scheme.

Lightweight containers provide an efficient approach for deploying computation-intensive applications in net-work edge. The layered storage structure of container images can further reduce the deployment cost and container startup time. Existing researches discuss layer sharing scheduling theoretically but with little attention paid to the practical implementation. To fill in this gap, we propose and implement a Layer-aware and Resource-adaptive container Scheduler (LRScheduler) in edge computing. Specifically, we first utilize container image layer information to design and implement a node scoring and container scheduling mechanism. This mechanism can effectively reduce the download cost when deploying containers, which is very important in edge computing with limited bandwidth. Then, we design a dynamically weighted and resource-adaptive mechanism to enhance load balancing in edge clusters, increasing layer sharing scores when resource load is low to use idle resources effectively. Our scheduler is built on the scheduling framework of Kubernetes, enabling full process automation from task information acquisition to container deployment. Testing on a real system has shown that our design can effectively reduce the container deployment cost as compared with the default scheduler.

In beyond 5G (B5G) Internet of Things (IoT) system based on edge intelligence, pay-for-use demand has become a consensus, and the pricing of IoT services has attracted the attention of academia and industry. The pricing method based on noncooperative game allows edge service providers (ESPs) to compete fairly, effectively preventing edge nodes from malicious bidding. However, since only one winner can make a profit each time, it is easy to cause a large number of ESPs to lose money for a long time. To this end, a dynamic alliance pricing mechanism based on distributed edge intelligence is proposed. ESPs can freely choose to form an edge dynamic alliance, which not only retains the independence of edge nodes but also makes full use of the advantages of mutual cooperation between nodes. According to the characteristics of edge nodes, various roles are reasonably divided. In order to prevent abnormal behaviors of edge nodes, we set up necessary restrictive rules. At the same time, we designed a privacy-enhanced joint pricing prediction algorithm to screen the alliance’s candidate solutions to improve pricing efficiency and edge benefit. The experimental results show that, compared with the traditional alliance game method, the performance of the mechanism we proposed improves the utilization rate of edge resources by 32.76%–61.37%. Meanwhile, the prediction accuracy was improved by 16.47%–38.86%, and the average prediction time was reduced by 42.81%–65.57%.

In recent years, the blockchain-based Internet of Things (IoT) has been widely studied and applied, and every IoT device can act as a node in the blockchain. However, these lightweight nodes usually do not have enough computing power to complete the consensus or other computing-required tasks. Edge computing network gives a platform to provide computing power to IoT devices. A fundamental problem is how to allocate limited edge servers to IoT devices in a highly untrusted environment. In a fair competitive environment, the allocation mechanism should be online, truthful, and privacy-preserved. In order to meet these three challenges, we propose an online multi-item double auction (MIDA) mechanism by means of auction theory, where IoT devices are buyers and edge servers are sellers. However, ensuring truthfulness is often contradictory to protecting users’ privacy. The participants’ private information is at risk of being exposed to inference attacks, which may lead to malicious manipulation of the market by adversaries. Thus, we enhance our MIDA mechanism with differential privacy (DP) to protect sensitive information from being leaked. It slightly interferes with the auction results in performance but guarantees privacy protection with high confidence. In addition, we upgrade our privacy-preserved MIDA mechanism such that it adapts to more complex and realistic scenarios. In the end, the effectiveness and correctness of algorithms are evaluated and verified by theoretical analysis and numerical simulations.

From the perspective of saving energy, it is important to maintain a desired coverage ratio with a minimum sensor density in camera sensor networks (CSNs). In some actual applications, the Field of Interest (FoI) is often an irregular bounded area with obstacles existence. The existence of obstacles would have an adverse effect on quality of coverage in CSNs. Besides, due to the camera sensor may be located in the boundary of the FoI, it also has influence on the coverage contribution of the camera sensor. In this article, we assume that heterogeneous camera sensors are randomly deployed in a convex polygon FoI with irregular obstacles existence and consider the border effects to derive the critical density (CD) of camera sensors for a desired coverage ratio in CSNs. We propose the concept of occlusion$K$-coverage, and present the expected effective possible sensing region to evaluate the coverage contribution of the camera sensor. Next, a series of simulation experiments are conducted to demonstrate the impact of border effects and parameters of camera sensor on the occlusion$K$-coverage ratio. The results show that our method can effectively estimate the CD for a desired occlusion$K$-coverage ratio.

With the development of networks and smart devices, artificial intelligence has drawn more and more attention, especially in the Unmanned Aerial Vehicles. Therefore, it is quite critical to train and run DNNs on resource-limited and hardware-constrained UAVs. The traditional methods fail to adjust offloading strategy due to the dynamic environment, while recently proposed intelligent computation offloading techniques rely on accessing IoT devices’ private data, which leads to privacy and security problem. To alleviate the above problems, we propose an novel edge-intelligent-based computation offloading technology via Federated Learning (FL). Specially, we utilize Multi-Layer Perceptron (MLP) to learn the computation tasks features and offload different tasks to different smart devices. Besides, to protect data privacy and improve the system’s security, a hierarchical FL framework is utilized to train the model of the computation tasks features extraction. Finally, performance analysis results obtained by experiments demonstrate the performance of our proposed approach.

Multiple unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) leverages UAVs equipped with computational resources as mobile-edge servers, providing flexibility and low-latency connections, especially beneficial in smart cities and the Internet of Things (IoT). Maximizing Quality of Services (QoS) while minimizing energy consumption necessitates developing a suitable offloading ratio and trajectory control algorithm for UAVs. However, existing research on UAV control algorithms overlooks significant challenges like the heterogeneity of user equipments (UEs) and offloading failures. Furthermore, there is a dearth of experimental validation in large-scale UAV-assisted MEC scenarios. To bridge these gaps, we introduce a QoS-aware energy-efficient multi-UAV offloading ratio and trajectory control algorithm (QEMUOT). Specifically, 1) a composite UE mobility model is proposed to enhance system heterogeneous modeling, encompassing models for high-speed, low-speed, and fixed UEs; 2) QEMUOT is devised using multiagent reinforcement learning algorithms to determine offloading ratio and trajectory control decisions. To tackle sparse reward space and offloading failures, we employ expert demonstrations for pretraining and enhance reward mechanisms; and 3) experimental simulations illustrate that our algorithm outperforms baseline algorithms in user QoS with reduced energy consumption and demonstrates superior scalability in scenarios with numerous UAVs and UEs.

Extending serverless computing to the edge has emerged as a promising approach to support service, but startup containerized serverless functions lead to the cold-start delay. Recent research has introduced container caching methods to alleviate the cold-start delay, including cache as the entire container or the Zygote container. However, container caching incurs memory costs. The system must ensure fast function startup and low memory cost of edge servers, which has been overlooked in the literature. This paper aims to jointly optimize startup delay and memory cost. We formulate an online joint optimization problem that encompasses container scheduling decisions, including invocation distribution, container startup, and container caching. To solve the problem, we propose an online algorithm with a competitive ratio and low computational complexity. The proposed algorithm decomposes the problem into two subproblems and solves them sequentially. Each container is assigned a randomized strategy, and these container-level decisions are merged to constitute overall container caching decisions. Furthermore, a greedy-based subroutine is designed to solve the subproblem associated with invocation distribution and container startup decisions. Experiments on the real-world dataset indicate that the algorithm can reduce average startup delay by up to 23% and lower memory costs by up to 15%.

Object detection plays a key role in helping to enable industrial quality control and safety monitoring. This article introduces a lightweight and efficient transformer-based object detection network called the encoder-free DEtection TRansformer (EF-DETR). This novel architecture enhances the DETR model through a redesigned network structure, leading to improved accuracy in object detection and a more lightweight network. To address the issue of suboptimal object detection accuracy, especially for small objects in the DETR model, we introduce a multiscale feature extractor and a high-efficiency feature fusion module. These components facilitate the direct extraction of fine-grained features, thereby enabling effective object detection. Departing from the use of a high-complexity encoder structure, we explore the utilization of an encoder-free neck structure to reduce the network's computational complexity. In addition, to expedite convergence, denoising training is incorporated into the decoder. This article presents extensive experiments, and the EF-DETR demonstrates strong performance on the MS COCO2017 dataset compared to other popular models.

Feature selection is a crucial step in data mining to enhance model performance by reducing data dimensionality. However, the increasing dimensionality of collected data exacerbates the challenge known as the “curse of dimensionality”, where computation grows exponentially with the number of dimensions. To tackle this issue, evolutionary computational (EC) approaches have gained popularity due to their simplicity and applicability. Unfortunately, the diverse designs of EC methods result in varying abilities to handle different data, often underutilizing and not sharing information effectively. In this article, we propose a novel approach called PSO-based Multi-task Evolutionary Learning (MEL) that leverages multi-task learning to address these challenges. By incorporating information sharing between different feature selection tasks, MEL achieves enhanced learning ability and efficiency. We evaluate the effectiveness of MEL through extensive experiments on 22 high-dimensional datasets. Comparing against 24 EC approaches, our method exhibits strong competitiveness. In addition, we have open-sourced our code on GitHub.

Virtual reality (VR) provides users with an immersive and interactive experience through head-mounted devices, which has attracted increasing attention in recent years. Specifically, tile-based VR content transmission provides a promising approach to alleviate the conflict between limited bandwidth and high-performance requirements (e.g., high-resolution and low-delay). However, the tiling pattern affects the encoding efficiency and visual distortion of the VR content. Accounting for this issue, in this paper, a quality of experience (QoE)-aware cost minimization problem is investigated for a tile-based VR content transmission scenario. In particular, an edge server (ES) co-located at a cellular base station (BS) separates its generated VR content into several tiles according to the tiling pattern selection, and a weighted-to-spherically-uniform quality model is used to evaluate the effect of different tiling patterns on QoE. Moreover, to improve the transmission performance between the edge server and VR users (VRUs), unmanned aerial vehicles (UAVs) are leveraged as relay points to provide line of sight channels. Then, we formulate an optimization problem to minimize the sum of weighted total energy consumption and VR content distortion (i.e., QoE-aware cost) by jointly optimizing the tiling pattern selections, the VRUs-UAV grouping, partial computing decisions, and resource allocation. The formulated problem is a mixed integer non-linear programming problem, which is challenging to solve. To address this difficulty, we equivalently decompose the formulated problem into three subproblems and propose corresponding algorithms to solve them, respectively. Numerical results demonstrate that our proposed solution can effectively reduce the QoE-aware cost for VR content transmission in comparison with other baseline algorithms.

The multi-armed bandit (MAB) models have attracted significant research attention due to their applicability and effectiveness in various real-world scenarios such as resource allocation in uncertain environments, online advertising, and dynamic pricing. As an important branch, the adversarial multi-armed bandit problems with delayed feedback have been proposed and studied by many researchers recently where a conceptual adversary strategically selects the reward distributions associated with each arm to challenge the learning algorithm and the agent experiences a bunch of delays in receiving the corresponding reward feedback from different users after taking an action on them. However, the existing models restrict the feedback to being generated from only one user, which makes models inapplicable to the prevailing scenarios of multiple users (e.g. ad recommendation for a group of users). In this paper, we consider that the delayed feedback results are from multiple users and are unrestricted on internal distribution while the feedback delay is arbitrary and unknown to the player in advance. Also, for different users in a round, the delays in feedback have no assumption of latent correlation. Thus, we formulate an adversarial multi-armed bandit problem with multi-user delayed feedback and design a modified EXP3 algorithm named MUD-EXP3, which makes a decision at each round by considering the importance-weighted estimator of the received feedback from different users. On the premise of known terminal round index$T$, the number of users$M$, the number of arms$N$, and upper bound of delay$d_{max}$, we prove a regret of$\mathcal {O}(\sqrt{TM^{2}\ln {N}(N\mathrm{e}+4d_{max})})$. Furthermore, for the more common case of unknown$T$, an adaptive algorithm named AMUD-EXP3 is proposed with a sublinear regret concerning$T$. Finally, extensive experiments are conducted to indicate the correctness and effectiveness of our algorithms in dynamic environments.

In edge computing, applications can be scheduled in the granularity of inter-dependent tasks to proximate edge servers to achieve high performance. Before execution, the edge server must initialize the corresponding runtime environment, named task startup. However, existing studies on dependent task scheduling severely ignore bandwidth constraints during task startups, which is impractical and incurs a long startup latency. To fill in this gap, we first model the task startup process with bandwidth constraints on edge servers. Then, we formulate the dependent task scheduling problem with startup latency in heterogeneous edge computing. To efficiently generate schedules and satisfy the real-time requirements in edge computing, a novel low-complexity list scheduling algorithm integrated with cloud clone, Startup-aware Dependent Task Scheduling (SDTS), is proposed. Constrained by bandwidth and computation resources, SDTS first coordinates task startup, dependent data transmission, and task execution to optimize each task’s finish time. Then, a cloud clone for each task is deployed to utilize scalable resources and initialized runtime environments. Furthermore, task scheduling refinement is designed to release the bandwidth and computation resources consumed by redundant tasks and improve the schedule. Extensive simulations based on real-world datasets show that SDTS substantially reduces 30%-60% makespan compared with existing baselines.

Containers have gained popularity in Edge Computing (EC) networks due to their lightweight and flexible deployment advantage. In resource-constrained EC environments, overbooking container resources can substantially improve resource utilization. However, existing work overlooks the complex interplay between resource provisioning and container scheduling, which may result in performance degradation or inefficient resource utilization due to highly dynamic resource heterogeneity in EC. To address this issue, this paper presents a novel joint Resource Overbooking and Container Scheduling (ROCS) algorithm. Our approach accounts for resource heterogeneity and the geographical distribution of edge nodes, and we formulate the ROCS problem to consolidate various costs and revenues into a single profit metric for service providers. To enhance resource utilization and maximize the profit of the service providers, we develop an efficient algorithm that operates within a hybrid action space scheme by leveraging soft actor-critic reinforcement learning. Furthermore, we introduce a risk assessment mechanism to mitigate overbooking risks. Large-scale simulations with real-world data traces demonstrate the efficacy of our proposed ROCS algorithm, validating its advantage of improving resource utilization within EC networks.

Collaborative Mobile Edge Computing (MEC) has emerged as a promising approach to provide low service latency for computation-intensive Internet of Things applications, facilitated by the cooperation of edge-edge and edge-cloud resources. However, existing collaborative MEC methods typically restrict the collaborative processing between any two Edge Servers (ESs) or one ES and the cloud server for a task request, limiting the exploitation of available resources on other ESs. Moreover, these conventional methods rely on offline task partitioning, potentially leading to extended make-span, especially when ES computing capacities exhibit heterogeneity. In this paper, we propose an innovative method named SMCoEdge. This method performs dynamic parallel multi-ES selection and workload allocation in heterogeneous collaborative MEC environments, thus simultaneously enabling multiple ESs' idle resources to accelerate task processing. We formulate our problem into an online linear programming problem, with the objective of minimizing task computing and transmission make-spans. To enhance computational efficiency, we decompose the problem into two stages: multi-ES selection and workload allocation. Then, we propose an online Deep Reinforcement Learning based Simultaneous Multi-ES Offloading (DRL-SMO) algorithm along with a top-$k$deep Q-learning network model to effectively solve our problem, where an efficient algorithm is proposed to achieve the optimal solution for the workload allocation stage. Furthermore, we provide a theoretical performance analysis, demonstrating that the DRL-SMO algorithm achieves a near-optimal solution for our problem within an approximate linear time complexity. Finally, our extensive experimental results demonstrate the substantial advantages of our method. It consistently reduces the average make-span by 19.63% and keeps a lower offloading failure rate, when compared to state-of-the-art methods. These findings underline the efficacy of our method in enhancing collaborative MEC performance.

Images are composed of “things” (i.e., structured objects) and “stuff” (i.e., textured surfaces), which have completely different effects on the human visual system (HVS). A good image quality assessment (IQA) method should fully consider the visual salience effects of image structures and the masking effects of image textures. In this article, we propose a perceptual quality analysis model using structure separation and high-order moments (SSHMPQA) in the deep domain. First, we use a total variation (TV) model to separate the perceptual structures in images from their deep feature maps, thereby maintaining meaningful object shapes with texture suppression and defining perceptual structure-aware distances in the deep domain. Then, we use the first- to fourth-order moments to calculate the mean, skewness and kurtosis of the probability distributions of the deep features. On this basis, we define a perceptual texture-aware distance in the deep domain. We then formulate the final model by solving a well-defined perceptual optimization problem. The proposed SSHMPQA model has good interpretability and is data-driven; moreover, the model does not require a complex and long training process because the optimization problem is convex and has an exact analytical solution. To verify the effectiveness of our model, comprehensive experiments are conducted. The experimental results show that the proposed model is superior to other state-of-the-art traditional and deep learning-based full-reference (FR) IQA methods.

In edge-assisted vehicular networks, containers are very suitable for deploying applications and providing services due to their lightweight and rapid deployment. To provide high-quality services, many existing studies show that the containers need to be migrated to follow the vehicles’ trajectory. However, it has been conspicuously neglected by existing work that making full use of the complex layer-sharing information of containers among multiple users can significantly reduce migration latency. In this paper, we propose a novel online container migration algorithm to reduce the overall task latency. Specifically: 1) we model the multi-user layer-aware online container migration problem in edge-assisted vehicular networks, comprehensively considering the initialization latency, computation latency, and migration latency. 2) A feature extraction method based on attention and long short-term memory is proposed to fully extract the multi-user layer-sharing information. Then, a policy gradient-based reinforcement learning algorithm is proposed to make the online migration decisions. 3) The experiments are conducted with real-world data traces. Compared with the baselines, our algorithms effectively reduce the total latency by 8% to 30% on average.

Application-layer distributed denial-of-service (DDoS) attacks incapacitate systems by using up their resources, causing service interruptions, financial losses, and more. Consequently, advanced deep-learning techniques are used to detect and mitigate these attacks in cloud infrastructures. However, in mobile edge computing (MEC), it becomes economically impractical to equip each node with defensive resources, as these resources may largely remain unused in edge devices. Furthermore, current methods are mainly concentrated on improving the accuracy of DDoS attack detection and saving CPU resources, neglecting the effective allocation of computational power for benign tasks under DDoS attacks. To address these issues, this paper introduces SecEG, a secure and efficient strategy against DDoS attacks for MEC that integrates container-based task isolation with lightweight online anomaly detection on edge nodes. More specifically, a new model is proposed to analyze resource contention dynamics between DDoS attacks and benign tasks. Subsequently, by employing periodic packet sampling and real-time attack intensity predicting, an autoencoder-based method is proposed to detect DDoS attacks. We leverage an efficient scheduling method to optimize the edge resource allocation and the service quality for benign users during DDoS attacks. When executed in the real-world edge environment, our experimental findings validate the efficacy of the proposed SecEG strategy. Compared to conventional methods, the service rate of benign requests increases by 23% under intense DDoS attacks, and the CPU resource is saved up to 35%.

In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Cluster software upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers during edge cluster upgrades to minimize the impact on running tasks. This article proposes a latency-aware container scheduling algorithm for efficient edge cluster upgrading. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm that addresses this problem by considering the characteristics of MEC, including heterogeneous resources, image distribution, and low-latency requirements. Subsequently, a location feature extraction method based on self-attention is designed to fully extract and utilize edge node distribution. 3) Experiments based on simulated and real-world data traces demonstrate that our algorithm reduces total task latency by approximately 30% compared to baseline algorithms.

Network measurement is essential for efficient network management and operations. In-band network telemetry (INT) offers fine-grained per-device per-packet information which could provide full-visibility for networks. However, the existing solutions fall short in achieving high accuracy, generality, and low overhead simultaneously. To address this limitation, we introduceOffsetINTto meet these three criteria. The key idea ofOffsetINTis to use minimal bits to carry collected states during monitoring, which is based on our observation that the value of telemetry states are usually very close (e.g., the time of adjacent arrival packets) or small (e.g., only a few tens of microseconds for processing latency) for most of the time in real networks. Instead of embedding complete values of state in packets,OffsetINToptimizes bit usage by encoding an offset (using fewer bits), which is carried in-band by passing packets to the end-hosts for recovery and analysis. We theoretically derive the bounds of bandwidth mitigation forOffsetINT. We have implementedOffsetINTin both P4 hardware switches (with Intel Tofino ASIC) and BMv2. Expensive evaluation results show thatOffsetINTcan achieve an accuracy of up to 100% compared to the original INT while reducing INT bandwidth by up to 48%.

Multi-Intent Spoken Language Understanding (SLU), a novel and more complex scenario of SLU, is attracting increasing attention. Unlike traditional SLU, each intent in this scenario has its specific scope. Semantic information outside the scope even hinders the prediction, which tremendously increases the difficulty of intent detection. More seriously, guiding slot filling with these inaccurate intent labels suffers error propagation problems, resulting in unsatisfied overall performance. To solve these challenges, in this paper, we propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on Transformer, which contains a Scope Recognizer (SR) and a Result Attention Network (RAN). SR assignments scope information to each token, reducing the distraction of out-of-scope tokens. RAN effectively utilizes the bidirectional interaction between SF and ID results, mitigating the error propagation problem. Experiments on two public datasets indicate that our model significantly improves SLU performance (5.4% and 2.1% on Overall accuracy) over the state-of-the-art baseline.

Stock trend prediction (STP) aims to predict the price fluctuation, which is critical in financial trading. The existing STP approaches only use market data with the same granularity (such as daily market data). However, in the actual financial investment, there are a large number of more detailed investment signals contained in finer-grained data (e.g, high- frequency data). This motivates us to research how to leverage multi-granularity market data to capture more useful information and improve the accuracy in the task of STP. However, the effective utilization of multi-granularity data presents a major challenge. Firstly, the iteration of multi-granularity data with time will lead to more complex noise, which makes it difficult to identify and extract it. Secondly, the difference in granularity may lead to opposite target trends in the same time interval. Thirdly, the target trends of stocks with similar features can be quite different, and different sizes of granularity will aggravate this gap. In order to address the above three challenges, in this paper, we present a self-supervised framework of multi-granularity denoising contrast learning (MDC). Specifically, we construct a dynamic dictionary of memory, which can obtain clear and unified representations by filtering noise and aligning multi- granularity data. Moreover, we design two contrast learning objectives to solve the differences in trends by constructing additional self-supervised signals. Extensive experiments on the CSI 300 datasets show that our framework stands out from the existing top-level systems and has excellent profitability in real investing scenarios.

The integration of edge computing and deep neural networks (DNNs) holds great promise for enhancing application intelligence. Edge devices generate or collect vast amounts of data, which DNNs can leverage to make informed decisions. Nevertheless, the limited resources of edge devices pose a significant challenge for deploying DNNs. To accommodate some edge devices (e.g. smart watches), lightweight models are often required. However, the accuracy of these models may not meet user expectations. In this paper, we present EKDF, an ensemble knowledge distillation framework that crafts lightweight models for collaborative DNN inferences. More specifically, we utilize knowledge distillation to compress DNN models. On this basis, we introduce multi-teacher joint supervision and dropout in knowledge distillation to improve model performance and preserve the diversity between the generated DNN models. This process produces a range of compact models of varying computational complexity for different edge devices. The experimental results demonstrate that our proposed EKDF can greatly improve the overall predictive ability.

In the classic network security games, the defender distributes defending resources to the nodes of the network, and the attacker attacks a node, with the objective of maximizing the damage caused. In this paper, we consider the network defending problem against contagious attacks, e.g., the attack at a node u spreads to the neighbors of u and can cause damage at multiple nodes. Existing works that study shared resources assume that the resource allocated to a node can be shared or duplicated between neighboring nodes. However, in the real world, sharing resource naturally leads to a decrease in defending power of the source node, especially when defending against contagious attacks. Therefore, we study the model in which resources allocated to a node can only be transferred to its neighboring nodes, which we refer to as a reallocation process. We show that the problem of computing optimal defending strategy is NP-hard even for some very special cases. For positive results, we give a mixed integer linear program formulation for the problem and a bi-criteria approximation algorithm. Our experimental results demonstrate that the allocation and reallocation strategies our algorithm computes perform well in terms of minimizing the damage due to contagious attacks.

Edge Computing (EC) with cloud-like Quality of Service (QoS) can find its wide applications in various resource-constrained smart cities where the resource requirements can be different during peak and off-peak periods. During off-peak periods, there are often many resources that have been requested but not used, which can be reused to obtain higher profit. However, to the best of our knowledge, there is no effective pricing model or overbooking mechanism in EC. To fill in this gap, a novel pricing model for dynamic resource overbooking is proposed in this paper, specifically: 1) To meet the needs of different users in EC, methods of on-demand, daily, auction, and the new spot billing are designed, in which resources can be overbooked. 2) An auction approach with pricing rule and winner determination rule is designed for auction billing, which is proved to guarantee individual rationality, computational efficiency, and truthfulness. 3) To make more use of the auction approach to utilize idle resources, a dynamic resource overbooking mechanism is introduced, including a cancellation policy and a resource prediction method. The mechanism is validated with real-world data-trace. Experimental results show that the dynamic resource overbooking mechanism maximizes the profit of edge nodes with a high QoS Satisfaction ratio of on-demand and daily billing.

Fast and accurate image retrieval is an important and challenging task in massive image data scenarios. As the core technology of image retrieval tasks, deep metric learning aims at learning effective embedding representations that possess two properties among data points: positive concentrated and negative separated. In this work, we propose a multilevel similarity-aware method based on deep local descriptors for deep metric learning. We take the rich interclass similarity relationship based on the deep local invariant descriptors from the data into account to optimize sampling strategies for mining informative samples. The method dynamically adjusts the margin between data points to better match the true similarity relationship between classes. Specifically, for images in a batch, we first obtain deep local descriptors and calculate the similarity matrix of the channel, pixel, and spatial levels. Then, depending on the calculated comprehensive similarity matrix, we propose a multilevel similarity-aware loss function through the deviation between pairwise distance and violate margin to make full use of informative samples. The experimental results demonstrate that our proposed method outperforms other state-of-the-art methods in terms of fine-grained image retrieval and clustering tasks.

Advanced 360$^\circ$video streaming is essential for high-quality communication and industrial video applications, supporting novel and interactive visual experiences that promote consumption. However, due to the inherent fluctuations in communication networks, hardware resources, and power costs, a tradeoff needs to be achieved between occupying network bandwidth and guaranteeing 360$^\circ$visual quality. To address this issue, this article proposes a quality-aware global optimization solution that combines different types of sensory characteristics to further enhance the 360$^\circ$streaming efficiency in cellular networks. First, considering the characteristics of 360$^\circ$video, each frame is divided into three different regions based on a proposed field-of-view prediction method. Second, a new region-based rate-distortion model reflecting the bitrate-quality relationship is proposed. The divided regions are represented by different rate-distortion models. In addition, a rate-distortion model parameter update strategy with robustness to region changes is proposed to further guarantee transmission performance. Finally, we propose a globally optimized adaptive bitrate allocation algorithm to optimize 360$^\circ$mobile streaming, which uses both the rate-distortion models and viewpoint prediction results. Evaluation results indicate that the proposed method outperforms state-of-the-art approaches in terms of several quality of experience objectives under various network conditions.

In this paper, a joint decision tree and visual feature optimization rate control scheme for ultrahigh-definition (UHD) versatile video coding (VVC) is proposed. First, we design a new rate-distortion (R-D) model for UHD videos, and we establish a decision-tree-based multiclass classification scheme to improve the prediction accuracy of the R-D model by fully considering visual features. Second, based on the proposed R-D model, the globally optimal solution is obtained through convex optimization. Finally, we embed our algorithm into the latest VVC reference software, VTM 10.2. According to our experimental results, compared with the latest algorithm in VTM 10.2 and other state-of-the-art algorithms, our method can achieve significant bit rate reductions while maintaining a given peak signal-to-noise ratio (PSNR) or structural similarity index measure (SSIM).

How to globally construct the effective traffic congestion control and management method on large-scale urban road network is an important research challenge. Since an urban region can be divided into many areas to be managed, grid management has great potential to improve traffic management on large-scale urban road network. In this paper, we propose an adaptive area-based traffic congestion control and management scheme, which is based on fog computing-based internet of vehicles. In our proposed scheme, an urban region is divided to many traffic management areas. Based on the real-time dynamic traffic tightness degrees and the static correlation degrees between these divided management areas, we can obtain the complete real-time regional traffic correlations of these divided management areas. Further, when a traffic congestion occurs, the fog servers use the constructed measures to control the signal lights in real time and effectively guide the traffic flow through the cooperation between these divided management areas. The experimental results show our proposed scheme can effectively implement the road guidance and traffic light control to alleviate or eliminate traffic congestion when a traffic congestion occurs.

Federated learning (FL) has been considered as a promising paradigm for enabling distributed machine learning (ML) in wireless networks. To address the limited energy capacity of wireless devices, we propose a simultaneous wireless information and power transfer (SWIPT) aided FL, in which one FL server (FLS) co-located at a cellular base station (BS) uses SWIPT to simultaneously broadcast the global model to wireless user-devices (UDs) and provide wireless power transfer to them. The UDs then use the harvested energy to train their local models and further transmit the local models to the FLS for aggregation. To improve the spectrum efficiency, we consider that the UDs form a non-orthogonal multiple access (NOMA) group for simultaneously sending their local models over the same spectrum channel. Taking the UDs’ time-varying available energy and channel conditions into account, we propose a dynamic optimization of the UDs-scheduling, the BS's transmit-power allocation, and the UDs’ power-splitting factors for SWIPT, with the objective of minimizing the long-term energy consumption while ensuring the FL convergence. The optimization problem, however, is challenging to solve since it is a finite-horizon dynamic programming problem but with an unknown stopping time, and moreover, the action space covers both discrete and continuous variables. To address these difficulties, we first execute a series of equivalent transformations to reduce the number of decision variables and then formulate the problem as a stochastic shortest path problem, based on which we propose an actor-critic deep reinforcement learning algorithm with the proximal policy optimization to efficiently learn the policy that dynamically adjusts the UDs-scheduling for FL as well as the BS's transmit-power for SWIPT. Numerical results validate the effectiveness and performance of our proposed algorithm. The results demonstrate that our proposed algorithm can effectively reduce the long-term energy consumption in comparison with two baseline algorithms.

In Mobile Edge Computing (MEC), latency-sensitive mobile applications comprising dependent tasks can be scheduled to edge or cloud servers to reduce latency and execution costs. However, existing algorithms based on deadline distribution can hardly satisfy tight application deadlines in heterogeneous MEC due to lacking a global view of the future impacts on descendant tasks. To fill in this gap, we formulate the deadline-constrained cost optimization problem for dependent task scheduling in MEC and propose a low-complexity scheduling algorithm that considers a single task's future impacts in two stages. Specifically: (1) In the edge scheduling stage, each task is scheduled according to its successors’ latest start times instead of its sub-deadline to alleviate the lateness of its successors. An edge-only schedule plan is generated by scheduling tasks only on edge servers to save execution costs. (2) In the cloud offloading stage, in order to utilize the powerful cloud resources to satisfy the deadline, the edge-only schedule plan missing the deadline is efficiently modified by properly offloading multiple successive tasks to the cloud. Simulation results show the substantial advantage of the proposed algorithm over baselines in both online and offline scenarios.

Thanks to the wide deployment of heterogeneous radio access networks (RANs) in the past decades, the emerging paradigm of multi-access mobile edge computing, which allows mobile terminals to simultaneously offload the computation-workloads to several different edge-computing servers via multi-RANs, has provided a promising scheme for enabling the computation-intensive mobile Internet services in future wireless systems. The broadcasting nature of radio transmission, however, may lead to a potential secrecy-outage during the offloading transmission. In this paper, we thus investigate the energy-efficient multi-access mobile edge computing with secrecy provisioning. Specifically, we first investigate the scenario of one wireless device's (WD's) multi-access offloading subject to a malicious node's eavesdropping. By characterizing the WD's secrecy based throughput in its offloading transmission, we formulate a joint optimization of the WD's multi-access computation offloading, secrecy provisioning, and offloading-transmission duration, with the objective of minimizing the WD's total energy consumption, while providing a guaranteed secrecy-outage during offloading and a guaranteed overall-latency in completing the WD's workload. Despite the non-convexity of this joint optimization problem, we exploit its layered structure and propose an efficient algorithm for solving it. Based on the study on the single-WD scenario, we further investigate the scenario of multiple WDs, in which a group of WDs sequentially execute the multi-access computation offloading, while subject to a malicious node's eavesdropping. Taking the coupling effect among different WDs into account, we propose a swapping-heuristic based algorithm (that uses our proposed single-WD algorithm as a subroutine) for finding the ordering of the WDs to execute the multi-access computation offloading, with the objective of minimizing all WDs’ total energy consumption. Extensive numerical results are provided to validate the effectiveness and efficiency of our proposed algorithms. The results demonstrate that our algorithms can outperform some conventional fixed offloading scheduling scheme and randomized offloading ordering scheme.

Due to the features of lightweight and easy deployment, the use of containers has emerged as a promising approach for Mobile Edge Computing (MEC). Before running the container, an image composed of several layers must exist locally. However, it has been conspicuously neglected by existing work that task scheduling at the granularity of the layer instead of the image can significantly reduce the task completion time to further meet the real-time requirement and resource efficiency in resource-limited MEC. To bridge the gap, considering the complex dependency between layers and images, a novel layer dependency-aware container scheduling algorithm is proposed to reduce the total task completion time. Specifically: 1) We model the online layer dependency-aware scheduling problem for containers in a heterogeneous MEC, considering the layer download time and task computation time. 2) A policy gradient algorithm is proposed to solve this problem, and the high-dimensional and low-dimensional relations for layer dependencies are extracted with improved action selection. 3) Experiments based on the real-world data trace show that the proposed algorithm outperforms the image-based and layer-based baseline algorithms by 54% and 19% on average, respectively.

Compared with cloud computing-based data storage, distributed data storage in fog computing is more vulnerable to malicious attacks. So, it is very necessary to provide a secure distributed auditing mechanism with protecting the identity privacy of data owners and controlling the identities of auditors under fog computing-based data storage. In this paper, we propose a dual attribute-based auditing scheme for fog computing-based data dynamic storage. Our auditing scheme can protect the identity privacy of data owners, and provide an attribute-based access control for corresponding audits with a distributed collaborative verification between related fog servers. In our scheme, a data owner can securely upload his divided and blinded file blocks with corresponding block authenticators (related with his attribute set) to related fog servers. To prevent malicious auditors from consuming system resources by abusing audit requests, the data owner can provide an attribute-based access control for corresponding audits, where the data owner specifies the attribute set of corresponding auditors who have the right to check the integrity of related data. Further, a distributed collaborative verification mechanism between related fog servers is constructed to reduce the disadvantages of centralized verification, where the Shamir’s secret-sharing method is used to decompose the picked blinding factor as the shared sub-secrets sent to each fog server respectively. Compared with cloud computing-based data storage, our collaborative verification mechanism can implement distributed auditing consent of stored data between multiple fog servers. Our auditing scheme can further audit out specific suspicious fog servers. Additionally, we provide a dynamic data operation mechanism to efficiently support the updating of users’ data under fog computing-based data storage. Furthermore, related theoretical analysis and experimental evaluation show our scheme is secure and efficient.

Energy-efficient task scheduling in data centers is a critical issue and has drawn wide attention. However, the task execution times are mixed and hard to estimate in a real-world data center. It has been conspicuously neglected by existing work that scheduling decisions made at tasks’ arrival times are likely to cause energy waste or idle resources over time. To fill in such gaps, in this paper, we jointly consider assignment and migration for mixed duration tasks and devise a novel energy-efficient task scheduling algorithm. Task assignment can improve resource utilization, and migration is required when long-running tasks run in low-load servers. Specifically: 1) We formulate mixed duration task scheduling as a large-scale Markov Decision Process (MDP) problem; 2) To solve such a large-scale MDP problem, we design an efficient Deep Reinforcement Learning (DRL) algorithm to make assignment and migration decisions. To make the DRL algorithm more practical in real scenarios, multiple optimizations are proposed to achieve online training; 3) Experiments with real-world data have shown that our algorithm outperforms the existing baselines 14% on average in terms of energy consumption while keeping the same level of Quality of Service (QoS).

Spoken Language Understanding (SLU), a core component of the task-oriented dialogue system, expects a shorter inference facing the impatience of human users. Existing work increases inference speed by designing non-autoregressive models for single-turn SLU tasks but fails to apply to multi-turn SLU in confronting the dialogue history. The intuitive idea is to concatenate all historical utterances and utilize the non-autoregressive models directly. However, this approach seriously misses the salient historical information and suffers from the uncoordinated-slot problems. To overcome those shortcomings, we propose a novel model for multi-turn SLU named Salient History Attention with Layer-Refined Transformer (SHA-LRT), which comprises a SHA module, a Layer-Refined Mechanism (LRM), and a Slot Label Generation (SLG) task. SHA captures salient historical information for the current dialogue from both historical utterances and results via a well-designed history-attention mechanism. LRM predicts preliminary SLU results from Transformer’s middle states and utilizes them to guide the final prediction, and SLG obtains the sequential dependency information for the non-autoregressive encoder. Experiments on public datasets indicate that our model significantly improves multi-turn SLU performance (17.5% on Overall) with accelerating (nearly 15 times) the inference process over the state-of-the-art baseline as well as effective on the single-turn SLU tasks.

This article proposes a frequency-based structure and texture decomposition model in a Retinex-based framework for low-light image enhancement and noise suppression. First, we utilize the total variation-based noise estimation to decompose the observed image into low-frequency and high-frequency components. Second, we use a Gaussian kernel for noise suppression in the high-frequency layer. Third, we propose a frequency-based structure and texture decomposition method to achieve low-light enhancement. We extract texture and structure priors by using the high-frequency layer and a low-frequency layer, respectively. We present an optimization problem and solve it with the augmented Lagrange multiplier to generate a balance between structure and texture in the reflectance map. Our experimental results reveal that the proposed method can achieve superior performance in naturalness preservation and detail retention compared with state-of-the-art algorithms for low-light image enhancement. Our code is available on the following website. 1

Containers are becoming a popular way of running applications in edge computing. Before running the application, the edge node must download the application’s container image consisting of multiple layers. However, given the limited bandwidth in edge computing, the container startup latency due to long image download time seriously affects the real-time performance. In this article, we jointly determine the container assignment and the layer download sequence to reduce the total startup latency. We formulate the Container Assignment and Layer Sequencing (CALS) problem and prove its NP-hardness. A Layer-Aware Scheduling Algorithm (LASA) is proposed, fully considering layer sharing among images. First, layers shared by the same set of images are grouped to reduce CALS’s problem scale without affecting the optimal result. Second, considering both layer sharing and existing layer size on edge nodes, a layer-aware algorithm is designed to assign containers to appropriate edge nodes. Finally, to determine the layer download sequence on each edge node, an approximation algorithm is proposed. We further analyze the approximation ratio of LASA in the case of identical edge nodes with sufficient capacity. Extensive experiments based on real-world data show the effectiveness of LASA, which reduces the total startup latency by 40% to 60%.

Service function chains (SFCs) are fundamental services in today's datacenters and ISP networks. Explosive volume of network traffic creates high demands for low latency and high performance. The emergence of programmable data planes has offered a new way to overcome the problem. However, limited by pipeline constraints in hardware architecture, implementing multiple network functions on programmable data planes is challenging. Besides, considering various types of network functions, e.g., stateful network functions, a general model is essential for abstracting distinct network functions. In this article, we proposepSFCwhich provides a fine-grained SFCs deployment scheme in programmable data planes. Control flow graph (CFG) is proposed to abstract and analyze various network functions. Then we model pipeline constraints in the hardware architecture using an ILP (Integer Linear Programming), and model the SFCs deployment in the substrate network as a one big switch (OBS) problem. To reduce deployment cost,pSFCfirst composes multiple SFCs to a compound CFG for eliminating redundant logics within SFCs, further decomposes the compound CFG based on the resource limitation per stage, and finally maps the OBS into the substrate network. We have implementedpSFCin both bmv2 software switch and P4 hardware switch (i.e., Intel Tofino ASIC). Evaluation results show thatpSFCreduces switch costs by 45.7% and decreases average latency by 22% without compromising throughput.

Network functions perform specific packet processing on network traffic. To meet operators' needs, forming service function chains (SFCs) is a fundamental technique used in today's ISPs and datacenter networks. Implementing SFCs in the programmable data plane with high throughput and low latency is a new approach to satisfy demands of ever-growing network traffic. Previous works have proposed different solutions to solve the problem, but they all inevitably have to make trade-offs between running time and performance. For example, an ILP (Integer Linear Programming) can optimize cost but suffers from long running time in large-scale network topologies. Heuristic algorithms depend strongly on manual designs and usually have a performance gap with the optimal solution. In this paper, we proposeDapper, a framework for deploying SFCs in the programmable data plane using DRL (Deep Reinforcement Learning) with graph convolutional network. In order to expand the searching space to prevent the optimal value from being missed,Dapperallows the RL (Reinforcement Learning) agent to simultaneously extract features from both the substrate network and the hardware pipeline, and exploit a graph convolutional network to enhance performance. Moreover, a mask mechanism is also designed to accelerateDapperand improve its scalability.Dapperhas been implemented and extensively evaluated on both P4 hardware switches (equipped with Intel Tofino ASIC) and software switches (i.e., bmv2). Experimental results show thatDappercan automatically generate deployment solutions in a few seconds of running time after training. They also demonstrate thatDapperreduces hardware stage usage and the latency of SFCs by up to 17.8% and 50$\sim$73% respectively on average when compared with heuristics.

With the rapid development of wireless sensor networks (WSNs) and the Internet of Things (IoT), increasing computing tasks are sinking to mobile edge networks, such as distributed learning systems. These systems benefit from the massive amounts of data and computing power on mobile devices and can learn qualified models on the premise of protecting user privacy. In fact, coordinating mobile devices to participate in computing is challenging. On the one hand, the heterogeneous performance of devices makes it difficult to guarantee computing efficiency. On the other hand, there are unreliable factors in the mobile network, which will destroy the stability of the distributed learning. Therefore, we design a three-layer framework called an edge-intelligence-based distributed learning system (EIDLS). Specifically, a novel multilayer perceptron-based device availability evaluation model is proposed to select devices with good performance. The evaluation model performs online learning and optimization according to the resources (CPU, battery, etc.) of devices. Meanwhile, we propose a dynamic trust evaluation algorithm to reduce the side effects of unreliable devices. The experimental results of some commonly used datasets validate that the proposed EIDLS dramatically minimizes the energy consumption and communication cost and improves the calculation accuracy and the stability of the system.

The discovery of biomarker genes from gene expression data is a hot topic for understanding the mechanisms underlying disease etiology. However, while the collection of high-dimensional gene expression data has been made possible by the adoption of technologies such as DNA microarray, it also poses challenges for the identification of key disease-causing genes due to its high-dimensional nature. To address this problem, we propose a feature weighting particle swarm optimization method (FWPSO) for efficiently identifying biomarker genes from high-dimensional microarray data. Specifically, there are two significant phases in FWPSO: 1) Feature Weighting Phase: Features will be discriminated into relevant and irrelevant based on the evolutionary performance of individuals in the PSO population in each generation, and features will be assigned weights based on this. 2) Feature Selection Phase: By focusing the search on a feature set that have been determined to be relevant based on the results of the previous phase, the PSO population will improve the efficiency of removing redundant features and discovering the most related genes. Both phases work together and operate in synergy to achieve the optimized results. The experimental results on four microarray datasets shows that FWPSO not only reduces the number of feature dimensions to a large extent, but also achieves higher classification accuracy compared to other methods, demonstrating the effectiveness of our method. Our implementation of FWPSO is available at https://github.com/wangxb96/FWPSO.

This paper proposes a global optimization solution that combines different types of perceptual information to further improve transmission efficiency. First, a new rate-distortion (R-D) model is proposed to reflect the characteristics of 360degree video. Second, a globally optimized adaptive bitrate control algorithm is proposed using both the R-D models to adjust the bitrate for each tile in a segment. Finally, a new model parameter updating strategy that is robust to quality variations is proposed to further reduce prediction errors. Comparison results indicate that the proposed method can outperform state-of-the-art methods in terms of various quality of experience (QoE) objectives1.

The Stackelberg security game is played between a defender and an attacker, where the defender needs to allocate a limited amount of resources to multiple targets in order to minimize the loss due to adversarial attack by the attacker. While allowing targets to have different values, classic settings often assume uniform requirements to defend the targets. This enables existing results that study mixed strategies (randomized allocation algorithms) to adopt a compact representation of the mixed strategies. In this work, we initiate the study of mixed strategies for the security games in which the targets can have different defending requirements. In contrast to the case of uniform defending requirement, for which an optimal mixed strategy can be computed efficiently, we show that computing the optimal mixed strategy is NP-hard for the general defending requirements setting. However, we show that strong upper and lower bounds for the optimal mixed strategy defending result can be derived. We propose an efficient close-to-optimal Patching algorithm that computes mixed strategies that use only few pure strategies. We also study the setting when the game is played on a network and resource sharing is enabled between neighboring targets. Our experimental results demonstrate the effectiveness of our algorithm in several large real-world datasets.

In vehicle ad hoc networks (VANETs), if a legal vehicle node becomes malicious, then it is more likely to tamper with transferred data or provide false data easily. Because the malicious node is a valid internal user in VANETs, its behavior is difficult to be detected only through some cryptographic methods. Then the behavior may cause many serious traffic accidents. Based on the available (unencrypted) data only, how to detect out the internal malicious vehicle nodes by some lightweight methods needs to be researched in VANETs. Additionally, fog computing seamlessly integrates heterogeneous computing resources widely distributed in edge networks and then provides stronger computing services for users. Therefore, in this article, we propose a malicious node detection scheme in fog computing-based VANETs, where the fog server uses the reputation calculation to score each suspicious node based on the correlation of acquired data and network topology. In our proposed scheme, we build a reputation mechanism to score each suspicious node according to the correlation between outlier detection of acquired data and influence of nodes. Based on our proposed experiments, our proposed scheme can efficiently and effectively detect out malicious vehicle nodes so that fog server can acquire more true data.

In social Internet of Vehicles (SIoVs), vehicles can usually act as data-relaying nodes to forward data. However, vehicle nodes often show their personal and social selfishness in data forwarding (namely vehicle nodes are not willing to forward data), whose selfishness greatly influences the delivery ratio of data forwarding. In this article, we propose a 2-D behavior-marker-based data-forwarding incentive scheme to motivate vehicle nodes to participate in data forwarding in fog-computing-based SIoVs. First, we design a 2-D behavior marker mechanism, which can be used to completely evaluate vehicle nodes. Second, we construct a currency credit-based data-forwarding incentive strategy based on the 2-D marker and the social attributes of vehicle nodes, which is used to deal with vehicular normal behavior, vehicular selfish behavior, and vehicular malicious behavior. Compared with other related schemes, our proposed scheme can completely evaluate the behaviors of vehicle nodes and can further promote the cooperation of data-forwarding between selfish nodes. The experimental results show that our scheme is more efficient and stable in data forwarding in fog-computing-based SIoVs.

Internet of Vehicles (IoV) is a variant of Vehicular Ad-Hoc Network (VANET), it is being developed as an important communication way between vehicles. However, when some traffic messages are collected in IoV, these messages are usually linked to specific identifiable information. Therefore, there exists the privacy-preserving problem of vehicular identities in IoV. On the other hand, if the private information of vehicles is fully protected in IoV, then the true vehicular identities cannot be determined because the transferred messages are not related with the specific information of vehicles. Then it will also lead to more security problems in IoV. Additionally, fog computing seamlessly integrates heterogeneous computing resources widely distributed in edge networks and then provides stronger computing services for users. Therefore, in this paper we propose a decentralized traceable privacy-preserving scheme for vehicular identity in fog computing-based IoV, where our scheme uses multiple fog servers to trace the specific identity and most likely trajectory of a vehicle by the collected data under certain conditions. In our scheme, the true identity of a vehicle is hidden to some related parameters generated by the certificate authority; further the secret sharing scheme is used to hide and trace the true identity of a vehicle. We construct a voting mechanism to generate the most reliable fog server, which is able to calculate the true identity and corresponding trajectory of a vehicle by reconstructing the polynomial based on the secret sharing scheme. Additionally, we analyze that our scheme can satisfy the security requirements, and formally prove that the data collection procedure is secure under the real-or-random model. Also, the experimental results show our scheme is efficient in IoV.

A 360-degree streaming system can provide immersive, interactive, and autonomous experiences surrounding the user by means of viewpoint changes to see different angles of a 360-degree video. However, due to the limited capacity and highly dynamic conditions of cellular networks, high-resolution 360-degree video playback over mobile devices often suffers from playback freezing, and bandwidth waste is inevitably incurred in delivering out-of-view video data. In this paper, a hybrid control scheme is presented for segment-level continuous bitrate selection and tile-level bitrate allocation for 360-degree streaming over mobile devices to increase users’ quality of experience. First, a deep reinforcement learning (RL) method is proposed to predict the segment bitrate and avoid playback freezing. Second, a viewpoint-prediction-map-based cooperative bargaining game theory is proposed for bitrate allocation optimization to choose a suitable bitrate for each tile to reduce unreasonable bandwidth waste. The proposed scheme is compared with state-of-the-art approaches under a wide variety of mobile network conditions with multiple viewpoint traces and 360-degree video contents. The experimental results indicate that the proposed method outperforms the compared state-of-the-art approaches in terms of various experimental objectives on mobile devices.

Presently many searchable encryption schemes have been proposed for cloud and fog computing, which use fog nodes (or fog servers) to partly undertake some computational tasks. However, these related schemes still retain cloud servers to undertake most computational tasks, which result in large communication costs between edge devices and cloud servers. Therefore, in this paper we propose a self-verifiable attribute-based keyword search scheme for distributed data storage (SV-KSDS) in full fog computing, where each decryption operation on the data required by a user must meet the negotiated decryption rule between fog servers. Our SV-KSDS scheme first provides attribute-based distributed data storage among fog servers through the$(w, \sigma)$threshold secret-sharing scheme, where fog servers can provide self-verifiable keyword search and data decryption for terminal users. Compared with the data storage in cloud computing, our scheme extends it to the distributed structure while providing fine-grained access control for distributed data storage through attribute-based encryption. The access control policy of our scheme is constructed on linear secret-sharing scheme, whose security is reduced to the decisional bilinear Diffie-Hellman assumption against chosen-keyword attack and the decisional${q}$-parallel bilinear Diffie-Hellman assumption against chosen-plaintext attack in the standard model. Based on theoretical analysis and practical testing, our SV-KSDS scheme generates less computation and communication costs, which further unloads some computational tasks from terminal users to fog servers so as to reduce computing costs of terminal users.

Social Internet of vehicles (SIoVs) is a combination of vehicular ad-hoc networks (VANETs) and mobile social networks (MSNs). Although social relationships between vehicle nodes are more stable than location changes of vehicle nodes in SIoVs, there always are the problems of data forwarding optimization and adaptability for dynamic networks. In this paper, we propose a dual-mode data forwarding scheme based on interest tags for fog computing-based SIoVs. In the first data forwarding mode, the vehicle nodes calculate and use the cooperation degrees to select the next cooperative forwarding nodes until the data is forwarded to the destination node. In the second data forwarding mode, the RSUs assist the data forwarding of vehicle nodes based on the RSU ranking mechanism of interests, where the fog servers calculate the RSU ranking table of all the interests and the related RSU selects the top-${k}$RSUs to forward the data according to the RSU ranking table. The experimental results show that our proposed scheme is more efficient and stable than other related schemes by the comparisons of delivery ratio, overhead ratio and average hop count.

Internet of vehicles (IoVs) is a variant of vehicular ad hoc network, which provides an efficient communication method for vehicles. However, some traffic messages usually include sensitive identity information, which is easy to bring about the leakage of vehicular identities during data communications. Further, if vehicular identities are fully protected, then it can lead to trusted authority cannot reveal the real identities of malicious vehicles, which incurs more security issues in IoVs. Therefore, in this article, we propose an efficient conditional identity privacy-preserving authentication scheme based on cooperation of multiple fog servers under fog computing-based IoVs, where fog servers are used to verify (authenticate) the legitimacy of vehicles without revealing their real identities. Further, an associated vehicular identity updating mechanism is constructed to solve the problem that some compromised fog servers may leak their stored verification information to pool real vehicular identities. Additionally, a malicious vehicular identity tracing mechanism is proposed to support related fog servers that receive signed false messages can trace the real identities of malicious vehicles. Compared with other related schemes, our scheme further improves its security. Experimental results show our scheme is efficient under fog computing-based IoVs.

In classic network security games, the defender distributes defending resources to the nodes of the network, and the attacker attacks a node, with the objective to maximize the damage caused. Existing models assume that the attack at node u causes damage only at u. However, in many real-world security scenarios, the attack at a node u spreads to the neighbors of u and can cause damage at multiple nodes, e.g., for the outbreak of a virus. In this paper, we consider the network defending problem against contagious attacks. Existing works that study shared resources assume that the resource allocated to a node can be shared or duplicated between neighboring nodes. However, in real world, sharing resource naturally leads to a decrease in defending power of the source node, especially when defending against contagious attacks. To this end, we study the model in which resources allocated to a node can only be transferred to its neighboring nodes, which we refer to as a reallocation process. We show that this more general model is difficult in two aspects: (1) even for a fixed allocation of resources, we show that computing the optimal reallocation is NP-hard; (2) for the case when reallocation is not allowed, we show that computing the optimal allocation (against contagious attack) is also NP-hard. For positive results, we give a mixed integer linear program formulation for the problem and a bi-criteria approximation algorithm. Our experimental results demonstrate that the allocation and reallocation strategies our algorithm computes perform well in terms of minimizing the damage due to contagious attacks.

Spoken Language Understanding (SLU), a core component of the task-oriented dialogue system, expects a shorter inference latency due to the impatience of humans. Non-autoregressive SLU models clearly increase the inference speed but suffer uncoordinated-slot problems caused by the lack of sequential dependency information among each slot chunk. To gap this shortcoming, in this paper, we propose a novel non-autoregressive SLU model named Layered-Refine Transformer, which contains a Slot Label Generation (SLG) task and a Layered Refine Mechanism (LRM). SLG is defined as generating the next slot label with the token sequence and generated slot labels. With SLG, the non-autoregressive model can efficiently obtain dependency information during training and spend no extra time in inference. LRM predicts the preliminary SLU results from Transformer's middle states and utilizes them to guide the final prediction. Experiments on two public datasets indicate that our model significantly improves SLU performance (1.5% on Overall accuracy) while substantially speed up (more than 10 times) the inference process over the state-of-the-art baseline.

Deepfakes is a branch of malicious techniques that transplant a target face to the original one in videos, resulting in serious problems such as infringement of copyright, confusion of information, or even public panic. Previous efforts for Deepfakes videos detection mainly focused on appearance features, which have a risk of being bypassed by sophisticated manipulation, also resulting high model complexity and sensitiveness to noise. Besides, how to mine the temporal features of manipulated videos and exploit them is still an open question. We propose an efficient and robust framework named LRNet for detecting Deepfakes videos through temporal modeling on precise geometric features. A novel calibration module is devised to enhance the precision of geometric features, making it more discriminative, and a two-stream Recurrent Neural Network (RNN) is constructed for sufficient exploitation of temporal features. Compared to previous methods, our proposed method is lighter-weighted and easier to train. Moreover, our method has shown robustness in detecting highly compressed or noise corrupted videos. Our model achieved 0.999 AUC on FaceForensics+ + dataset. Meanwhile, it has a graceful decline in performance (-0.042 AUC) when faced with highly compressed videos.1

Distantly supervised relation extraction is widely used in the construction of knowledge bases due to its high efficiency.However, the automatically obtained instances are of low quality with numerous irrelevant words.In addition, the strong assumption of distant supervision leads to the existence of noisy sentences in the sentence bags.In this paper, we propose a novel Multi-Layer Revision Network (MLRN) which alleviates the effects of wordlevel noise by emphasizing inner-sentence correlations before extracting relevant information within sentences.Then, we devise a balanced and noise-resistant Confidence-based Multi-Instance Learning (CMIL) method to filter out noisy sentences as well as assign proper weights to relevant ones.Extensive experiments on two New York Times (NYT) datasets demonstrate that our approach achieves significant improvements over the baselines.

Spoken language understanding (SLU), which is a core component of the task-oriented dialogue system, has made substantial progress in the research of single-turn dialogue. However, the performance in multi-turn dialogue is still not satisfactory in the sense that the existing multi-turn SLU methods have low portability and compatibility for other single-turn SLU models. Further, existing multi-turn SLU methods do not exploit the historical predicted results when predicting the current utterance, which wastes helpful information. To gap those shortcomings, in this paper, we propose a novel Result-based Portable Framework for SLU (RPFSLU). RPFSLU allows most existing single-turn SLU models to obtain the contextual information from multi-turn dialogues and takes full advantage of predicted results in the dialogue history during the current prediction. Experimental results on the public dataset KVRET have shown that all SLU models in baselines acquire enhancement by RPFSLU on multi-turn SLU tasks.

Summary With the popularity of Sensor‐Cloud, its security issues get more attention from industry and academia. Especially, Sensor‐Cloud underlying network is very vulnerable to internal attacks due to its limitations in computing, storage, and analysis. Most existing trust evaluation mechanisms are proposed to detect internal attack issues from the behavior level. However, there are some special internal attacks in the data level such as hidden data attacks, which are normal in the behavior level but generate malicious data to lead user to make wrong decisions. To detect this type of attacks, we design a fog‐based detection system (FDS), which is based on the trust evaluation mechanism in the behavior level. In this paper, three types of scenes (the redundant data, the parameter curve characteristic, and the data validation) are defined, and three detection schemes are given. Some experiments are conducted, which manifest that FDS has certain advantages in detecting hidden data attacks.

The deep penetration of electric vehicles (EVs) into the transportation section and the associated charging management has yielded a critical issue, namely, how to efficiently store the generated charging records. In this article, we investigate the cost-efficient charging-record storage scheme by exploiting blockchain (BC). Accounting for the operational cost due to the consensus process via the practical Byzantine fault tolerance (PBFT) protocol, we model the associated cost for storing the charging records via an ideal multiblockchain system and formulate a joint optimization of the storage selection (i.e., either storing the charging record locally or selecting one of the BCs for storing the charging record) and server-node allocation for each BC, with the objective of minimizing a systemwise cost. Despite the nature of the complicated mixed binary and integer programming problem, we exploit the decomposition structure and propose a layered algorithm (i.e., the bottom subproblem for determining the optimal storage selection and the top problem for finding the server-node allocation) to solve it. For the bottom subproblem, we exploit the nature of minimum weighted matching of the problem and propose a distributed auction-based algorithm for computing the optimal storage selection. With the optimal solution from the subproblem, we further propose an annealing-based algorithm to determine the server-node allocation for each BC. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our cost-efficient charging-record storage scheme via BC.

Nowadays, IoT systems can better satisfy the service requirements of users with effectively utilizing edge computing resources. Designing an appropriate pricing scheme is critical for users to obtain the optimal computing resources at a reasonable price and for service providers to maximize profits. This problem is complicated with incomplete information. The state-of-the-art solutions focus on the pricing game between a single service provider and users, which ignoring the competition among multiple edge service providers. To address this challenge, we design an edge-intelligent hierarchical dynamic pricing mechanism based on cloud-edge-client collaboration. We introduce an improved double-layer Stackelberg game model to describe the cloud-edge-client collaboration. Technically, we propose a novel pricing prediction algorithm based on double-label Radius K-nearest Neighbors, thereby reducing the number of invalid games to accelerate the game convergence. The experimental results show that our proposed mechanism effectively improves the quality of service for users and realizes the maximum benefit equilibrium for service providers, compared with the traditional pricing scheme. Our proposed mechanism is highly suitable for the IoT applications (e.g., intelligent agriculture or Internet of Vehicles), where there are multiple competing edge service providers for resource allocation.

Distant supervision is widely used to extract relational facts with automatically labeled datasets to reduce high cost of human annotation. However, current distantly supervised methods suffer from the common problems of word-level and sentence-level noises, which come from a large proportion of irrelevant words in a sentence and inaccurate relation labels for numerous sentences. The problems lead to unacceptable precision in relation extraction and are critical for the success of using distant supervision. In this paper, we propose a novel and robust neural approach to deal with both problems by reducing influences of the multi-granularity noises. Three levels of noises from word, sentence until knowledge type are carefully considered in this work. We first initiate a question-answering based relation extractor (QARE) to remove noisy words in a sentence. Then we use multi-focus multi-instance learning (MMIL) to alleviate the effects of sentence-level noise by utilizing wrongly labeled sentences properly. Finally, to enhance our method against all the noises, we initialize parameters in our method with a priori knowledge learned from the relevant task of entity type classification by transfer learning. Extensive experiments on both existing benchmark and an improved larger dataset demonstrate that our proposed approach remarkably achieves new state-of-the-art performance.

Rate control (RC) plays a critical role in the transmission of high-quality video data under certain bandwidth restrictions in High Efficiency Video Coding (HEVC). Most current HEVC RC algorithms based on spatio-temporal information for rate-distortion (R-D) model parameters cannot effectively handle the cases with dynamic video sequences that contain fast moving objects, significant object occlusion or scene changes. In this paper, we propose an RC method based on deep reinforcement learning (DRL) for dynamic video sequences in HEVC to improve the coding efficiency. First, the rate control problem is formulated as a Markov decision process (MDP) problem. Second, with the MDP model, we develop a DRL-based algorithm to find the optimal quantization parameters (QPs) by training a deep neural network. The resulting intelligent agent selects the optimal RC strategy to reduce distortion, buffer and quality fluctuations by observing the current state of the encoder. The asynchronous advantage actor-critic (A3C) method is used to solve the MDP problem. Finally, the proposed DRL-based RC method is implemented in the newest video coding standard. Experimental results show that the proposed method offers substantially enhanced RC accuracy and consistently outperforms HEVC reference software and other state-of-the-art algorithms.

Since heavy flows account for a significant fraction of network traffic, being able to predict heavy flows has benefited many network management applications for mitigating link congestion, scheduling of network capacity, exposing network attacks and so on. Existing machine learning based predictors are largely implemented on the control plane of Software Defined Networking (SDN) paradigm. As a result, frequent communication between the control and data planes can cause unnecessary overhead and additional delay in decision making. In this paper, we presentpHeavy, a machine learning based scheme for predicting heavy flows directly on the programmable data plane, thus eliminating network overhead and latency to SDN controller. Considering the scarce memory and limited computation capability in the programmable data plane,pHeavyincludes a packet processing pipeline which deploys pre-trained decision tree models for in-network prediction. We have implementedpHeavyin both bmv2 software switch and P4 hardware switch (i.e., Barefoot Tofino). Evaluation results demonstrate thatpHeavyhas achieved 85% and 98% accuracy after receiving the first 5 and 20 packets of a flow respectively, while being able to reduce the size of decision tree by 5.4x on average. More importantly,pHeavycan predict heavy flows at line rate on the P4 hardware switch.

Sink mobility is a significant technique to improve the performance of wireless sensor networks (WSNs). Generally a mobile sink visits several rendezvous points (RPs), forming a trip tour for data collection. However, the low movement speeds of mobile sinks tend to incur serious data delivery delays. In this article, we propose a quick convex hull-based rendezvous planning (QCHBRP) scheme, which aims to not only achieve full connectivity for disjoint WSNs but also construct a shorter trip tour and minimize the data delivery latency accordingly. The trajectory formation of the mobile sink is based on a path skeleton, i.e., a near-convex hull, which is created by the quick determination of several special locations as RPs. The benefits of QCHBRP are threefold. First, it is especially designed for disjoint WSNs where sensor nodes are deployed in multiple isolated segments and the network connectivity is lost in advance. Second, it is suitable for delay-harsh applications which require short paths of the mobile sink. Third, it is of much lower computational complexity compared with existing methods. The extensive analysis and experiments validate the effectiveness and advantages of this new scheme in terms of connectivity cost and data delivery delay.

Distantly supervised relation extraction has been widely applied in knowledge base construction due to its less requirement of human efforts.However, the automatically established training datasets in distant supervision contain low-quality instances with noisy words and overlapped relations, introducing great challenges to the accurate extraction of relations.To address this problem, we propose a novel Regularized Attentive Capsule Network (RA-CapNet) to better identify highly overlapped relations in each informal sentence.To discover multiple relation features in an instance, we embed multi-head attention into the capsule network as the low-level capsules, where the subtraction of two entities acts as a new form of relation query to select salient features regardless of their positions.To further discriminate overlapped relation features, we devise disagreement regularization to explicitly encourage the diversity among both multiple attention heads and low-level capsules.Extensive experiments conducted on widely used datasets show that our model achieves significant improvements in relation extraction.

The dependent task offloading problem for one single job in edge computing (EC) has drawn attention widely. Unlike most existing approaches that only focus on a single job, we aim to solve the dependent task offloading problem for multiple jobs, which is more general in the real world. To solve this problem, we propose a deep reinforcement learning (DRL) based multi-job dependent task offloading algorithm. Specifically, 1) we model edge nodes, jobs, and tasks in a resource-limited EC scenario, where the dependent tasks of multiple jobs are offloaded to the nodes to be processed. Then we model the task offloading decision as a Markov decision process (MDP) problem to minimize the transmission cost and computation cost. 2) To represent the state space of MDP and to accelerate decision-making in EC, we propose a DRL-based algorithm with the aid of graph convolutional network (GCN) to extract the dependency information of different tasks and then improve the action selection process. 3) We conduct experiments with real-world trace, demonstrating our algorithm outperforms the baseline algorithms 13.78% on average in regarding to offloading cost.

With the development of deep convolutional neural networks (CNNs), the object detection accuracy has been greatly improved. But the performance of small object detection is still far from satisfactory, mainly because small objects are so tiny that the information contained in the feature map is limited. Existing methods focus on improving classification accuracy but still suffer from the limitation of bounding box prediction. To solve this issue, we propose a detection framework by generative and discriminative learning. First, a reconstruction generator network is designed to reconstruct the mapping from low frequency to high frequency for anchor box prediction. Then, a detector module extracts the regions of interest (ROIs) from generated results and implements a RoI-Head to predict object category and refine bounding box. In order to guide the reconstructed image related to the corresponding one, a discriminator module is adopted to tell from the generated result and the original image. Extensive evaluations on the challenging MS-COCO dataset demonstrate that our model outperforms most state-of-the-art models in detecting small objects, especially the reconstruction module improves the average precision for small object (APs) by 7.7%.

Internet of Medical Things (IoMT), intended for real-time health monitoring, are generating quantity of health data such as electrocardiogram, oxygen saturation, and blood pressure every second. The captured data should be processed and analyzed in a delay sensitive way which is vital to the survival rate for cardiovascular and cerebrovascular diseases. In this regard, Unmanned Aerial Vehicles (UAVs) have already demonstrated the enormous potentials. To begin with, due to better line-of-sight, wider communication and more flexible on-demand deployment, UAVs can realize seamless wireless connection to IoMT. Furthermore, UAVs can act as fog nodes to provision services for IoMTs such as task performing and data analysis. We in this paper focus on a sub-problem, i.e., the placement of UAVs over the serving area when they function as fog nodes. In the airborne fog computing, the placement of UAVs has an important influence on energy consumption and exploration area, let alone the communication coverage of the personal health devices on the ground. Therefore, we in this paper propose a particle swarm optimization (PSO) based algorithm to optimize the UAV placement over the serving area for the IoMT devices. We have conducted extensive simulations to evaluate it. The results show that our approach can significantly reduce the number of UAVs needed to deploy while considering the communication coverage and other factors.

Effective long-term predictions have been increasingly demanded in urban-wise data mining systems. Many practical applications, such as accident prevention and resource pre-allocation, require an extended period for preparation. However, challenges come as long-term prediction is highly error-sensitive, which becomes more critical when predicting urban-wise phenomena with complicated and dynamic spatial-temporal correlation. Specifically, since the amount of valuable correlation is limited, enormous irrelevant features introduce noises that trigger increased prediction errors. Besides, after each time step, the errors can traverse through the correlations and reach the spatial-temporal positions in every future prediction, leading to significant error propagation. To address these issues, we propose a Dynamic Switch-Attention Network (DSAN) with a novel Multi-Space Attention (MSA) mechanism that measures the correlations between inputs and outputs explicitly. To filter out irrelevant noises and alleviate the error propagation, DSAN dynamically extracts valuable information by applying self-attention over the noisy input and bridges each output directly to the purified inputs via implementing a switch-attention mechanism. Through extensive experiments on two spatial-temporal prediction tasks, we demonstrate the superior advantage of DSAN in both short-term and long-term predictions. The source code can be obtained from https://github.com/hxstarklin/DSAN.

Mobile edge computing (MEC) has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for terminal devices. In this paper, we investigate multi-user computation off loading in MEC and propose a joint optimization of off loading decisions, bandwidth and computation-resource allocations, with the objective of minimizing the total latency for completing all users' tasks. Due to the non-convexity of the formulated joint optimization problem, we identify its layer structure and decompose it into two problems, i.e·, a sub-problem and a top-problem. For the sub-problem, we propose a bisection-search based algorithm to efficiently find the optimal off loading solutions under a given feasible top-problem solution. Then, we use a linear-search based algorithm to obtain the optimal solution of the top-problem. Numerical results are provided to validate our proposed algorithm for minimizing the total latency in MEC-based multi-user computation off loading. We also demonstrate the advantage of our proposed algorithm in comparison with the conventional multi-user computation off loading schemes.

Abstract Fog computing has become an emerging environment that provides data storage, computing and some other services on the edge of network. It not only can acquire data from terminal devices, but also can provide computing services to users by opening computing resources. Compared with cloud computing, fog devices can collaborate to provide users with powerful computing services through resource allocation. However, as many of fog devices are not monitored, there are some security problems. For example, since fog server processes and maintains user information, device information, task parameters and so on, fog server is easy to perform illegal resource allocation for extra benefits. In this paper, we propose a secure computing resource allocation framework for open fog computing. In our scheme, the fog server is responsible for processing computing requests and resource allocations, and the cloud audit center is responsible for auditing the behaviors of the fog servers and fog nodes. Based on the proposed security framework, our proposed scheme can resist the attack of single malicious node and the collusion attack of fog server and computing devices. Furthermore, the experiments show our proposed scheme is efficient. For example, when the number of initial idle service devices is 40, the rejection rate of allocated tasks is 10% and the total number of sub-tasks is changed from 150 to 200, the total allocation time of our scheme is only changed from 15 ms to 25 ms; additionally, when the task of 5000 order matrix multiplication is tested on 10 service devices, the total computing time of our scheme is $\sim$250 s, which is better than that of single computer (where single computer needs more than 1500 s). Therefore, our proposed scheme has obvious advantages when it faces some tasks that require more computational cost, such as complex scientific computing, distributed massive data query, distributed image processing and so on.

Driver fatigue is a leading factor in road accidents that can cause severe fatalities. Existing fatigue detection works focus on vision and electroencephalography (EEG) based means of detection. However, vision-based approaches suffer from view-blocking or vision distortion problems and EEG-based systems are intrusive, and the drivers have to use/wear the devices with inconvenience or additional costs. In our work, we propose a novel Wi-Fi signals based fatigue detection approach, called WiFind to overcome the drawbacks as associated with the current works. WiFind is simple and (wearable) device-free. It can detect the fatigue symptoms in the vehicle without relying on any visual image or video. By applying self-adaptive method, it can recognize the body features of drivers in multiple modes. It applies Hilbert-Huang transform (HHT) based pattern extract method results in accuracy increase in motion detection mode. WiFind can be easily deployed in a commodity Wi-Fi infrastructure, and we have evaluated its performance in real driving environments. The experimental results have shown that WiFind can achieve the recognition accuracy of 89.6 percent in a single driver scenario.

Fog computing needs to seamlessly integrate heterogeneous computing resources widely distributed in edge networks, and then, it can provide unified resources and services for users. However, due to a large number of fog nodes, there are still many security problems in the fog computing process. For example, when fog servers make resource allocation between users' tasks and fog nodes, some fog nodes can falsely claim that they have more resources than their actual ability, so as to get more task processing qualifications. Thus, it will greatly damage the interests of users and affect the quality of service. In this article, we propose a resource allocation scheme for community-based fog computing based on a reputation mechanism. When fog network provides computing services for users, we use a reputation mechanism to enable users to obtain reliable resources in fog computing. In our proposed scheme, a user first submits his/her task request to the community-based fog network, and then, the fog server makes a reliable resource allocation process based on multiple-layer communities and reputation calculation. Based on our experiments, our scheme enables users to obtain reliable resources and improves the service quality of computing resources in fog computing.

As an enabler for smart industrial Internet of Things (IoT), sensor cloud facilitates data collection, processing, analysis, storage, and sharing on demand. However, compromised or malicious sensor nodes may cause the collected data to be invalid or even endanger the normal operation of an entire IoT system. Therefore, designing an effective mechanism to ensure the trustworthiness of sensor nodes is a critical issue. However, existing cloud computing models cannot provide direct and effective management for the sensor nodes. Meanwhile, the insufficient computation and storage ability of sensor nodes makes them incapable of performing complex intelligent algorithms. To this end, mobile edge nodes with relatively strong computation and storage ability are exploited to provide intelligent trust evaluation and management for sensor nodes. In this article, a mobile edge computing-based intelligent trust evaluation scheme is proposed to comprehensively evaluate the trustworthiness of sensor nodes using probabilistic graphical model. The proposed mechanism evaluates the trustworthiness of sensor nodes from data collection and communication behavior. Moreover, the moving path for the edge nodes is scheduled to improve the probability of direct trust evaluation and decrease the moving distance. An approximation algorithm with provable performance is designed. Extensive experiments validate that our method can effectively ensure the trustworthiness of sensor nodes and decrease the energy consumption.

The proliferation of advanced underwater technology and the emergence of various cloud services promote the horizon of cloud-based underwater acoustic sensor network (UASN). Sending end data to cloud for analysis is becoming a prominent trend, driving cloud computing as an indispensable computing paradigm. However, UASN bears tremendous burdens with respect to data collection from end to cloud, such as large transmission power consumption and high delay, which makes it difficult to meet the delay-sensitive and context-aware service requirements by using cloud computing alone. To this end, a two-level bidirectional data prediction model for end-edge-cloud orchestration is proposed in this article. The mobility and computing ability of edge elements are exploited to analyze and collect data. Edge elements predict the future data based on historical information and trend to decrease acoustic communication. Moreover, a data collection protocol with mobile edge elements is designed. With this protocol, computing paradigms are shifted from centralized cloud to distributed edge, and the differentiated capability of heterogeneous devices is exploited. After extensive experiments, the results show that the data collection cost is dramatically decreased while the bandwidth utilization is increased, which is critical for underwater acoustic communication. The proposed method and protocol strike a good balance between data accuracy and energy consumption for the new end-edge-cloud orchestrated system.

In this paper, we propose a just noticeable distortion (JND)-based perceptual rate control method for high efficiency video coding (HEVC). First, the JND factor of a coding unit has been mathematically shown to be an approximation of the average pixel-level JND weight, which means that it can also be used as a weight for bitrate allocation. Second, rate-distortion (R-D) modelling is conducted based on the JND factor. Finally, the proposed R-D model is integrated into an existing rate control framework to improve the coding efficiency, and the proposed algorithm is implemented in the newest video coding standard. As the experimental results reveal, compared with HEVC reference software, our algorithm achieves significantly improved coding performance, subjective coding quality and bitrate accuracy.

Fog computing is mainly used to process a large amount of data produced by terminal devices. As fog nodes are the closest acquirers to the terminal devices, the processed data may be tampered with or illegally captured by some malicious nodes while the data is transferred or aggregated. When some applications need to require real-time process with high security, cloud service may sample some data from fog service to check final results. In this paper, we propose a secure data query framework for cloud and fog computing. We use cloud service to check queried data from fog network when fog network provides queried data to users. In the framework, cloud server pre-designates some data aggregation topology trees to fog network, and then fog network may acquire related data from fog nodes according to one of the pre-designated data aggregation trees. Additionally, some fog nodes are assigned as sampled nodes that can feed back related data to cloud server. Based on the security requirements of fog computing, we analyze the security of our proposed framework. Our framework not only guarantees the reliability of required data but also effectively protects data against man-in-the-middle attack, single node attack and collusion attack of malicious users. Also, the experiments show our framework is effective and efficient.

With the increasing number of users in blockchain-based cryptocurrencies, the public has raised the demand for transaction throughput, and many protocols are designed to improve the throughput following the Nakamoto consensus. Although astonishing progress has been made in the on-chain throughput improvement, high throughput makes the blockchains suffer from the increasing blockchain size, hard forks, and possible attacks. In this work, we propose a quantitative model to describe and analyze the Nakamoto consensus. We then design a robust scheme named Ursa to reduce storage requirements and to reduce the forks by automatically adjusting block size according to users’ needs.

Existing works adopt the Edge-Oriented Resource Allocation (EORA) scheme, in which edge nodes cache services and schedule user requests to distribute workloads over cloud and edge nodes, so as to achieve high-quality services and low latency. Unfortunately, EORA does not fully take into account the fact that service providers are sometimes independent from the edge operators with their own objectives. To deal with the conflict and cooperation between service providers and edge nodes, we devise a service-oriented resource allocation (SORA) scheme, where edge nodes and service providers adjust their resource allocations to provide requested services. We first prove that such resource allocation problem is NP-hard. We then propose a three-sided cyclic game (3CG) involving users, edge nodes, and service providers who make their individual decisions by choosing respectively high-quality services, high-value users, and cost-effective edge nodes for service deployment. Based on 3CG, we prove the existence and approximation ratio of pure-strategy Nash equilibriums (NEs). We also develop both centralized and distributed approximate algorithms for resource allocation. Finally, extensive experimental results validate the effectiveness and convergence of the proposed algorithms.

To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works.

Comprehensive document encoding and salient information selection are two major difficulties for generating summaries with adequate salient information.To tackle the above difficulties, we propose a Transformerbased encoder-decoder framework with two novel extensions for abstractive document summarization.Specifically, (1) to encode the documents comprehensively, we design a focus-attention mechanism and incorporate it into the encoder.This mechanism models a Gaussian focal bias on attention scores to enhance the perception of local context, which contributes to producing salient and informative summaries.(2) To distinguish salient information precisely, we design an independent saliency-selection network which manages the information flow from encoder to decoder.This network effectively reduces the influences of secondary information on the generated summaries.Experimental results on the popular CNN/Daily Mail benchmark demonstrate that our model outperforms other state-of-the-art baselines on the ROUGE metrics.

Nowadays, time-sync comment (TSC), a new form of interactive comments, has become increasingly popular on Chinese video websites. By posting TSCs, people can easily express their feelings and exchange their opinions with others when watching online videos. However, some spoilers appear among the TSCs. These spoilers reveal crucial plots in videos that ruin people's surprise when they first watch the video. In this paper, we proposed a novel Similarity-Based Network with Interactive Variance Attention (SBN-IVA) to classify comments as spoilers or not. In this framework, we firstly extract textual features of TSCs through the word-level attentive encoder. We design Similarity-Based Network (SBN) to acquire neighbor and keyframe similarity according to semantic similarity and timestamps of TSCs. Then, we implement Interactive Variance Attention (IVA) to eliminate the impact of noise comments. Finally, we obtain the likelihood of spoiler based on the difference between the neighbor and keyframe similarity. Experiments show SBN-IVA is on average 11.2% higher than the state-of-the-art method on F1-score in baselines.

Managing edge resources is one of key issues in edge computing. Recent works of resource allocation in edge computing focus on service caching and request scheduling on edge nodes by distributing tasks over cloud and edge nodes, so as to achieve high-quality services and low latency. Unfortunately, most schemes do not pay enough attention on service providers which also have their own resources. To serve users, service providers cooperate with edge nodes and are sometimes independent of the edge operators with their own objectives. To deal with the cooperation and conflict among users, service providers and edge nodes, we propose a cyclic selection model to depict the resource allocation among users, edge nodes and service providers where they cooperate for completing user requests and compete for their own interest. Such resource allocation problem can be formulated as a non-linear integer programming which is very difficult to solve. Thus, we propose a three-sided cyclic game (3CG) involving users, edge nodes, and service providers who make their decisions: users select preferred services, service providers select cost-effective edge nodes, edge nodes select high-value users. 3CG is proved to have pure-strategy Nash equilibriums and an approximation ratio. We develop central and distributed approximate algorithms for resource allocation. The evaluation results of 3CG show the effectiveness and efficiency of the proposed algorithms.

Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations. However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Experiments show that ITF-HEA is on average 3.78% higher than the state-of-the-art method upon F1-score in baselines.

The Legal Judgment Prediction (LJP) is to determine judgment results based on the fact descriptions of the cases. LJP usually consists of multiple subtasks, such as applicable law articles prediction, charges prediction, and the term of the penalty prediction. These multiple subtasks have topological dependencies, the results of which affect and verify each other. However, existing methods use dependencies of results among multiple subtasks inefficiently. Moreover, for cases with similar descriptions but different penalties, current methods cannot predict accurately because the word collocation information is ignored. In this paper, we propose a Multi-Perspective Bi-Feedback Network with the Word Collocation Attention mechanism based on the topology structure among subtasks. Specifically, we design a multi-perspective forward prediction and backward verification framework to utilize result dependencies among multiple subtasks effectively. To distinguish cases with similar descriptions but different penalties, we integrate word collocations features of fact descriptions into the network via an attention mechanism. The experimental results show our model achieves significant improvements over baselines on all prediction tasks.

In the context of Edge Computing (EC) and Internet of Things (IoT), numerous tasks are offloaded from mobile users and sensor devices to edge nodes for further processing to reduce delay and solve the problem of insufficient local computation resources. These tasks can be mainly divided into delay-sensitive and computation-oriented tasks. The former tasks depend on the service provided by the container, while the latter tasks are submitted as a batch with task dependencies. Considering the heterogeneity of edge nodes, joint task scheduling can effectively improve resource utilization. However, relatively few researches consider the different characteristics of tasks like container constraints and task dependencies in joint task scheduling in EC. In order to fill in this gap, we propose a deep deterministic policy gradient (DDPG) based online joint task scheduling (OJTS) algorithm. Specifically, 1) We first model the problem of joint scheduling of delay-sensitive and computation-oriented tasks in resource-constrained EC scenario with the goals of maximizing system utility and minimizing system cost (weighted sum of the number and duration of unfinished tasks). 2) Then, we propose a deep reinforcement learning (DRL) algorithm to solve the above problem and make appropriate adjustments to the original network structure according to the scheduling decision. 3) Through validation on real-world trace, OJTS can improve the system utility by 26.0% and overall reward by 51.2% compared with baselines and meet real-time decision-making requirements.

Internet of Things (IoT) can be used to promote many advanced applications by utilizing the sensed data collected from various settings. To reduce the energy consumption of IoT devices, and to extend the lifetime of network, the sensed data are usually compressed before their transmission through compressed sensing theory. By reconstructing the sensed data at the edge of network with more resourceful devices, such as laptops and servers, the intensive computation and energy consumption of the IoT nodes could be effectively offloaded. However, most of the existing data collection schemes are limited in their scalability, because the unified data reconstruction models of them are not suitable for large-scale surveillance scenarios. In our proposed scheme, the whole network is first partitioned into a number of data correlated clusters based on spatial correlation. Then, a data collection tree is built to collect the compressed data in a hybrid mode. Finally, the data reconstruction problem is modelled as a group sparse problem and solved through using an alternating direction method of multiplier-based algorithm. The performance of data communication and reconstruction of the proposed scheme is evaluated through experiments with real data set. The experimental results show that the proposed scheme can indeed lower the amount of data transmission, prolong the network life, and achieve a higher level of accuracy in data collection compared to existing data collection schemes.

With the development of Internet of Things (IoT) technologies, increasingly many devices are connected, and large amounts of data are produced. By offloading the computing-intensive tasks to the edge devices, cloud-based storage technology has become the mainstream. However, if the end IoT devices send all of their data to the cloud, then data privacy becomes a great issue. In this paper, we propose a new architecture for data synchronization based on fog computing. By offloading part of computing and storage work to the fog servers, the data privacy can be guaranteed. Moreover, to decrease the communication cost and reduce the latency, we design a differential synchronization algorithm. Furthermore, we extend the method by introducing Reed-Solomon code for security consideration. We prove that our architecture and algorithm really have better performance than traditional cloud-based solutions in terms of both efficiency and security through a series of experiments.

Special Issue on Intelligent Edge Computing for Cyber Physical and Cloud SystemsCyber Physical Systems (CPS) and Cloud Computing have received tremendous research interest and efforts from both academia and industry.Cloud computing extends the computing and storage ability of CPS and leads to a new paradigm-Cyber Physical and Cloud Systems (CPCS), which is a product of combining CPS and Cloud Computing together.It enables a new breed of applications and services, such as industrial process control, video surveillance, structural health monitoring, and intelligent agriculture, and can fundamentally change the way that people interact with the physical world.However, CPCS face many important challenges.First, the Cloud can neither manage CPS devices directly nor satisfy requirements of real-time.Second, communication bottleneck exists between CPS and the Cloud.Third, new security challenges need to be overcome to accelerate the development of these integrated applications.In particular, edge computing, acting as a new computing scheme, is a promising technology to address these challenges.It extends the Cloud Computing paradigm to the edge of the network.For example, edge computing devices, which are capable of intelligent computing, can reduce the network latency by enabling computation and storage capacity at the edge network.These so-called edge devices can bridge the gap between CPS and Cloud.The intelligent computing and storage on edge devices offer the potential to solve the communication problem, real-time problem, and security problem.The accepted papers represent the urgent needs to be considered in developing an intelligent computing for edge devices and to fill the gap between CPS and Cloud.Moreover, the outcome of this special section exhibits the latest research achievements and state-of-art research results to solve intelligent computing issues for CPCS. INTELLIGENT COMPUTING FOR EDGE DEVICES IN CYBER PHYSICAL AND CLOUD SYSTEMSThrough a peer-review process, we have accepted 10 submissions, and each selected article has received at least two rounds of rigorous reviews.The accepted articles represent activities in areas around the world and propose various theoretical research results and applications on applying Intelligent Edge Computing for Cyber Physical and Cloud Systems in industrial informatics.A brief introduction is provided to each of the articles as follows:The first three articles introduce intelligent computing for edge devices in Cyber Physical and Cloud Systems.In "Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System," Zhaolong Ning et al. construct an intelligent offloading system for vehicular edge computing in the development of smart vehicles, bringing a comfortable and safe environment to drivers and passengers.In this research, the author has investigated two-sided matching scheme and a deep reinforcement learning to solve sub-optimization problems.Numerical results demonstrate that the matching algorithm in the first module can reach 95% of the exhaustive algorithm in different network scenarios and decrease the execution time by more than 90%.For the

Time-sync comments (TSCs) reveal a new way of extracting the online video tags. However, such TSCs have lots of noises due to users’ diverse comments, introducing great challenges for accurate and fast video tag extractions. In this article, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). Specifically, we first generate corresponding semantic association graph (SAG) using semantic similarities and timestamps of the TSCs. Second, we propose two graph cluster algorithms, i.e., dialogue-based algorithm and topic center-based algorithm, to deal with the videos with different density of comments. Third, we design a graph iteration algorithm to assign the weight to each comment based on the degrees of the clustered subgraphs, which can differentiate the meaningful comments from the noises. Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF). In this way, the video tags are extracted automatically in an unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments; while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122 MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density comments. It has a better performance than the state-of-the-art unsupervised algorithms in both F1-score and MAP.

TCP congestion control is a vital component for the latency of Web services. In practice, a single congestion control mechanism is often used to handle all TCP connections on a Web server, e.g., Cubic for Linux by default. Considering complex and ever-changing networking environment, the default congestion control may not always be the most suitable one. Adjusting congestion control to meet different networking scenarios usually requires modification of TCP stacks on a server. This is difficult, if not impossible, due to various operating system and application configurations on production servers. In this paper, we propose Mystique, a light-weight, flexible, and dynamic congestion control switching scheme that allows network or server administrators to deploy any congestion control schemes transparently without modifying existing TCP stacks on servers. We have implemented Mystique in Open vSwitch (OVS) and conducted extensive test-bed experiments in both public and private cloud environments. Experiment results have demonstrated that Mystique is able to effectively adapt to varying network conditions, and can always employ the most suitable congestion control for each TCP connection. More specifically, Mystique can significantly reduce latency by 18.13% on average when compared with individual congestion controls.

Today's data center operators deploy network policies in both physical (e.g., middleboxes, switches) and virtualized (e.g., virtual machines on general purpose servers) network function boxes (NFBs), which reside in different points of the network, to exploit their efficiency and agility respectively. Nevertheless, such heterogeneity has resulted in a great number of independent network nodes that can dynamically generate and implement inconsistent and conflicting network policies, making correct policy implementation a difficult problem to solve. Since these nodes have varying capabilities, services running atop are also faced with profound performance unpredictability. In this paper, we propose a Heterogeneous netwOrk Policy Enforcement (HOPE) scheme to overcome these challenges. HOPE guarantees that network functions (NFs) that implement a policy chain are optimally placed onto heterogeneous NFBs such that the network cost of the policy is minimized. We first experimentally demonstrate that the processing capacity of NFBs is the dominant performance factor. This observation is then used to formulate the Heterogeneous Network Policy Placement problem, which is shown to be NP-Hard. To solve the problem efficiently, an online algorithm is proposed. Our experimental results demonstrate that HOPE achieves the same optimality as Branch-and-bound optimization but is 3 orders of magnitude more efficient.

Fog Computing (FC) is a flexible architecture to support distributed domain-specific applications with cloud-like quality of service. However, current FC still lacks the mobility support mechanism when facing many mobile users with diversified application quality requirements. Such mobility support mechanism can be critical such as in the industrial internet where human, products, and devices are moveable. To fill in such gaps, in this paper we propose novel container migration algorithms and architecture to support mobility tasks with various application requirements. Our algorithms are realized from three aspects: 1) We consider mobile application tasks can be hosted in a container of a corresponding fog node that can be migrated, taking the communication delay and computational power consumption into consideration; 2) We further model such container migration strategy as multiple dimensional Markov Decision Process (MDP) spaces. To effectively reduce the large MDP spaces, efficient deep reinforcement learning algorithms are devised to achieve fast decision-making and 3) We implement the model and algorithms as a container migration prototype system and test its feasibility and performance. Extensive experiments show that our strategy outperforms the existing baseline approaches 2.9, 48.5 and 58.4 percent on average in terms of delay, power consumption, and migration cost, respectively.

The development of cloud computing pours great vitality into traditional wireless sensor networks (WSNs). The integration of WSNs and cloud computing has received a lot of attention from both academia and industry. However, collecting data from WSNs to cloud is not sustainable. Due to the weak communication ability of WSNs, uploading big sensed data to the cloud within the limited time becomes a bottleneck. Moreover, the limited power of sensor usually results in a short lifetime of WSNs. To solve these problems, we propose to use multiple mobile sinks (MSs) to help with data collection. We formulate a new problem which focuses on collecting data from WSNs to cloud within a limited time and this problem is proved to be NP-hard. To reduce the delivery latency caused by unreasonable task allocation, a time adaptive schedule algorithm (TASA) for data collection via multiple MSs is designed, with several provable properties. In TASA, a non-overlapping and adjustable trajectory is projected for each MS. In addition, a minimum cost spanning tree (MST) based routing method is designed to save the transmission cost. We conduct extensive simulations to evaluate the performance of the proposed algorithm. The results show that the TASA can collect the data from WSNs to Cloud within the limited latency and optimize the energy consumption, which makes the sensor-cloud sustainable.

Selfish mining is a well-known mining attack strategy discovered by Eyal and Sirer in 2014. After that, the attackers' strategy has been further discussed by many other works, which analyze the strategy and behavior of a single attacker. The extension of the strategy research is greatly restricted by the assumption that there is only one attacker in the blockchain network, since, in many cases, a proof of work blockchain has multiple attackers. The attackers can be independent of others instead of sharing information and attacking the blockchain as a whole. In this paper, we will establish a new model to analyze the miners' behavior in a proof of work blockchain with multiple attackers. Based on our model, we extend the attackers' strategy by proposing a new strategy set publish-n. Meanwhile, we will also review other attacking strategies such as selfish mining and stubborn mining in our model to explore whether these strategies work or not when there are multiple attackers. The performances of different strategies are compared using relative stale block rate of the attackers. In a proof of work blockchain model with two attackers, strategy publish-n can beat selfish mining by up to 26.3%.

Extracting relations is critical for knowledge base completion and construction in which distant supervised methods are widely used to extract relational facts automatically with the existing knowledge bases.However, the automatically constructed datasets comprise amounts of low-quality sentences containing noisy words, which is neglected by current distant supervised methods resulting in unacceptable precisions.To mitigate this problem, we propose a novel word-level distant supervised approach for relation extraction.We first build Sub-Tree Parse (STP) to remove noisy words that are irrelevant to relations.Then we construct a neural network inputting the subtree while applying the entity-wise attention to identify the important semantic features of relational words in each instance.To make our model more robust against noisy words, we initialize our network with a priori knowledge learned from the relevant task of entity classification by transfer learning.We conduct extensive experiments using the corpora of New York Times (NYT) and Freebase.Experiments show that our approach is effective and improves the area of Precision/Recall (PR) from 0.35 to 0.39 over the state-of-the-art work.

VANET is emerging as a new landscape of mobile ad-hoc networks to make the connected vehicles' projects a reality. The continuous connectivity in VANET is a huge challenge caused by the extremely dynamic network topology and the highly variable number of mobile nodes. Moreover, this specific network faces many challenges to define reliable protocols and mechanisms like rate adaptation schemes. The performance of different VANET applications like traffic management and multimedia delivery depends on the network throughput and the success ratio these networks can provide. Rate adaptation is the key method to maximize the throughput and to avoid performance network degradation by estimating the current channel qualities and deciding the best bitrate for the next frames. Although numerous data rate adaptation mechanisms are available for 802.11 WLANs standards, there is little work dedicated to the IEEE 802.11p rate adaptation in vehicular networks. In this paper, we evaluate and compare the available 802.11 wireless networks rate adaptation mechanisms in various vehicular scenarios to analyze their performance and understand their behavior under different conditions. Five mechanisms were selected to be compared using NS-3 simulations: Minstrel, AARF-CD, CARA, Onoe, and Ideal algorithm. The performance results show that Minstrel is the most stable algorithm since it performs better in dynamic and dense environments when compared with the other algorithms.

Fog Computing (FC - similarly edge computing) as new computing paradigm can support distributed domain-specific or area-specific applications with cloud-like quality of service (QoS). This promising paradigm thus can find its wide applications in various industrial scenarios and smart cities in which the resource requirements will be divided into peak-hour or non-peak-hour. To deal with such features of applications, a flexible resource allocation approach based on pricing model can be critical for the success of such paradigm. To the best of our knowledge, we have not seen such pricing based resource allocation approach ever been reported for FC scenarios. In this paper, we propose a novel pricing based dynamic resource allocation model through overbooking mechanism, and it is realized through three steps: 1) According to different QoS requirements of user tasks, methods of on-demand billing, daily billing, and auction billing are designed, in which we allow the resource to be overbooked; 2) For auction billing, we design an auction approach including pricing rule and winner determination rule. We prove that our auction approach guarantees individual rationality, computational efficiency, and truthfulness. 3) To overbook as much resource as possible with a high degree of QoS satisfaction of on-demand and daily billing, we overbook the resource based on a resource utilization prediction using neural network and service level agreement violation feedback. In the end, we validate the mechanism with real-world data trace. Experimental results show that our auction approach achieves desirable properties, and our dynamic resource overbooking mechanism maximizes the profit of nodes with a high degree of QoS satisfaction of on-demand and daily billing and a high resource utilization prediction accuracy rate.

Shonosuke Ishiwatari, Jingtao Yao, Shujie Liu, Mu Li, Ming Zhou, Naoki Yoshinaga, Masaru Kitsuregawa, Weijia Jia. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.

Driver fatigue is a leading factor in road accidents that can cause serious fatalities. Existing fatigue detection works focus on vision and electroencephalography(EEG) based means of detection. However, vision-based approaches suffer from view-blocking or vision distortion problems and EEG-based systems are intrusive and the drivers have to use/wear the devices with inconvenience or additional costs. In this paper, we propose a novel Wi-Fi signals based fatigue detection approach, called WiFind to overcome the drawbacks as associated with the current works. WiFind is simple and (wearable) device-free. It can detect the fatigue symptoms in the vehicle without relying on any visual image or video. By applying peak recognition and SVM-based method, it can recognize driver fatigue according to the body features of drivers both in breath mode and motion mode. We deploy WiFind in commodity Wi-Fi infrastructure and evaluate its performance in real driving environments. The results show WiFind can achieve a recognition accuracy of 82.1% in a single driver scenario.

Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). SW-IDF first generates corresponding semantic association graph (SAG) using semantic similarities and timestamps of the time-sync comments. Then it clusters the comments into sub-graphs of different topics and assigns weight to each comment based on SAG. This can clearly differentiate the meaningful comments with the noises. In this way, the noises can be identified, and effectively eliminated. Extensive experiments have shown that SW-IDF can achieve 0.3045 precision and 0.6530 recall in high-density comments; 0.3800 precision and 0.4460 recall in low-density comments. It is the best performance among the existing unsupervised algorithms.

With the emergence of network function virtualization, data center start to deploy a variety of network function boxes (NFBs) in both physical and virtual form factors in order to combines inherent efficiency offered by physical NFBs with the agility and flexibility of virtual ones. However, existing schemes are limited to exclusively consider physical or virtual NFBs, which may reduce the performance efficiency of services running atop. In this paper, we propose a Heterogeneous NetwOrk Policy Enforcement scheme (HOPE) to overcome these challenges. An efficient algorithm that can closely approximate optimal latency-wise NF service chaining is proposed. The experimental results have also shown that HOPE can outperform greedy algorithm by 25% in terms of network latency and is 56× more efficient than naive depth-first search algorithm.

For better service provision and utilization of renewable energy, Internet service providers have already built their data centers in geographically distributed locations. These companies balance quality of service (QoS) revenue and power consumption by migrating virtual machines (VMs) and allocating the resource of servers adaptively. However, existing approaches model the QoS revenue by service-level agreement (SLA) violation, and ignore the network communication cost and immigration time. In this paper, we propose a reinforcement learning-based adaptive resource management algorithm, which aims to get the balance between QoS revenue and power consumption. Our algorithm does not need to assume prior distribution of resource requirements, and is robust in actual workload. It outperforms other existing approaches in three aspects: (1) The QoS revenue is directly modeled by differentiated revenue of different tasks, instead of using SLA violation. (2) For geodistributed data centers, the time spent on VM migration and network communication cost are taken into consideration. (3) The information storage and random action selection of reinforcement learning algorithms are optimized for rapid decision making. Experiments show that our proposed algorithm is more robust than the existing algorithms. Besides, the power consumption of our algorithm is around 13.3% and 9.6% better than the existing algorithms in non-differentiated and differentiated services.

With the explosive growth of users in mobile carrier, telecommunication fraud causes a serious loss to both of the users and carriers. The academia has an increasing interest in the issue of detecting and recognizing fraudster, and varies strategies have been proposed to prevent the attack and fraudulent activity. However, fraudsters are always inclined to hide their identity and perform the fraudulent activity through different mobile carriers, which makes the previous methods less effective in fraud detection. In this paper, we propose a novel strategy with a high accuracy and security through the cooperation among mobile carriers. We introduce the Latent Dirichlet Allocation (LDA) model to profile users in different carriers. In order to match the fraud accounts, we propose a strategy based on Maximum Mean Discrepancy (MMD) to analyze and compare the distribution of statistical samples. Meantime, during the cooperation of carriers, there is a risk of privacy disclosure. To deal with this weakness, we also demonstrate that our method can detect the fraudulent accounts without leaking the private records and data of user accounts based on the differential privacy.

Multi-proxy signature is a variant of proxy signature, which allows that a delegator (original signer) may delegate his signing rights to many proxy signers. Comparing with proxy signatures, multi-proxy signatures can effectively prevent that some of proxy signers abuse signing rights. Also, with the rapid development of identity-based cryptography, identity-based multi-proxy signature (IBMPS) schemes have been proposed. Comparing with proxy signature based on public key cryptography, IBMPS can simplify key management and be used for more applications. Presently, many identity-based multi-proxy signature schemes have been proposed, but most of them are constructed in the random oracle model. Also, the existing security model for identity-based multi-proxy signature is not enough complete according to the Boldyreva et al.’s work. In this paper, we present a framework for IBMPS on n + 1 users ( n is the number of proxy signers participating in signing), and show a detailed security model for IBMPS. Under our framework, we present an identity-based multi-proxy signature scheme in the standard model. Comparing with other identity-based multi-proxy signature schemes, the proposed scheme has more complete security.

Policies play an important role in network configuration and therefore in offering secure and high performance services especially over multi-tenant Cloud Data Center (DC) environments. At the same time, elastic resource provisioning through virtualization often disregards policy requirements, assuming that the policy implementation is handled by the underlying network infrastructure. This can result in policy violations, performance degradation and security vulnerabilities. In this paper, we define PLAN, a PoLicy-Aware and Network-aware VM management scheme to jointly consider DC communication cost reduction through Virtual Machine (VM) migration while meeting network policy requirements. We show that the problem is NP-hard and derive an efficient approximate algorithm to reduce communication cost while adhering to policy constraints. Through extensive evaluation, we show that PLAN can reduce topology-wide communication cost by 38 percent over diverse aggregate traffic and configuration policies.

With rapid growth of LTE network and Voice-over-LTE(VoLTE), detecting and preventing security threats like Denial of Service attack becomes a necessary and urgent requirement. VoLTE is an voice solution based on Internet Protocol and 4G LTE technology, at the same time exposing many vulnerabilities when using packet-switched network. There are many heavy weighted detection systems using content analysis, while high demands of computing resource constraint their practical use. In this paper, we purpose a lightweight detection scheme for VoLTE network security, based on analysis of data traffic flow. To optimize parameters in our scheme, we formulate a Bayesian game model. Bayesian game has the features of incomplete information and asymmetry, similar to practical attack-defense model. Besides, The dynamic Bayesian game is more realistic, since both sides can update believes about their opponents. Simulation results provide some guidances on parameter selection, as well as verifying the superiority of our scheme.

With the increasing demand of Quality of Service(QoS) in Crowdsensing Networks, providing broadcast authentication and preventing Denial of Service (DoS) attacks become not only a fundamental issue but also a challenging security service. The multi-level TESLA is a series of lightweight broadcast authentication protocols, which can effectively mitigate DoS attacks via randomly selected messages. However, the rule of the parameter selection still remains a problem. In this paper, we formulate the attack-defense model as an evolutionary game accordingly, and then present an optimal solution, which achieves security assurance along with minimum resource cost. We then analyze the stability of our evolutionary strategy theoretically. Simulation results are given to evaluate the performance of the proposed algorithm under low QoS channels and severe DoS attacks, which demonstrates that our proposed protocol canworks even in the extreme case.

Camera sensor networks (CSNs) are gradually being used in a tele-intensive care unit (tele-ICU), providing useful patient information to remote intensivists. Intensivists wish to focus on different regions of interest (RoIs) containing their patients. We consider a situation where preinstalled camera sensors' locations remain static and they can change the fields of view only by rotating orientations, while the RoIs are dynamically changed in location and size because of changes in the number of patients and care unit configuration. Therefore, an important issue is how to enhance the coverage of these RoIs by controlling the camera sensors' orientations. Previous studies on coverage optimization either focus on single area coverage or point(s) coverage. However, ignoring those multiple RoIs or simply treating them as points can cause unwanted coverage, resulting in performance degradation. In this paper, we investigate a novel multiple RoI coverage (MRC) problem in a CSN-based tele-ICU, aiming to maximize the lowest coverage ratio of all RoIs. The MRC problem is nondeterministic polynomial-time hard, so we propose an efficient heuristic algorithm MRC-Priority to solve it. We have implemented a CSN testbed to evaluate the performance of our proposed algorithm. Experimental results show that our proposed algorithm can improve the lowest coverage ratio up to 200% as compared with existing solutions.

Policies play an important role in network configuration and, therefore, in offering secure and high performance services, especially over multi-tenant Cloud Data Center (DC) environments. At the same time, elastic resource provisioning through virtualization often disregards policy requirements, assuming that the policy implementation is handled by the underlying network infrastructure. In this paper, we define PLAN, a Policy-Aware virtual machine management scheme to jointly consider DC communication cost reduction through Virtual Machine (VM) migration while meeting network policy requirements.

Presently, many identity-based proxy signature (IBPS) schemes have been proposed, but most of them are constructed in the random oracle model. Also, the proposed security model for IBPS is not enough complete according to Boldyreva's work. Cao and Cao proposed an IBPS scheme in the standard model. However, their scheme is not secure because of not resisting the attack of delegator and requires more computation cost. In this paper, we present a framework for IBPS and show a detailed security model for IBPS. Under our framework, we present an efficient IBPS scheme in the standard model. Compared with other IBPS schemes, the proposed scheme has more complete security and is more efficient.

Online social networks (OSN) have become one of the major platforms for people to exchange information. Both positive information (e.g., ideas, news and opinions) and negative information (e.g., rumors and gossips) spreading in social media can greatly influence our lives. Previously, researchers have proposed models to understand their propagation dynamics. However, those were merely simulations in nature and only focused on the spread of one type of information. Due to the human-related factors involved, simultaneous spread of negative and positive information cannot be thought of the superposition of two independent propagations. In order to fix these deficiencies, we propose an analytical model which is built stochastically from a node level up. It can present the temporal dynamics of spread such as the time people check newly arrived messages or forward them. Moreover, it is capable of capturing people’s behavioral differences in preferring what to believe or disbelieve. We studied the social parameters impact on propagation using this model. We found that some factors such as people’s preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it. The extensive simulations conducted on the real topologies confirm the high accuracy of our model.

Due to the critical security threats imposed by email-based malware in recent years, modeling the propagation dynamics of email malware becomes a fundamental technique for predicting its potential damages and developing effective countermeasures. Compared to earlier versions of email malware, modern email malware exhibits two new features, reinfection and self-start. Reinfection refers to the malware behavior that modern email malware sends out malware copies whenever any healthy or infected recipients open the malicious attachment. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. In the literature, several models are proposed for email malware propagation, but they did not take into account the above two features and cannot accurately model the propagation dynamics of modern email malware. To address this problem, we derive a novel difference equation based analytical model by introducing a new concept of virtual infected user. The proposed model can precisely present the repetitious spreading process caused by reinfection and self-start and effectively overcome the associated computational challenges. We perform comprehensive empirical and theoretical study to validate the proposed analytical model. The results show our model greatly outperforms previous models in terms of estimation accuracy.

In this article, we address a new unexplored problem: what are the optimal patterns to achieve connected coverage in wireless networks with directional antennas. As their name implies, directional antennas can focus their transmission energy in a certain direction. This feature leads to lower cross-interference and larger communication distance. It has been shown that, with proper scheduling mechanisms, directional antennas may substantially improve networking performance in wireless networks. In this article, we propose a set of deployment patterns to achieve full coverage and up to 2-connectivity under two different antenna models, namely the sector model and the knob model. These patterns are optimal under most combinations of communication and sensing ranges. We also introduce with detailed analysis several fundamental theorems and conjectures. Finally, we examine a more realistic physical model, where there might be strong interference and both the sensing range and the communication range might be irregular. The results show that our designed patterns work well even in unstable and fickle physical environments.

Restraining the spread of rumors in online social networks (OSNs) has long been an important but difficult problem to be addressed. Currently, there are mainly two types of methods 1) blocking rumors at the most influential users or community bridges, or 2) spreading truths to clarify the rumors. Each method claims the better performance among all the others according to their own considerations and environments. However, there must be one standing out of the rest. In this paper, we focus on this part of work. The difficulty is that there does not exist a universal standard to evaluate them. In order to address this problem, we carry out a series of empirical and theoretical analysis on the basis of the introduced mathematical model. Based on this mathematical platform, each method will be evaluated by using real OSN data. We have done three types of analysis in this work. First, we compare all the measures of locating important users. The results suggest that the degree and betweenness measures outperform all the others in the Facebook network. Second, we analyze the method of the truth clarification method, and find that this method has a long-term performance while the degree measure performs well only in the early stage. Third, in order to leverage these two methods, we further explore the strategy of different methods working together and their equivalence. Given a fixed budget in the real world, our analysis provides a potential solution to find out a better strategy by integrating both types of methods together. From both the academic and technical perspective, the work in this paper is an important step towards the most practical and optimal strategies of restraining rumors in OSNs.

ABSTRACT Recently, the direct sequence spread spectrum (DSSS)‐based technique has been proposed to trace anonymous network flows. In this technique, homogeneous pseudo‐noise (PN) codes are used to modulate multiple bit signals that are embedded into the target flow as watermarks. This technique could be maliciously used to degrade an anonymous communication network. In this paper, we propose an effective single flow‐based scheme to detect the existence of these watermarks. Our investigation shows that, even if we have no knowledge of the applied PN code, we are still able to detect malicious DSSS watermarks via mean‐square autocorrelation (MSAC) of a single modulated flow's traffic rate time series. MSAC shows periodic peaks because of self‐similarity in the modulated traffic caused by homogeneous PN codes that are used in modulating multiple bit signals. Our scheme has low complexity and does not require any PN code synchronization. We evaluate this detection scheme's effectiveness via simulations. Our results demonstrate a high detection rate with a low false positive rate. Real‐world experiments on Tor also validate the feasibility of the detection scheme. Our scheme is more flexible and accurate than the existing multiflow‐based approach in DSSS watermark detection. We also present a theory for reconstructing the DSSS code once the DSSS code length is known and simulations validate the feasibility. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we present a study on the anonymity of Anonymizer, a well-known commercial anonymous communication system. We discovered the architecture of Anonymizer and found that the size of web packets in the Anonymizer network can be very dynamic at the client. Motivated by this finding, we investigated a class of novel packet size-based covert channel attacks against Anonymizer. The attacker between a website and the Anonymizer server can manipulate the web packet size and embed secret signal symbols into the target traffic. An accomplice at the user side can sniff the traffic and recognize the secret signal. In this way, the anonymity provided by Anonymizer is compromised. We developed intelligent and robust algorithms to cope with the packet size distortion incurred by Anonymizer and Internet. We developed techniques to make the attack harder to detect: 1) We pick up right packets of web objects to manipulate to preserve the regularity of the TCP packet size dynamics, which can be measured by the Hurst parameter; 2) We adopt the Monte Carlo sampling technique to preserve the distribution of the web packet size despite manipulation. We have implemented the attack over Anonymizer and conducted extensive analytical and experimental evaluations. It is observed that the attack is highly efficient and requires only tens of packets to compromise the anonymous web surfing via Anonymizer. The experimental results are consistent with our theoretical analysis.

Abstract—Wide range wireless networks often suffer from annoying service deterioration due to fickle wireless environment. This is especially the case with passengers on long distance train (LDT) to connect onto the Internet. To improve the service quality of wide range wireless networks, we present the DragonNet protocol with its implementation. The DragonNet system is a chained gateway which consists of a group of interlinked DragonNet routers working specifically for mobile chain transport systems. The protocol makes use of the spatial diversity of wireless signals that not all spots on a surface see the same level of radio frequency radiation. In the case of a LDT of around 500 meters, it is highly possible that some of the spanning routers still see sound signal quality, when the LDT is partially blocked from wireless Internet. DragonNet protocol fully utilizes this feature to amortize single point router failure over the whole router chain by intelligently rerouting traffics on failed ones to sound ones. We have implemented the DragonNet system and tested it in real railways over a period of three months. Our results have pinpointed two fundamental contributions of DragonNet protocol. First, DragonNet significantly reduces average temporary communication blackout (i.e. no Internet connection) to 1.5 seconds compared with 6 seconds that without DragonNet protocol. Second, DragonNet efficiently doubles the aggregate throughput on average.

Social network worms, such as email worms and facebook worms, pose a critical security threat to the Internet. Modeling their propagation dynamics is essential to predict their potential damages and develop countermeasures. Although several analytical models have been proposed for modeling propagation dynamics of social network worms, there are two critical problems unsolved: temporal dynamics and spatial dependence. First, previous models have not taken into account the different time periods of Internet users checking emails or social messages, namely, temporal dynamics. Second, the problem of spatial dependence results from the improper assumption that the states of neighboring nodes are independent. These two problems seriously affect the accuracy of the previous analytical models. To address these two problems, we propose a novel analytical model. This model implements a spatial-temporal synchronization process, which is able to capture the temporal dynamics. Additionally, we find the essence of spatial dependence is the spreading cycles. By eliminating the effect of these cycles, our model overcomes the computational challenge of spatial dependence and provides a stronger approximation to the propagation dynamics. To evaluate our susceptible-infectious-immunized (SII) model, we conduct both theoretical analysis and extensive simulations. Compared with previous epidemic models and the spatial-temporal model, the experimental results show our SII model achieves a greater accuracy. We also compare our model with the susceptible-infectious-susceptible and susceptible-infectious- recovered models. The results show that our model is more suitable for modeling the propagation of social network worms.

The majority of current anonymous systems focus on improving anonymity at the network and website level in order to defend against traffic analysis attacks. However, the vulnerability of the connections between end users and the anonymous network do not attract any attention yet. For the first time, we reveal an end user browsing dynamics based attack on anonymous browsing systems at the LAN where the victim locates. This new attack method is fundamentally different from existing attack methodologies. In general, web surfers browse the web following certain patterns, such as requesting a web page, viewing it and requesting another page. The browsing pattern of a victim can be clearly observed by a local adversary when the victim is viewing the web without protection. Unfortunately, browsing dynamics releases rich information for attacking even though the web page content is encrypted. In order to show how a local eavesdropper can decipher which pages have been viewed with the knowledge of user browsing dynamics and the public information of a given website, we established a specific hidden Markov model to represent browsing dynamics for the website. By using this model, we can then identify the optimal of the accessed pages using the Viterbi algorithm. In order to confirm the effectiveness of the revealed attack method, we have conducted extensive experiments on a real data set. The results demonstrated that the attack accuracy can be more than 80%. A few possible counter-attack strategies are discussed at the end of the paper.

Efficient estimation of global information is a common requirement for many wireless sensor network applications. Examples include counting the number of nodes alive in the network and measuring the scale of physically correlated events. These tasks must be accomplished at extremely low overhead due to the severe resource limitation of sensor nodes, which poses a challenge for large-scale sensor networks. In this paper, we develop a novel protocol FLAKE to efficiently and accurately estimate the global information of large-scale sensor networks based on the sparse sampling theory. Specially, FLAKE disseminates a small number of messages called seeds to the network and issues a query about which nodes receive a seed. The number of nodes that have the information of interest can be estimated by counting the seeds disseminated, the nodes queried, and the nodes that receive a seed. FLAKE can be easily implemented in a distributed manner due to its simplicity. Moreover, desirable tradeoffs can be achieved between the accuracy of estimation and the system overhead. Our simulations show that FLAKE significantly outperforms several existing schemes on accuracy, delay, and message overhead.

Recent research shows that significant energy saving can be achieved in mobility-enabled wireless sensor networks (WSNs) that visit sensor nodes and collect data from them via short-range communications. However, a major performance bottleneck of such WSNs is the significantly increased latency in data collection due to the low movement speed of mobile base stations. To address this issue, we propose a rendezvous-based data collection approach in which a subset of nodes serve as rendezvous points that buffer and aggregate data originated from sources and transfer to the base station when it arrives. This approach combines the advantages of controlled mobility and in-network data caching and can achieve a desirable balance between network energy saving and data collection delay. We propose efficient rendezvous design algorithms with provable performance bounds for mobile base stations with variable and fixed tracks, respectively. The effectiveness of our approach is validated through both theoretical analysis and extensive simulations.

Various low-latency anonymous communication systems such as Tor and Anonymizer have been designed to provide anonymity service for users. In order to hide the communication of users, most of the anonymity systems pack the application data into equal-sized cells (e.g., 512 B for Tor, a known real-world, circuit-based, low-latency anonymous communication network). Via extensive experiments on Tor, we found that the size of IP packets in the Tor network can be very dynamic because a cell is an application concept and the IP layer may repack cells. Based on this finding, we investigate a new cell-counting-based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the number of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then, an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor, and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective, and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g., using our hopping-based signal embedding).

In this paper, we study the optimal deployment pattern problem in wireless sensor networks (WSNs). We propose a new set of patterns, particularly when sensors' communication range ($r_{\rm c}$) is relatively small compared to their sensing range ($r_{\rm s}$), and prove their optimality. In this study, we discover an interesting phenomenon—pattern mutation. To the best of our knowledge, this is the first time that mutation in pattern deployments has been discovered. This phenomenon, which contradicts the conjecture presented in a previous work that there exists a universal elemental pattern among optimal pattern deployment, significantly furthers our understanding of optimal patterns in WSNs.

This paper presents an empirical study on the performance of mobile High Speed Packet Access (a 3.5G cellular standard usually abbreviated as HSPA) networks in Hong Kong via extensive field tests. Our study, from the viewpoint of end users, covers virtually all possible mobile scenarios in urban areas, including subways, trains, off-shore ferries, and city buses. We have confirmed that mobility has largely negative impacts on the performance of HSPA networks, as fast-changing wireless environment causes serious service deterioration or even interruption. Meanwhile, our field experiment results have shown unexpected new findings and thereby exposed new features of the mobile HSPA networks, which contradict commonly held views. We surprisingly find out that mobility can improve fairness of bandwidth sharing among users and traffic flows. Also, the triggering and final results of handoffs in mobile HSPA networks are unpredictable and often inappropriate, thus calling for fast reacting fallover mechanisms. Moreover, we find that throughput performance does not monotonically decrease with increased mobility level. We have conducted in-depth research to furnish detailed analysis and explanations to what we have observed. We conclude that mobility is a double-edged sword for HSPA networks. To the best of our knowledge, this is the first public report on a large-scale empirical study on the performance of commercial mobile HSPA networks.

Distributed Denial of Service (DDoS) attack is a critical threat to the Internet, and botnets are usually the engines behind them. Sophisticated botmasters attempt to disable detectors by mimicking the traffic patterns of flash crowds. This poses a critical challenge to those who defend against DDoS attacks. In our deep study of the size and organization of current botnets, we found that the current attack flows are usually more similar to each other compared to the flows of flash crowds. Based on this, we proposed a discrimination algorithm using the flow correlation coefficient as a similarity metric among suspicious flows. We formulated the problem, and presented theoretical proofs for the feasibility of the proposed discrimination method in theory. Our extensive experiments confirmed the theoretical analysis and demonstrated the effectiveness of the proposed method in practice.

Broadband wireless access for maritime users is a brand-new topic which demands sophisticated transmission technology to support high speed and wide coverage communications. Although the IEEE 802.16j network has been identified as a promising solution to broadband wireless access, the deployment of such networks in a maritime environment faces significant challenges because the natural movement of the ocean surface often causes the motion of stations and further leads to the variation in quality of communication channels. To tackle with these challenges and facilitate channel exploitation for deploying 802.16j based maritime communication networks, we first propose a novel scheme to specify the communication channel state between different maritime stations, based on which a new adaptation scheme is developed to exploit the optimal channel transmission capacity. Moreover, an adaptive traffic scheduling scheme is proposed to fully utilize the channel transmission bandwidth in 802.16j maritime networks. Simulation experiments have shown that the proposed schemes can fully exploit the maritime channels to achieve both satisfactory QoS performance and system throughput for maritime users.

Anonymizer is a proprietary anonymous communication system. We discovered its architecture and found that the size of web packets through Anonymizer are very dynamic at the client. Motivated by this finding, we investigated a novel packet size based covert channel attack, against the anonymity service. In the attack, one attacker manipulates the web packet size between the web server and Anonymizer and embed signal symbols into the target traffic. An accomplice at the user side can sniff the traffic and recognize the secret signal. We developed intelligent and robust algorithms to cope with the packet size distortion incurred by Anonymizer and Internet. We developed several techniques to make the attack harder to detect: (i) We pick up right packets of web objects to manipulate in order to preserve the regularity of the TCP packet size dynamics; (ii) We adopt the Monte Carlo sampling technique to preserve the distribution of the web packet size despite manipulation. We have implemented the attack over Anonymizer and conducted extensive analysis and experimental evaluations. It is observed that the attack is highly efficient and requires only tens of packets to compromise the anonymous web surfing. The experimental results are consistent with our theoretical analysis.

Wide range wireless networks often suffer from annoying service deterioration due to fickle wireless environment. This is especially the case with passengers on long distance train (LDT) to connect onto the Internet. To improve the service quality of wide range wireless networks, we present the DragonNet protocol with its implementation. The DragonNet system is a chained gateway which consists of a group of interlinked DragonNet routers working specifically for mobile chain transport systems. The protocol makes use of the spatial diversity of wireless signals that not all spots on a surface see the same level of radio frequency radiation. In the case of a LDT of around 500 meters, it is highly possible that some of the spanning routers still see sound signal quality, when the LDT is partially blocked from wireless Internet. DragonNet protocol fully utilizes this feature to amortize single point router failure over the whole router chain by intelligently rerouting traffics on failed ones to sound ones. We have implemented the DragonNet system and tested it in real railways over a period of three months. Our results have pinpointed two fundamental contributions of DragonNet protocol. First, DragonNet significantly reduces average temporary communication blackout (i.e. no Internet connection) to 1.5 seconds compared with 6 seconds that without DragonNet protocol. Second, DragonNet efficiently doubles the aggregate throughput on average.

In this paper, we address a new unexplored problem - what are the optimal patterns to achieve connected coverage in wireless networks with directional antennas. As their name implies, directional antennas can focus their transmission energy in a certain direction. This feature leads to lower cross-interference and larger communication distance. It has been shown that with proper scheduling mechanisms, directional antennas may substantially improve networking performance in wireless networks. In this paper, we propose a set of optimal patterns to achieve full coverage and global connectivity under two different antenna models, i.e., the sector model and the knob model. We also introduce with detailed analysis several fundamental theorems and conjectures. Finally, we examine a more realistic physical model, where there might be strong interference, and both the sensing range and the communication range might be irregular. The results show that our designed patterns work well even in unstable and fickle physical environments.

In wireless sensor networks (WSNs), constructing a coordinate system is a challenging issue for geographic routing. The use of specialized hardware and/or infrastructure supporting for localization is costly, and it is infeasible in many deployment scenarios. To address this issue, we define a virtual region coordinate system without location information in WSNs, which is highly scalable and efficient with a simple system set-up procedure. A highly efficient coordinate-based routing scheme is also proposed. Extensive simulation shows that our scheme is suitable for the condition that sensor nodes don't know their location information.

Distributed Denial-of-Service (DDoS) attacks are a critical threat to the Internet. However, the memoryless feature of the Internet routing mechanisms makes it extremely hard to trace back to the source of these attacks. As a result, there is no effective and efficient method to deal with this issue so far. In this paper, we propose a novel traceback method for DDoS attacks that is based on entropy variations between normal and DDoS attack traffic, which is fundamentally different from commonly used packet marking techniques. In comparison to the existing DDoS traceback methods, the proposed strategy possesses a number of advantages - it is memory nonintensive, efficiently scalable, robust against packet pollution, and independent of attack traffic patterns. The results of extensive experimental and simulation studies are presented to demonstrate the effectiveness and efficiency of the proposed method. Our experiments show that accurate traceback is possible within 20 seconds (approximately) in a large-scale attack network with thousands of zombies.

Network coding allows a network node to code the information flows before forwarding them. While it has been theoretically proved that network coding can achieve maximum network throughput, the theoretical results usually do not consider the burstiness of data traffic, delays, and the stochastic nature in information processing and transmission. There is currently no theory to systematically model and evaluate the performance of network coding, especially when node's capacity (i.e., coding and transmission) becomes stochastic. Without such a theory, the performance of network coding under various system settings is far from clear. To fill the vacancy, we develop an analytical approach by extending the stochastic network calculus theory to tackle the special difficulties in the evaluation of network coding. We prove the new properties of the stochastic network calculus and design an algorithm to obtain the performance bounds for acyclic stochastic networks with network coding. The tightness of theoretical bounds is validated with simulation.

This paper introduces AMR (Adaptive Multi Rate) codec algorithm and the basic characteristics of ARM architecture, and focus on the optimization methods of AMR algorithm based on ARM platform, making use of ARMv5E core hardware features. Firstly the CPU usage rate of AMR key functions are analyzed, then two methods including inline optimization and assembly optimization are discussed, finally optimization result is verified through experiment in smart phones. Experiments prove that the running speed of optimized code have been significantly increased in ARM core smart phones.

In this paper, we focus on the query problem of trajectory tracking of moving targets in a wireless sensor network. Our motivation is to design in-network storage and querying protocols to support queries for searching a given target signature in a low query latency, high query reliability, and energy efficient manner. We propose a target trajectory query protocol, which combines index-based query and random query schemes. The query protocol guarantees query reliability and energy efficiency. In an index-based queries scheme, each target trajectory has an index, which is stored at an index node (designated node). Query nodes send query messages to the index node to get results. Naturally, index-based queries can guarantee query reliability, but the disadvantage is the query hotspot problem, which reduces the lifetime of the sensor network. A random queries scheme, which is based on opportunistic target signature propagation, sends query messages in random chosen directions to hit a sensor node that contains the given target signature near the query node. However, queries for targets whose signature propagation is very limited need a large number of random query messages to achieve high query reliability. Our algorithm combines the two query schemes. It takes advantages of both of them, and eliminates each individual's disadvantages. Performance analysis and simulation studies show that our query protocol is superior to index-based queries in terms of the query hotspot problem, and it is superior to random queries in terms of energy efficiency, query latency, and query reliability.

In this paper, we study the optimal deployment pattern problem in wireless sensor networks (WSNs). We propose a new set of patterns, particularly when sensors' communication range (rc) is relatively small compared with their sensing range (rs), and prove their optimality among regular patterns. In this study, we discover a surprising and interesting phenomenon-pattern mutation. This phenomenon contradicts the conjecture presented in a previous work that there exists a universal elemental pattern among optimal pattern evolution and that pattern evolution is continuous. For example, we find mutation happens among the patterns for full-coverage and 3-connectivity when rc/rs= 1.0459, among the patterns for full-coverage and 4-connectivity when rc/rs= 1.3903, and among the patterns for full-coverage and 5-connectivity when rc/rs= 1.0406. To the best of our knowledge, this is the first time that mutation in pattern evolution has been discovered. Also, our work further completes the exploration of optimal patterns in WSNs.

Efficient estimation of global information is a common requirement for many wireless sensor network applications. Examples include counting the number of nodes alive in the network and measuring the scale of physically correlated events. These tasks must be accomplished at extremely low overhead due to the severe resource limitation of sensor nodes, which poses a challenge for large-scale sensor networks. In this paper, we develop a novel protocol called FLAKE that can efficiently and accurately estimate the global information of large-scale sensor networks based on the sparse sampling theory. Specially, FLAKE disseminates a small number of messages called seeds to the network and issues a query about which nodes receive a seed. The number of nodes that have the information of interest can be estimated by counting the seeds disseminated, the nodes queried, and the nodes that receive a seed. FLAKE can be easily implemented in a distributed manner due to its simplicity. Moreover, desirable trade-offs can be achieved between the accuracy of estimation and the system overhead. Our simulations show that FLAKE significantly outperforms several existing schemes on accuracy, delay and message overhead.

The IEEE 802.16j WiMAX is designed to provide a broadband multi-hop wireless access. However traffic transmission in such networks faces the challenges of user demand differentiation, link congestion and network status variation. To tackle those issues we construct effective schedulers implemented at each RS to achieve the desired end to end flow control and congestion control over the whole network. Specifically we employ the control theory method and into the schedulers to control and tune both downlink and uplink traffic rate at each RS in a distributed manner. Different hops of scheduling can be coordinated with each other so that all traffic transmissions over the whole network can be regulated to and stabilized at the desired levels. Both the theoretical analysis and experiment tests verify that our proposed scheduling schemes can achieve an effective traffic control over the IEEE 802.16j WiMAX networks.

Recent years have witnessed the emergence of numerous new Internet services for mobile users. Supporting mobile applications via public WiFi networks has received significant research attention due to the drastic increase of penetration rate of 802.11-based networks. Nevertheless, recent empirical studies showed that unplanned WiFi networks cannot provide satisfactory Quality of Service for interactive mobile applications due to intermittent network connectivity. In this paper, we exploit realistic mobility characteristics of users to deploy WiFi Access Points (APs) for continuous service for mobile users. We study two AP deployment problems that aim to maximize the continuous user coverage and to minimize the AP deployment cost, respectively. Both problems are formulated based on mobility graphs that capture the statistical mobility patterns of users. We prove that both problems are NP-hard. We develop several optimal and approximation algorithms with provable performance bounds for different topologies of mobility graphs. The effectiveness of our approaches is validated by extensive simulations using real user mobility traces.

This paper presents an empirical study on the performance of mobile High Speed Packet Access (HSPA, a 3.5G cellular standard) networks in Hong Kong via extensive field tests. Our study, from the viewpoint of end users, covers virtually all possible mobile scenarios in urban areas, including subways, trains, off-shore ferries and city buses. We have confirmed that mobility has largely negative impacts on the performance of HSPA networks, as fast-changing wireless environment causes serious service deterioration or even interruption. Meanwhile our field experiment results have shown unexpected new findings and thereby exposed new features of the mobile HSPA networks, which contradict commonly held views. We surprisingly find out that mobility can improve fairness of bandwidth sharing among users and traffic flows. Also the triggering and final results of handoffs in mobile HSPA networks are unpredictable and often inappropriate, thus calling for fast reacting fallover mechanisms. We have conducted in-depth research to furnish detailed analysis and explanations to what we have observed. We conclude that mobility is a double-edged sword for HSPA networks. To the best of our knowledge, this is the first public report on a large scale empirical study on the performance of commercial mobile HSPA networks.

Wireless access points (APs) divide a plane into small areas where their coverage ranges overlap. A mobile device can be located within a particular small overlapped area based on the unique set of APs covering the device. We formally define an optimal AP deployment problem for both coverage and area localization. Our objective is to deploy a minimum number of APs that provide full communication coverage while achieving the ability to locate a mobile device within a certain area no larger than a given accuracy parameter. We propose a set of optimal solutions and approximations to this problem under the diamond pattern, a deployment pattern that has been shown to achieve optimal coverage in most cases. We conduct extensive numerical evaluation as well as real experiments to validate our proposed solutions.

This paper proposes a Binomial Probability Redundancy Model (BPRM). This model is based on the Forward Error Correction (FEC) Reed-Solomon Coding Technique and the Interweaving Packet Loss Recovery Technique. It calculates the number of redundant packets according to the feedback from the receiver and dynamically generates these superfluous packets. Different from the Linear Probability Redundancy Model (LPRM) proposed by McKinley et al., we adopt the interweaving technique to transmit these redundant packets. We also present a bandwidth control strategy to improve quality of video data. The experimental result shows that BPRM is able to generate enough redundant packets even under congestive network condition. These packets ensure the receiver to restore the original data and achieve a better video quality compared with LPRM.

Distributed denial of service (DDoS) attack is a continuous critical threat to the Internet. Derived from the low layers, new application-layer-based DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. The case may be more serious when such attacks mimic or occur during the flash crowd event of a popular Website. In this paper, we present the design and implementation of CALD, an architectural extension to protect Web servers against various DDoS attacks that masquerade as flash crowds. CALD provides real-time detection using mess tests but is different from other systems that use resembling methods. First, CALD uses a front-end sensor to monitor the traffic that may contain various DDoS attacks or flash crowds. Intense pulse in the traffic means possible existence of anomalies because this is the basic property of DDoS attacks and flash crowds. Once abnormal traffic is identified, the sensor sends ATTENTION signal to activate the attack detection module. Second, CALD dynamically records the average frequency of each source IP and check the total mess extent. Theoretically, the mess extent of DDoS attacks is larger than the one of flash crowds. Thus, with some parameters from the attack detection module, the filter is capable of letting the legitimate requests through but the attack traffic stopped. Third, CALD may divide the security modules away from the Web servers. As a result, it keeps maximum performance on the kernel web services, regardless of the harassment from DDoS. In the experiments, the records from www.sina.com and www.taobao.com have proved the value of CALD.

Low-connectivity and full-coverage three dimensional Wireless Sensor Networks (WSNs) have many real-world applications. By low connectivity, we mean there are at least k disjoint paths between any two sensor nodes in a WSN, where k ≤ 4. In this paper, we design a set of patterns to achieve 1-, 2-, 3- and 4-connectivity and full-coverage, and prove their optimality under any value of the ratio of communication range rcover sensing range rs, among regular lattice deployment patterns. We further investigate the evolutions among all the proposed low-connectivity patterns. Finally, we study the proposed patterns under several practical settings.

In this paper, we study optimal deployment in terms of the number of sensors required to achieve four-connectivity and full coverage under different ratios of sensors' communication range (denoted by rc) to their sensing range (denoted by rs). We propose a new pattern, the Diamond pattern, which can be viewed as a series of evolving patterns. When rc/rs¿ ¿(3), the Diamond pattern coincides with the well-known triangle lattice pattern; when rc/rs¿ ¿(2), it degenerates to a Square pattern (i.e., a square grid). We prove that our proposed pattern is asymptotically optimal when rc/rs> ¿(2) to achieve four-connectivity and full coverage. We also discover another new deployment pattern called the Double-strip pattern. This pattern provides a new aspect to research on optimal deployment patterns. Our work is the first to propose an asymptotically optimal deployment pattern to achieve four-connectivity and full coverage for WSNs. Our work also provides insights on how optimal patterns evolve and how to search for them.

In this paper, we study deployment patterns to achieve full coverage andk-connectivity(k≤ 6) under different ratios of the sensor communication range (denoted byRc) to the sensing range (denoted byRs) for homogeneous wireless sensor networks (WSNs). In particular, we propose new patterns for 3- and 5-connectivity. We also discover that there exists a hexagon-based universally elemental pattern that can generate all known optimal patterns. The previously proposed Voronoi-based approach cannot be applied to prove the optimality of the new patterns due to their special features. We propose a new deployment-polygon-based methodology. We prove the optimality of deployment patterns to achieve 3-connectivity, 4-connectivity, and 5-connectivity for certain ranges ofRc/Rs, respectively, and prove the optimality of deployment patterns to achieve 6-connectivity under all ranges ofRc/Rs.

Null data frames are a special but important type of frames in IEEE 802.11 WLANs. They are widely used in 802.11 WLANs for control purposes such as power management, channel scanning, and association keeping alive. The wide applications of null data frames come from their salient features such as lightweight frame format and implementation flexibility. However, such features can be taken advantage of by malicious attackers to launch a variety of attacks on 802.11 WLANs. In this paper, we identify potential security vulnerabilities in current null data frame applications in 802.11 WLANs. We then study two types of attacks taking advantage of these vulnerabilities in detail that are functionality-based Denial-of-Service attack and implementation-based fingerprinting attack. We also evaluate their effectiveness based on extensive experiments. Furthermore, we design and implement novel defense mechanisms against the attacks, and evaluate their effectiveness based on extensive experiments. Although our proposed defenses help alleviate the vulnerabilities, completely eliminating the vulnerabilities brought by null data frames remains an open issue. Finally, we point out that our work has broader impact in that similar vulnerabilities exist in many other networks due to the adoption of simple and lightweight messages for control purpose.

Various low-latency anonymous communication systems such as Tor and Anoymizer have been designed to provide anonymity service for users. In order to hide the communication of users, many anonymity systems pack the application data into equal-sized cells (e.g., 512 bytes for Tor, a known real-world, circuit-based low-latency anonymous communication network). In this paper, we investigate a new cell counter based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the counter of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g. using our hopping-based signal embedding).

Admission control is an important mechanism for the provisioning of the user-perceived Quality-of-Service (QoS) in the IEEE 802.11e Wireless Local Area Networks (WLANs). In this paper, we present an efficient admission control scheme based on analytical modelling and non-cooperative game theory where the Access Point (AP) and new users are the players. The decision of admission control is made by virtue of the strategies to maximize the utilities of the players, which are determined by the QoS performance metrics in terms of the end-to-end delay and frame loss probability. To obtain these required performance metrics, we develop a new analytical model incorporating the Contention Window (CW) and Transmission Opportunity (TXOP) differentiation schemes in the IEEE 802.11e protocol under unsaturated working conditions. The efficiency of the proposed admission control scheme is validated via NS-2 simulation experiments. The numerical results demonstrate that the proposed admission control scheme can maintain the system operation at an optimal point where the utility of the AP is maximized subject to the QoS constraints of both the real-time and non-real-time users.

In this paper, we study the problem of constructing full-coverage three dimensional networks with multiple connectivity. We design a set of patterns for full coverage and two representative connectivity requirements, i.e. 14- and 6-connectivity. We prove their optimality under any ratio of the communication range over the sensing range among regular lattice deployment patterns. We also conduct a study on the proposed patterns under practical settings. To our knowledge, our work is the first one that provides deployment patterns with proven optimality that achieve both coverage and connectivity in three dimensional networks.

Recently, the direct sequence spread-spectrum (DSSS)-based technique has been proposed to trace anonymous network flows. In this technique, homogeneous pseudo-noise (PN) codes are used to modulate multiple-bit signals that are embedded into the target flow as watermarks. This technique could be maliciously used to degrade an anonymous communication network. In this paper, we propose a simple single flow-based scheme to detect the existence of these watermarks. Our investigation shows that even if we have no knowledge of the applied PN code, we are still able to detect malicious DSSS watermarks via mean-square autocorrelation (MSAC) of a single modulated flow's traffic rate time series. MSAC shows periodic peaks due to self-similarity in the modulated traffic caused by homogeneous PN codes that are used in modulating multiple-bit signals. Our scheme has low complexity and does not require any PN-code synchronization. We evaluate this detection scheme's effectiveness via simulations and real-world experiments on Tor. Our results demonstrate a high detection rate with a low false positive rate. Our scheme is more flexible and accurate than an existing multi-flow-based approach in DSSS watermark detection.

802.11 wireless networks have gained ever greater popularity nowadays. Apart from static wireless connections, people begin to expect more user-friendly features from this kind of networks, such as support for seamless roaming. In this paper, we study the handoff process in large AP-dense 802.11 networks, which is one of the most common forms of WiFi under usage. A series of field experiments are carried out and some critical handoff parameters are evaluated. With some newly discovered features, i.e. differentiated probe response time and rich AP information hidden in wireless traffic, we have managed to significantly improve the essential process of AP scan, a bottleneck towards fast and smooth handoffs. The solution is collectively called D-Scan (Scan in AP-Dense 802.11 networks). Real experiments are conducted to show the superiority of our solution.

Current investment in crop monitoring consumes a large amount of financial cost, and how to reduce this cost has been a long-standing problem in agriculture. Traditional crop monitoring approaches are not cost-effective, because they rely on either heavy human labor or intensive computation with expensive instruments. In this paper, we explore the possibility of deploying networked sensor nodes for low-cost crop monitoring. As an example, we compute an important agricultural metric called global leaf area index (LAI) to illustrate the benefit of using sensor networks. We propose an approach called FOCUS that incrementally deploys sensor nodes into farmland to improve the accuracy of global LAI measurements. We design and implement a novel algorithm that calculates the total size of crop leaves with light intensity readings captured by the sensors under the crop canopies. FOCUS not only lowers the deployment cost considerably but also reduces the number of sensors for the long-term monitoring. Through a small-scale field test and large-scale simulations, we validate our design and show its effectiveness in crop monitoring.

Low-connectivity and full-coverage three dimensional Wireless Sensor Networks (WSNs) have many real-world applications. By low connectivity, we mean there are at least k disjoint paths between any two sensor nodes in a WSN, where k ≤ 4. In this paper, we design a set of patterns for these networks. In particular, we design and prove the optimality of 1- and 2-connectivity patterns under any value of the ratio of communication range rc over sensing range rs, among regular lattice deployment patterns. We further propose a set of patterns to achieve 3- and 4-connectivity patterns and investigate the evolutions among all the proposed low-connectivity patterns. Finally, we study the proposed patterns under several practical settings.

To support the differentiated Quality-of-Service (QoS) and improve the utilization of the scarce wireless bandwidth, the IEEE 802.11e standard specifies an efficient burst transmission scheme referred to as the transmission opportunity (TXOP). Recently, analytical models have been reported to evaluate the performance of the TXOP scheme. However, most of these models were developed under the assumptions of the ideal channels or uniform error channels which fail to capture the characteristics of bursty and correlated channel errors in the practical wireless environment. In this paper, we propose an analytical model for the TXOP scheme in WLANs in the presence of bursty error channels. To this end, the transmission queue of each station is modelled by a two-state continuous time Markov chain. This model can be adopted to obtain the performance metrics including the throughput and buffer overflow probability. The accuracy of the analytical model is validated via NS-2 simulation experiments. Utilizing the proposed model, we investigate the impact of traffic loads, TXOP limit, and the number of stations on the performance of the TXOP scheme under various channel conditions.

In this paper, we investigate video-based vulnerabilities in 3G Smartphones. Particularly, we design a new video-based spyware, called Stealthy Video Capturer (SVC). SVC can secretly record video information for the third party, greatly compromising Smartphone users' privacy. We implement the spyware and conduct extensive experiments on real world 3G Smartphones. Our experimental results show that the spyware can capture private video information with unremarkable power consumption, CPU and memory occupancy, hence being stealthy to Smartphone users. Moreover, SVC can naturally be resistant to almost all commercial anti-virus tools, like McAfee, Kaspersky and F-Secure mobile version. To the best of our knowledge, our work is the first one to address video-based vulnerabilities in 3G Smartphones. We expect our work will prompt serious attentions on this issue.

Effective location distinction can help to detect the replication attack towards wireless stations. Instantaneous signal strength information can be used to identify different location information for one certain station. However, most of the previous solutions are under an assumption of a static network. In this paper, we propose a simple centroid based classification model to effectively classify the packets sent by masqueraders among all the packets received based on the aggregate signal strength vectors of packets from multiple access points. The simulation results indicate that the self-location recognition accuracies of our method for static and moving stations achieve 95% and 90%, respectively. Moreover, our method is shown to be very effective in attacker detection, in which attacker locations detection accuracy surpasses 80% even if the attacked targets are moving.

This paper proposes the so-called adaptive resource scheduling (ARS) schemes to cope with both uplink and downlink traffic transmissions in WiMAX, in order to fully utilize the radio resource, and meet the various QoS requirements as well. Specifically, for the uplink transmission, each SS control and adjust its local sessions in a distributed manner; while for downlink the BS performs the resource scheduling in a centralized manner. The corresponding rate control mechanism is given, by adopting the control theory method as well. The simulation results also validate the efficiency and practice of our proposed scheme.

Many of packet loss as a result of factors other than congestion impact the performance of TCP in wired/wirelss hybrid networks. Firstly, this paper proposes one concept of congestion probability (CP) and analyzes the correlation of CP and network state. Then a transport control protocol named as TCP-PCP is proposed, which is based on the prediction of congestion probability instead of single loss event. Depending on the ECN mechanism, TCP-PCP calculates the value of CP by analyzing some latest loss events. The TCP-PCP sender's behavior is controlled by the change of CP value. Simulation results show that TCP-PCP can improve TCP performance more efficiently than Westwood and Jersey.

Currently, the truncated binary exponential backoff based scheme had been defined as the mandatory contention resolution scheme in WiMAX standards. However, it had been discussed in many research that these schemes can not work perfectly with WiMAX in most cases. In this paper, a novel N modules based self-adaptive contention resolution scheme has been proposed, which divides all Subscriber Stations into several groups according to the number of available transmission opportunities in each frame and requires each Subscriber Station send bandwidth request during its group time. Meanwhile, the number of available transmission opportunities will be self-adaptively updated by Base Station according to the average collision probability presented in last frame. The analysis and simulation results show our scheme can improve the performance of whole network remarkably.

Multipath routing is one of the most important research directions in the area of network routing. However, it is very difficult to achieve node-disjoint multipath routing in Mobile Ad Hoc NETworks (MANETs), especially in large scale MANETs. We propose a Multiple Zones-based routing protocol (M-Zone for short) to discover node-disjoint routing paths segment-by-segment in large scale MANETs effectively. M-Zone uses a multiple zoning method based on location to guarantee that the nodes in multiple routes are different apart from the source and the destination. We propose two approaches to maintain the routes: one is local route maintenance and the other is global route maintenance. Simulation studies show that the average path length of M-Zone is close to that of GZRP, which combines the Zone Routing Protocol (ZRP) and Global Positioning System (GPS). The average packet delivery ratio of M-Zone is significantly higher than that of GZRP.

Multiple memory banks design is employed in many high performance DSP processors. This architectural feature supports higher memory bandwidth by allowing multiple data memory access to be executed in parallel. Dedicated address generation units (AGUs) are commonly presented in DSPs to perform address arithmetic in parallel to the main datapath. Address assignment, optimization of memory layout of program variables to reduce address arithmetic instruction, has been studied extensively on single memory architecture. Make effective use of AGUs on multiple memory banks is a great challenge to compiler design and has not been studied previously. In this paper, we exploit address assignment with variable partitioning for scheduling on DSP architectures with multiple memory banks and AGUs. Our approach is built on novel graph models which capture both parallelism and serialism demands. An efficient scheduling algorithm, Address Assignment Sensitive Variable Partitioning (AASVP), is proposed to best leverage both multiple memory banks and AGUs. Experimental results show significant improvement compare to existing methods.

Arbitrary inter-frame space (AIFS), contention window (CW) and transmission opportunity (TXOP) are three important quality-of-service (QoS) differentiation schemes specified in the IEEE 802.11e enhanced distributed channel access (EDCA) protocol for wireless local area networks (WLANs). Analytical models of EDCA in the current literature have been mainly developed for the AIFS, CW, and TXOP schemes, separately. This study proposes a comprehensive analytical model to accommodate the combination of these three QoS schemes in WLANs under unsaturated traffic loads. We derive the performance metrics in terms of throughput, end-to- end delay, and frame loss probability. Extensive simulation experiments are conducted to validate the accuracy of the model.

Null data frames are a special but important type of frames in IEEE 802.11 based wireless local area networks (e.g., 802.11 WLANs). They are widely used for power management, channel scanning and association keeping alive. The wide applications of null data frames come from their salient features such as lightweight frame format and implementation flexibility. However, such features can be taken advantage of by malicious attackers to launch a variety of attacks. In this paper, we identify the potential security vulnerabilities in the current applications of null data frames. We then study two types of attacks taking advantage of these vulnerabilities in detail, and evaluate their effectiveness based on extensive experiments. Finally, we point out that our work has broader impact in that similar vulnerabilities exist in many other networks.

Greedy routing protocols provide a scalable and cost effective solution for routing packets in mobile ad-hoc networks (MANETs). However, such routing protocols can not be used in the networks where holes exist. We propose a virtual ring-based hole avoiding routing protocol (VRHAR) in MANETs where holes exist. Simulation studies show that the proposed protocol can improve the routing performance more effectively than existing greedy routing protocols.

We study the issue of optimal deployment to achieve four connectivity and full coverage for wireless sensor networks (WSNs) under different ratios of sensors' communication range (denoted by rc) to their sensing range (denoted by rs). We propose a "Diamond" pattern, which can be viewed as a series of different evolving patterns. When rc/rsges radic3, the Diamond pattern coincides with the well-known triangle lattice pattern; when rc/rsges radic2, it degenerates to a "Square" pattern. We prove the Diamond pattern to be asymptotically optimal when rc/rsges radic2- Our work is the first to propose an asymptotically optimal deployment pattern to achieve four connectivity and full coverage for WSNs. We hope our work will provide some insights on how optimal patterns evolve and how to search for them.

In this paper, we propose deployment patterns to achieve full coverage and three-connectivity, and full coverage and five-connectivity under different ratios of sensor communication range (denoted by Rc) over sensing range (denoted by Rs) for wireless sensor networks (WSNs). We also discover that there exists a hexagon-based universally elemental pattern which can generate all known optimal patterns. The previously proposed Voronoi-based approach can not be applied to prove the optimality of the new patterns due to their special features. We propose a new deployment-polygon based methodology, and prove their optimality among regular patterns when Rc/Rs ≥ 1. We conjecture that our patterns are globally optimal to achieve full coverage and three-connectivity, and full coverage and five-connectivity, under all ranges of Rc/Rs. With these new results, the set of optimal patterns to achieve full coverage and k-connectivity (k≤6) is complete, for the first time.

Recent research shows that significant energy saving can be achieved in wireless sensor networks with a mobile base station that collects data from sensor nodes via short-range communications. However, a major performance bottleneck of such WSNs is the significantly increased latency in data collection due to the low movement speed of mobile base stations. To address this issue, we propose a rendezvous-based data collection approach in which a subset of nodes serve as the rendezvous points that buffer and aggregate data originated from sources and transfer to the base station when it arrives. This approach combines the advantages of controlled mobility and in-network data caching and can achieve a desirable balance between network energy saving and data collection delay. We propose two efficient rendezvous design algorithms with provable performance bounds for mobile base stations with variable and fixed tracks, respectively. The effectiveness of our approach is validated through both theoretical analysis and extensive simulations.

Cyber-physical systems (CPS) is a new generation of engineered systems that integrate physical systems with the capability of networked computing and control. Real-time video capture and communication is expected to be an important function in many cyber-physical systems that involve camera-equipped mobile phones. In this paper, we present AnySense, a network architecture that supports video communication between 3G phones and Internet hosts in cyber-physical systems. AnySense implements transcoding of video streams between the Internet and circuit-switched 3G cellular networks, and is transparent to 3G service providers. AnySense can support a class of ubiquitous cyber-physical systems that require video-based information collection and sharing. A prototype of AnySense has been built and a video demo is available at http://www.anyserver.org/.

Recent research shows that significant energy saving can be achieved in wireless sensor networks by using mobile elements (MEs) capable of carrying data mechanically. However, the low movement speed of MEs hinders their use in data-intensive sensing applications with temporal constraints. To address this issue, we propose a rendezvous-based approach in which a subset of nodes serve as the rendezvous points (RPs) that buffer data originated from sources and transfer to MEs when they arrive. RPs enable MEs to collect a large volume of data at a time without traveling long distances, which can achieve a desirable balance between network energy saving and data collection delay. We develop two rendezvous planning algorithms, RP-CP and RP-UG. RP-CP finds the optimal RPs when MEs move along the data routing tree while RP-UG greedily chooses the RPs with maximum energy saving to travel distance ratios. We design the rendezvous-based data collection protocol that facilitates reliable data transfers from RPs to MEs in presence of significant unexpected delays in ME movement and network communication. Our approach is validated through extensive simulations.

In this letter, a network-coded cooperation scheme with dynamic coding mechanism (DC-NCC) is proposed. In DC-NCC, the relay dynamically adapts forming the network-coded data based on the observed instantaneous source-to-relay channel quality, and then forwards the network-coded data towards corresponding destinations. Under the assumption (denoted as A) that each destination can reliably overhear the data from other sources, the diversity-multiplexing tradeoff of DC-NCC is proved to outperform that of conventional cooperation (CC), This verifies that DC-NCC outperforms CC in bandwidth efficiency. Moreover, DC-NCC offers reduced system outage probability and single-pair outage probability compared with CC, and achieves the same full diversity order as CC at high signal-to-noise ratio (SNR). Numerical results also show that with dynamic coding, DC-NCC avoids unnecessary error propagation which can be caused by coding those erroneous data into network-coded data. Finally the performance of DC-NCC is discussed in case the assumptionAis removed. For a wireless network composed ofNsource-destination (s-d) pairs and a single relay node, although there is a certain outage probability increase for eachs-dpair, DC-NCC still achieves a diversity-multiplexing tradeoff superior to CC.

In this paper, we propose a novel protocol called VC-MAC that utilizes the concept of cooperative communication tailored for vehicular networks, especially for gateway downloading scenarios. VC-MAC leverages the broadcast nature of the wireless medium to maximize the system throughput. The spatial and user diversity are exploited by the concurrent cooperative relaying to overcome the unreliability of the wireless channel in vehicular networks. We theoretically analyze the selection of optimal relay set using weighted independent set (WIS) model, and then design a back-off mechanism to select the concurrent relays in a distributed manner. Extensive simulations in ns-2 are carried out to demonstrate that compared with existing strategies, VC-MAC effectively enhances cooperative information downloading and significantly increases the system throughput.

This paper addresses the problem of the stability of congestion control for networks with large round-trip communication delays. Nearly all the existed AQM schemes neglect the impact on system behavior, such as stability, robustness and convergence, caused by large communication delay. The drastic queue oscillations in large delay networks of PI, REM and DC-AQM decrease the link utilization and introduce the avoidable delay jitter. To address this problem, we develop a robust IMC-PID congestion controller based on internal model control principle to restrict the negative impact on the stability caused by the large delay. Simulation results demonstrate that the integrated performance of proposed scheme outperforms others as communication delay increases, and achieves high link utilization and small delay jitter.

Mobile sinks can be a viable solution to solve the problem that energy consumption is not balanced for all the sensor nodes in wireless sensor networks (WSNs). Frequent location updates from the sink (a current possible solution) can lead to both rapid energy consumption of the sensor nodes and increased collisions in wireless transmissions. This paper proposes a "local update-based routing protocol (LURP)" to help resolve this problem. When the sink node moves, it only needs to broadcast its location information within a local area rather than among the entire network. Both theoretical analysis and simulation studies show that LURP consumes less energy in each sensor node and also decreases the collisions in wireless transmissions, which can be used in large-scale WSNs.

Research shows that significant energy saving can be achieved in wireless sensor networks by using mobile elements (MEs) capable of carrying data mechanically. However, the low movement speed of MEs hinders their use in data-intensive sensing applications with temporal constraints. To address this issue, we propose a rendezvous-based approach in which a subset of nodes serve as the rendezvous points (RPs) that buffer data originated from sources and transfer to MEs when they arrive. RPs enable MEs to collect a large volume of data at a time without traveling long distances, which can achieve a desirable balance between network energy saving and data collection delay. We develop two rendezvous planning algorithms, RP-CP and RP-UG. RP-CP finds the optimal RPs when MEs move along the data routing tree while RP-UG greedily chooses the RPs with maximum energy saving to travel distance ratios. We design the rendezvous-based data collection protocol that facilitates reliable data transfers from RPs to MEs in presence of significant unexpected delays in ME movement and network communication. Our approach is validated through extensive simulations.

We propose to demo a ubiquitous surveillance patrol robot system which can patrol in a candidate site to perform events detection where a wireless sensor network may be deployed. We have enabled the 3G phone controlled patrol robot (over 3G circuit switched network) with integrated access to the WiFi/Internet. Internet is used to provide sensor query, to send control signal to the robot and to request the real time audiovisual data from the robot. The robot can receive the movement instructions from and pull the real-time multimedia data stream to a remote user via WiFi laptop or 3G terminal. We also implemented a gateway which is a key component for the platform in responsible for the interconnection and heterogeneous communication of the networks.

The IEEE 802.16a standard defines WiMax mesh network, using the base station (BS) as a coordinator for the centralized scheduling. This paper proposes a centralized scheduling algorithm for WiMax mesh networks. In our scheme, each node has one transceiver and can be tuned between multiple channels, intending to eliminate the secondary interference for reducing the length of scheduling. We first study the problem when sufficient channels are supported, then extend our solution to the case with insufficient number of channels. Both the scheduling algorithm and the channel assignment strategies are included. The simulation results show that the multi-channel single-transceiver MAC can reduce the length of scheduling substantially as compared with the single channel system, and double channel may provide a performance similar to the multiple channels.

In multi-rate 802.11 wireless LAN, when the wireless node with lower data rate dominates shared channel usage time, the throughput experienced by other nodes transmitting at higher data rates will be drastically reduced. While most fair AQM (active queue management) algorithms are based on throughput fairness, we prove that temporal fair AQM algorithm can achieve more total throughput than throughput fair AQM algorithm in multi-rate 802.11 WLAN. The authors propose a temporal fair AQM-TFRED (temporal fair RED) on AP (access point) in multi-rate 802.11 WLAN. Taking into account the transmission rate of each flow, TFRED sets different drop probability for each flow going through the AP to guarantee equal channel usage time for each wireless node. Analysis and simulation results show that, compared with throughput fair AQM algorithms, TFRED achieves temporal fairness, per-flow throughput protection, and a 40% increase in total throughput.

The node mobility makes it difficult to guarantee a high reliability of data delivery for existing multicast routing protocols, but it is a major concern in protocol design for mobile ad-hoc networks (MANETs). We propose a novel scalable and reliable multicast protocol based on clustering technique and gossip methodology. Local retransmission and gossip-based mechanisms (LRG) are combined to provide the high reliability of data delivery. Dynamical changes of gossip probability and gossip scope upgrade the spreading ratio of messages and they make the protocol adaptive to the rapid changes of network environment. The proposed protocol is compared with the well-known route driven gossip (RDG) protocol with regard to communication overhead, reliability, and latency. Theoretical analysis and simulation studies show that LRG is reliable and scalable in MANETs.

Energy consumption is a major issue when designing routing protocols in wireless sensor networks. We propose a novel hole avoiding in advance routing protocol (HAIR) to address this issue. In the proposed protocol, a data packet can avoid meeting a "hole" in advance instead of bypassing a hole when it meets the hole as existing hole avoiding re-routing protocols (HARR) do. We prove that the proposed protocol can always find a routing path between a given source node and the sink if such a routing path does exist in the network. Simulation studies show that the proposed HAIR protocol constructs routing paths with shorter routing distance and less energy consumption in comparison with the existing HARR protocols. The proposed protocol can be used in large-scale wireless sensor networks.

In IEEE 802.16 networks, initial ranging is a primary and important procedure of connection setup between subscriber stations and base station. The mandatory method defined in the standard of contention resolution is based on a truncated binary exponential backoff, with a fixed initial contention window size. However, the original algorithm neglects the possibility that the number of actively contending stations may change dynamically over time, leading to dynamically changing contention intensity. The major contribution of this paper is twofold: 1) we propose an accurate analytical model to analyze the performance of initial ranging requests in IEEE 802.16 networks. Two metrics, connection probability and average connection delay, are investigated to evaluate the network performance; 2) based on the above analysis, we propose an efficient performance improvement method by using dynamic window adjustment for initial ranging. Unlike the standard algorithm, this algorithm automatically adjusts the initial contention window to an optimal trade-off point between connection probability and connection delay. The performance revels that improving the service capacity and buffer size of base station can optimize the connection probability and the average connection delay. The numerical results also show that the optimal contention window adjustment outperforms the algorithm in the standard.

Recently, IEEE has standardized the 802.16 protocol for wireless metropolitan area networks. In the point-to-multipoint (PMP) mode of the medium access control (MAC), the subscriber stations (SSs) request bandwidth based upon unicast, multicast and broadcast polling by the base station (BS). Polling is the process by which the BS allocates to the SSs bandwidth specifically for the purpose of making bandwidth requests. The SSs contend for the transmission opportunities which are allocated by the BS when they want to transmit the bandwidth request. Generally speaking, the contention intensity heavily influences the network performance. However, no attention is paid to investigate the impact of the system parameters to the utilization of transmission opportunity. This paper provides a simple, but nevertheless accurate, analytical model to compute the utilization of transmission opportunity, in the assumption of finite number of subscriber stations and ideal channel conditions. The proposed analysis applies to multicast and broadcast polling mechanisms. The analytical results match with the simulation results very well and the performance evaluation reveals that the utilization of transmission opportunity mainly depends on the initial contention window size, the network size and the average number of transmission opportunities in one frame. The simulation data also show that multicast polling can achieve higher utilization than broadcast polling especially when the network size is large.

Abstract—Many wireless sensor network (WSN) applications demand secure communications. The random key predistribution ðRKPÞ protocol has been well accepted in achieving secure communications in WSNs. A host of key management protocols have been proposed based on the RKP protocol. However, due to the randomness in key distribution and strong constraint in key path construction, the RKP-based protocols can only be applied in highly dense networks, which are not always feasible in practice. In this paper, we propose a methodology called network decoupling to address this problem. With this methodology, a WSN is decoupled into a logical keysharing network and a physical neighborhood network, which significantly releases the constraint in key path construction of the RKP protocol. We design two new key management protocols, that is, RKP-DE and RKP-DEA, as well as a set of link and path dependency elimination rules in decoupled sensor networks. Our analytical and simulation data demonstrate the performance enhancement of our solutions from the perspective of connectivity and resilience and its applicability in nonhighly dense sensor networks. Index Terms—Wireless sensor networks, random key predistribution, network decoupling. 1

This paper proposes a novel and simple adaptive control algorithm for the effective delay control and resource utilization of end host multicast (EMcast) when the traffic load becomes heavy in a multigroup network with real-time flows constrained by (sigma, rho) regulators. The control algorithm is implemented at the overlay networks and provides more regulations through a novel (sigma, rho, lambda) regulator at each group end host who suffers from heavy input traffic. To our knowledge, it is the first work to incorporate traffic regulators into the end host multicast to control heavy traffic output. Our further contributions include a theoretical analysis and a set of results. We prove the existence and calculate the value of the rate threshold rho* such that for a given set of K groups, when the average rate of traffic entering the group end hosts rho macr > rho*, the ratio of the worst-case multicast delay bound of the proposed (sigma, rho, lambda) regulator over the traditional (sigma, rho) regulator is O(1/Kn) for any integer n. We also prove the efficiency of the novel algorithm and regulator in decreasing worst-case delays by conducting computer simulations.

In most of the proposed clustering algorithms for wireless ad hoc networks, the cluster-heads form a dominating set in the network topology. A variant ofdominatingsetwhich is more suitable for cluster formation is theweakly-connecteddominatingset(WCDS). We propose an area based distributed algorithm for WCDS formation with time and message complexityO(n). In thisAreaalgorithm, we partition the wireless nodes into different areas, use some deterministic criteria to select the nodes for the WCDS in each area and adjust the area borders by adding additional nodes to the final WCDS. The effectiveness of our algorithm is confirmed through analysis and comprehensive simulation study.

This paper proposes a novel adaptive algorithm based on a new soft-regulator (sigma, rho, lambda) for concurrent multimedia flows at end hosts. Our algorithm has the following features: (1) does requires the support of the network layers (routers and switches); (2) is scalable as it can be installed into any intermediated nodes for the delay control; (3) uses network resource efficiently, in particular, when the bandwidth of network is very limited. Performance experimental data have shown that (sigma, rho, lambda) and related algorithm are efficient and applicable as compared with network layer solutions

In this paper, we propose an accurate analytical model to analyze the performance of initial ranging requests in IEEE 802.16 networks. Two metrics, connection probability and average connection delay, are investigated to evaluate the network performance. Performance observation demonstrate that the connection probability is not heavily influenced by the contention window size and reconnection retry limitation but the average connection delay is sensitive to the above two parameters. Moreover, we find that improving the service capacity and buffer size of base station can optimize the connection probability and average connection delay

We propose an efficient centralized scheduling algorithm in IEEE 802.16 based wireless mesh networks (WMN) to provide high qualified wireless multimedia services. Our algorithm takes special attention on the relay function of the mesh nodes in a transmission tree which is seldom studied in previous research. Some important design metrics, such as fairness, channel utilization and transmission delay are considered in this scheduling algorithm. IEEE 802.16 employs TDMA and the selection policy for scheduled links in a time slot will definitely impact the system performance. We evaluated the proposed algorithm with four selection criteria through extensive simulations and the results are instrumental for improving the performance of IEEE 802.16 based WMNs in terms of link scheduling

This paper introduces a multi-million project that intends to design and implement a novel intelligent platform-AnyServer-for providing the ubiquitous communication (video/audio streaming and data transmission) for mobile nodes terminals/handsets to connect and communicate. AnyServer, as the platform, will provide the inter-operability between the different networks, attacking with the following implementation/design objectives and challenging issues: 1) WiFi and 3G-324M multimedia transmission protocols stack, 2) server and networking functions will be implemented to harness a set of servers of call server, media gateway, signaling gateway, and subscriber location registration, 3) adaptive soft-switching package to support mobile units routing and roaming the heterogeneous networks

This paper proposes an efficient algorithm for P2P file sharing systems based on WiMAX mesh mode and Wi-Fi technologies. Wireless networks in our system are hierarchically divided into three layers: Wi-Fi based wireless local area networks under a subscriber station; the mesh network of subscriber stations under a base station; the network of base stations. File lookup procedure may go through three steps: the requesting end host firstly looks up for the requested file within it's neighbor end hosts under the same subscribe station; if fails, then it sends lookup messages to the nearest subscriber stations in the mesh network following to a changed dynamic source routing protocol; if both the steps fail, then the requesting end host searches the requested file through the chord-based base stations' overlay. By statistical analysis and simulations, we prove that our layered P2P file sharing system can reduce the number of lookup messages in physical networks and prevent over expenses of precious bandwidth in wireless metropolitan area networks

This paper presents a novel analytical model and an efficient admission control algorithm for IEEE 802.11 DCF access mechanism. In contrast to the previous approaches that only analyzed the saturated status of IEEE 802.11 networks, both saturated and unsaturated states of network are analyzed and the impacts of error-frame rate and retransmission limit are also taken into account based on an improved Markov chain model. Taking the throughput difference between saturated and unsaturated states as the residual bandwidth, an efficient admission control algorithm is designed to utilize the network resources effectively. Extensive simulation data demonstrate that the admission control algorithm is efficient and can make the effective utilization of network resources

In order to provide accurate and real-time bandwidth information and enhance the QoS for bandwidth-sensitive applications in a dynamically changing wireless network, the paper proposes an efficient method for wireless bandwidth detection (WBD) using a packet probing approach on mobile nodes. The novelty and contributions of WBD are three-fold: (1) efficiency - by sending probe packets of various sizes, WBD can determine the wireless link bandwidth with light load and short time duration; (2) accuracy - using different mechanisms to filter out the random time variation, a high detection accuracy can be achieved; (3) stability - our algorithm attains stable results under different cross traffic conditions. Experimental data observed through extensive simulations shows the effectiveness and efficiency of WBD.

To efficiently manage ad hoc networks, this paper proposes a novel distributed algorithm for connected dominating set (CDS) formation in wireless ad hoc networks with time and message complexity O(n). This Area algorithm partitions the nodes into different areas and selectively connects two dominators that are two or three hops away. Compared with previous well-known algorithms, we confirm the effectiveness of this algorithm through analysis and comprehensive simulation study. The number of nodes in the CDS formed by this Area algorithm is up to around 55% less than that constructed by others.

Bluetooth is a promising local area wireless technology designed to establish both personal area and multihop ad hoc networks. In this paper, we present BluePower as a novel and practical distributed scheme for building large multihop scatternets based on device transmitting power. The protocol is executed at each node with no prior knowledge of network topology and constructs the topology simply by enabling each node to alternate between scatternet formation and communication. Different from existing solutions, our design integrates device mobility and network self-healing from partitions with topology formation. Besides, partial loop detection is applied to minimize the redundant links in the network. The simulation results show that BluePower has low scatternet formation latency, decent number of slaves per piconet, short average route length and small connection delay for nodes to establish the first communication links in dynamic environments.

This paper presents a novel end host multicast protocol (DSM) on top of the mesh overlays for P2P networks. Unlike the previous schemes, the major feature ofDSM is that it does not need a multicast tree for routing the multicast messages. DSM can implement the scalable and efFcient multicast communications in a fully distributed way and consists of the following algorithms: I . cluster formation that forms the group into clusters and each cluster consists of a small number of members; 2. cluster core selection that selects a core for each cluster who has the minimum sum of overlay hops to all other cluster members; 3. multicast routing that includes the balanced RA routing approach which enables a cluster core to construct the connections with other cores dynamically by using the rectangle area and the balanced forwardjlooding scheme in each cluster which evenly distributes the data traf- Jic and link stress among the links in the multicast system. Simulation results show that DSM is fully distributed, scalable and efjicient as compared with some well-known end host multicast system.

We study a scalable and efficient end host multicast-DSCT (dynamic shared cluster tree) for P2P systems in this paper. In our design, we focus on making full use of underlying physical properties to improve both scalability and efficiency as data multicast in the overlay topologies of P2P systems. In this paper, we present the DSCT architecture, the dynamic scheme to select cluster cores according to the instantaneous network situations. These are all the key points to construct the basic DSCT (BDSCT) multicast. Through further studies, we provide DSCT improvement design (IDSCT) to make it more suitable for multiple-source P2P communications. Our simulation results indicate that it is possible to improve the scalability and efficiency of end host multicast in P2P systems by using our DSCT designs.

We develop a very simple mathematical model to investigate the upper bound of the size of limited choices in our proposed QoS path selection algorithm called the largest widest shortest path among limited choices (LWSP-LC) for engineering design. The LWSP-LC can achieve load balancing effectively and reduce path searching complexity significantly. However, its performance is highly related to many factors, including the network environment and traffic condition. Our simulation results show that the size of limited choices is highly dependent on both the network size and network connectivity. By considering a fully connected network, we derive a simple mathematical model on the upper bound of the size of limited choices based on the network size only.

Connected dominating set based routing is a promising approach for enhancing the routing efficiency in wireless ad hoc networks. However, finding the minimum dominating set in an arbitrary graph is a NP-hard problem. We propose a simple and efficient distributed algorithm for constructing a connected dominating set in wireless ad hoc networks with time complexity O(n) and message complexity O(nlog n). The dominating set generated with our algorithm can be more reliable and load balanced for routing as compared with some well-known algorithms. The simulation results demonstrate that our algorithm outperforms previous work in terms of the size of the resultant connected dominating set.

Anycasting has recently become an important research topic, especially for replicated servers. With anycasting, applications can request the "nearest" server for provision of desired (multimedia) service. In this paper, we study efficient distributed admission control (DAC) for anycast flows. We focus on algorithms that perform destination selection and efficient path establishment. Taking advantage of anycasting, our distributed algorithms differ from each other in their dependence on system status information. Performance data obtained through mathematical analysis and simulations show that, in terms of admission probabilities, DAC systems that are based on local status information have performance levels close to those that utilize global and dynamic status information. This renders our DAC algorithms useful not only for the network layer, but also for the application layer admission control for anycast flows.

This paper presents a new method to control the delay performance for high rate heterogeneous real-time traffic flows based on a novel traffic control algorithm which is a generalization of traditional (/spl sigma/, /spl rho/) regulator. Our new control algorithm operates like the traditional regulator under the normal loading situation, but provides more regulation for the high rate (heavy load condition) of the traffic. For a set of heterogenous real-time traffic flows R we can show that D/sub r/(R) /spl les/ D(R) where D/sub r/(R) and D(R) are the worst-case delay bounds with our new control algorithm and that with (/spl sigma/, /spl rho/) regulator respectively. More specifically, we develop a set of formula that can be used to set the parameters in our new traffic controller so that the worst case delay bound is minimized by streaming the traffic flow. We can prove that there exists a minimum (average) input rate p* such that D/sub r/(R) = D(R) for /spl rho/ /spl les/ /spl rho/* and D/sub r/(R)/spl rho/*. Using the extended regulator can effectively control the delay when the average heterogeneous traffic rate is high. The issues are particularly useful for Integrated Services where a flow may over claim its share of resource and for Differentiated Services where a class of traffic flows may possess very high rates.

Define anycast services as a group of replicated servers that may provide similar or identical services. Using anycast services can significantly simplify some applications such as to seek appropriate servers to provide quality of service and to achieve the load balance and fault-tolerance for service availability. An anycast flow is a sequence of packet that can be established between a user and any server in an anycast (replicated) service group. We study a set of efficient distributed connection setup algorithms for real-time anycast flows. Given an anycast flow between a server j and a request node s with end-to-end deadline D/sub s,j/ and minimum bandwidth requirement B/sub s,j/, our algorithms can effectively seek multiple destination connections in parallel thus the best path which satisfies the requirements of the anycast flow is chosen. The deterministic approach for worst delay bound analysis is also given.

Anycast is a communication model for IP and is more important for an ad-hoc network in terms of resource, robustness and efficiency. AODV is a simple and efficient routing protocol designed specifically for use in multi-hop wireless ad hoc networks of mobile nodes. In this paper, we propose an anycast protocol for IP flow in mobile ad hoc networks, which is based on the AODV protocol. We also test its performance with different network parameters and the simulation results show that our anycast protocol can balance the network load efficiently, reduce the delay of packet and improve the network throughput. Anycast service can also improve the performance of ad hoc network when mobility is high and links get disconnected frequently.

Link-state routing protocols, such as OSPF and IS-IS, are widely used in the Internet today. In link-state routing protocols, global network topology information is first collected at each node. A shortest path tree (SPT) is then constructed by applying Dijkstra's shortest path algorithm at each node. Link-state protocols usually require the flooding of new information to the entire (sub)network after changes in any link state (including link faults). Narvaez et al. proposed a fault-tolerant link-state routing protocol without flooding. The idea is to construct a shortest restoration path for each unidirectional link fault. Faulty link information is distributed only to the nodes in the restoration path and only one restoration path is constructed. It is shown that this approach is loop-free. However, the Narvaez et al. approach is inefficient when a link failure is bidirectional because a restoration path is unidirectional and routing tables of nodes in the path are partially updated. In addition, two restoration paths may be generated for each bidirectional link fault. In this paper, we extend the Narvaez et al. protocol to efficiently handle a bidirectional link fault by making the restoration path bidirectional. Several desirable properties of the proposed extended routing protocol are also explored. A simulation study is conducted to compare the traditional link-state protocol, the source-tree protocol, the Narvaez et al. unidirectional restoration path protocol, and the proposed bidirectional restoration path protocol.

Novel maximal coding and maximal prefix coding are introduced. We show that for finite source alphabets all Huffman codes are optimal maximal codes and optimal maximal prefix codes. Conversely, optimal maximal codes or optimal maximal prefix codes need not to be Huffman codes. For any maximal prefix code C, however, there exists some information source I such that C is exactly a Huffman code for I. And, for any maximal code C, there exists some information source I such that C is equivalent to a Huffman code for I. In other words, the class of Huffman codes coincides with the one of maximal prefix codes or maximal codes. Additionally, a case study of data compression is investigated. The optimal maximal coding and maximal prefix coding are used not only for statistical modeling but also for dictionary methods. Finally, it is proven that given an original file and a corresponding encoded file by the maximal prefix coding, the complexity of guessing the maximal prefix code is NP-complete.

A novel efficient and effective Internet multicast routing protocol is presented with short delay, high throughput, resource utilization and scalability for a single multicast group g. The protocol has two features: (1) multiple shared-trees (MST) are configured to provide efficient, dynamic and quality multicast routing; (2) an anycasting approach is used to form the tree roots into an anycast group so that the multicast packets can be anycast to the nearest node at one of the shared trees to achieve the best routing service for the multicast packets. The performance of the MST protocol is analyzed through extensive simulations and compared with well-known source tree and shared-tree routing.

We study a distributed admission control (DAC) procedure for anycast flows with QoS requirements. We focus on algorithms that perform destination selection, which is critical in anycast. Several algorithms are proposed. These algorithms differ from each other in their dependence on system status information. We also address the issue of resource reservation and re-trial control in the DAC procedure. Performance data obtained by mathematical analysis and computer simulation show that in terms of admission probabilities, DAC systems that are based on local status information can perform closely to those that utilize global and dynamic status information. We note that the latter is much more expensive and difficult to realize.

Link-state routing protocols, such as OSPF and IS-IS, are widely used on the Internet. In link-state routing protocols, global network topology is first collected at each node. A shortest path tree (SPT) is then constructed by applying Dijkstra's shortest path algorithm at each node. Link-state protocols normally require the flooding of new information to the entire (sub)network after changes in any link state (including link faults). Narvaez et al. (2000) proposed a fault-tolerant link-state routing protocol without flooding. The idea is to construct a shortest restoration path for each uni-directional link fault. Faulty link information is distributed only to the nodes in the restoration path and only one restoration path is constructed. It is shown that this approach is loop-free. However, the approach of Narvaez et al. is inefficient when a link failure is bi-directional, because a restoration path is uni-directional and routing tables of nodes in the path are partially updated. In addition, two restoration paths may be generated for each bi-directional link fault. We extend the Narvaez protocol to efficiently handle a bi-directional link fault by making the restoration path bi-directional. Several desirable properties of the proposed extended routing protocol are also explored.

A novel efficient and dynamic integrated routing protocol for multicast and anycast messages is presented. The contributions of the protocol differ from well-known shared-tree systems in two aspects: (1) Off-tree anycast configuration and routing: multicast sources use anycast routing to select a better path from the source to one router in the group in order to avoid congestion or any fault in the network. (2) On-tree router anycast configurations: The nodes in the shared-tree are formed into a virtual anycast group. The shared-tree approach is extended with capability of a group cores (anycast group). The simulation data demonstrates the efficiency of the protocol.

We consider the problem of designing rollback error recovery algorithms for dynamic, wide area distributed systems like the Internet. The characteristics and the scale of such a system complicate the design and performance of the algorithms. Traditional message passing based algorithms incur large overhead, in both the network traffic and message passing delay, in such a wide-area environment. In this paper, we propose a novel approach to designing checkpointing and rollback algorithms using mobile agents as an aid. Using mobile agent leads to a reduction of the total amount of communication and allows us to design algorithms that take the advantage of the most up to date system information for decision making. It also allows us to develop algorithms implementing flexible and adaptive policies. A mobile agent enabled hybrid algorithm combining independent and coordinated checkpointing is proposed. A prototype of the algorithms is developed using IBM's Aglets. Results of performance evaluation are presented and discussed.

Recent experimental research has revealed that the nature of WWW traffic is self-similarity (M.E. Crovella and A. Bestavros, 1997). That is, the behavior of WWW traffic is well modeled by second-order self-similar processes with long-range dependence. A closed form of autocorrelation functions about asymptotically self-similar processes is presented. The verification shows that this form is best for real traffic data on an Ethernet.

The relevant concepts of the most efficient uniquely decipherable and maximal uniquely decipherable encoding schemes similar to highly efficient instantaneous encoding schemes are introduced. We show that the Huffman encoding schemes are the most efficient possible among both all uniquely decipherable encoding schemes and all maximal uniquely decipherable encoding schemes, respectively. Generally, decoding messages encrypted by a uniquely decipherable encoding scheme is more difficult than decoding messages encrypted by an instantaneous encoding scheme. However, from the security point of view, it is much more secure to apply a uniquely decipherable encoding scheme than to apply an instantaneous encoding scheme to an information system.

We present an improved algorithm for the Vertex Cover problem on graphs of degree bounded by 3 (3DVC). We show that the 3DVC problem can be solved in time O(1.2192kk), where k is the number of vertices in a minimum vertex cover of the graph. Our algorithm also improves previous algorithms on the Independent Set problem on graphs with degree bounded by 3. © 2000 John Wiley & Sons, Inc.

An anycast packet is one that should be delivered to one member in a group of designated recipients. Using anycast services may considerably simplify some applications. Little work has been done on routing anycast packets. In this paper, we propose and analyze a routing protocol for anycast message. It is composed of two subprotocols: the routing table establishment subprotocol and the packet forwarding subprotocol. In the routing table establishment subprotocol, we propose four methods (SSP, MIN-D, SET, and GET) for enforcing an order among routers for the purpose of loop prevention. These methods differ from each other on information used to maintain orders, the impact on QoS, and the compatibility to the existing routing protocols. In the packet forwarding subprotocol, we propose a Weighted-Random Selection (WRS) approach for multiple path selection in order to balance network traffic. In particular, the fixed and adaptive methods are proposed to determine the weights. Both of them explicitly take into account the characteristics of distribution of anycast recipient group while the adaptive method uses the dynamic information of the anycast traffic as well. Correctness property of the protocol is formally proven. Extensive simulation is performed to evaluate our newly designed protocol. Performance data shows that the loop-prevention methods and the WRS approaches have great impact on the performance in terms of average end-to-end packet delay. In particular, the protocol using the SET or CBT loop-prevention methods and the adaptive WRS approach performs very close to a dynamic optimal routing protocol in most cases.

In this paper, we study an efficient fault-tolerant CBT multicast routing protocol. With our strategy, when a faulty component is detected, some pre-defined backup path(s) is (are) used to bypass the faulty component and enable the multicast communication to continue. Our protocol only requires that routers near the faulty component be reconfigured, thus reducing the runtime overhead without compromising much of the performance. Our performance evaluation shows that our new protocol performs nearly as well as the best possible global method while utilizing much less runtime overhead and implementation cost.

A novel and efficient protocol based on a single logical ring for multicast communication among a group of processes is presented. The senders and receivers are merged in the same group and this peer group reflects a cooperative (mirror) group of information servers. The protocol maintains consistency in the group by using two strategies. First, by placing a total sequence number in each of the multicast messages, it guarantees total ordering of message delivery for each member. Second, in contrast to other ring protocols which are based on real token passing, it uses a virtual token and achieves message atomicity by using up to n point-to-point control messages. Since no real token passing messages are rotating on the ring, the position of the token holder is calculated by using a logical clock located in each of the processes. The protocol can tolerate communication faults, process crash failures and network partitioning. The protocol has been implemented and experimental results show that the protocol achieves satisfactory performance.

In this paper, we design and analyze an efficient fault-tolerant multicast routing protocol. Reliable multicast communication is critical for the success of many Internet applications. Multicast routing protocols with core-based tree techniques (CBT) have been widely used because of their scalability and simplicity. We enhance the CBT protocol with fault tolerance capability and improve its efficiency and effectiveness. With our strategy, when a faulty component is detected, some pre-defined backup path(s) is (are) used to bypass the faulty component and enable the multicast communication to continue. Our protocol only requires that routers near the faulty component be reconfigured, thus reducing the runtime overhead without compromising much of the performance. Our approach is in contrast to other approaches that often require relatively large tree reformation when faults occur. These global methods are usually costly and complicated in their attempt to achieve theoretically optimal performance. Our performance evaluation shows that our new protocol performs nearly as well as the best possible global method while utilizing much less runtime overhead and implementation cost.

We propose and analyze three routing algorithms for anycast packets: source-destination based routing with weighted random selection (SD/WRS); destination based routing with weighted random selection (D/WRS); and the shortest shortest path first (SSPF) algorithms. The SSPF algorithm is a simple extension to the traditional SPF algorithm for routing unicast packets. The SD/WRS and D/WRS algorithms explicitly take into account characteristics of anycast message traffic and its recipient group. As a result, our simulation study shows that both the SD/WRS and D/WRS algorithms perform much better than SSPF in terms of average end-to-end packet delay. In particular, SD/WRS performs very close to a dynamic optimal algorithm in most cases. Our algorithms are simple, efficient and compatible with the most of existing routing technologies. We also formally prove the loop free and correctness properties for our algorithms.

An efficient multicast protocol is presented by using a heuristic function for passing a virtual token to decide the total message ordering. Unlike existing token-passing based algorithms, there is no physical token-passing message in the protocol. Instead, the token holder piggybacks token passing on to normal multicast messages. Executing the heuristic function does not incur any communication overhead and only relies on the local information of a token holder. For a group of n processes and k multicast messages, the protocol is able to achieve atomicity of one message by an average of (n-2)/k point-to-point control messages. Dynamic membership is non-blocking, i.e. each individual process in the group can take unilateral decisions at each step of the membership algorithms. System-wide consistent group configuration can be obtained in a consistent order of normal multicast messages. The performance of the protocol is shown by implementation figures.

A reliable multicast protocol (RMP) based on a logical token ring approach can achieve agreement of a group of operational processes in distributed systems. The structure of RMP is modulated into component protocols that achieve total ordering, atomicity of multicast messages; dynamic group configuration and fault-tolerance cooperatively. RMP uses a virtual token to order multicast messages in a logical process ring. It is highly efficient over networks and its algorithm design and implementation are presented. The use of a state machine approach simplifies this complex system implementation. Experience and lessons drawn from RMP and general techniques applied to group communication protocol are also described. © 1997 John Wiley & Sons, Ltd.

A novel and efficient group communication (multicast) protocol, based on a single logical-clock token ring approach, is described. The protocol is highly efficient and it guarantees total ordering and atomicity of multicast messages for asynchronous distributed systems. Unlike other logical token-ring algorithms, the protocol does not have a problem of token loss. The optimized fault-tolerant algorithms of the protocol can handle process failures and network partitioning. The experiment results of the implemented protocol in a local area network of workstations have demonstrated that its performance is better than any existing solutions in the same environment, especially, for achieving message total ordering and atomic (safe) delivery.

Maintaining a consistent state is crucial for cooperative work in autonomous and decentralized systems (ADS). This is achieved through reliable communication protocols in a group of autonomous components. Many existing protocols are quite expensive and not very suitable for the ADS. The paper presents a logical token ring based group multicast protocol for the service of decentralized action manage systems. Unlike previous token based algorithms, we assign a virtual identity to each of the processes executing in the autonomous components in an increasing order so as to form a logical ring. The protocol ensures total ordering and atomicity of multicast messages in the presence of the component failures. The total order is achieved by placing a sequence number in each multicast message. A token process implicitly multicasts one such ordered message and transfers the token to the next process on the ring, hence, reducing the synchronous overhead and preventing possible token lose problem. With a minimal number of control messages, the protocol guarantees that all nonfaulty processes belonging to the same group will observe a consistent view of ordered events affecting all cooperative components in the group. The efficient recovery algorithms can handle the member failures, dynamic groups, group partitions and merges.>

Abstract The paper presents the design and implementation of a CSP‐based object‐oriented system. The system consists of a specification model, Communicating‐object, and a prototype system, C‐OBJECT, supporting the model. The objects execute in a set of parallel processes called actions. The dynamic communicating objects exchange messages by both data transmissions and function invocations. The C‐OBJECT prototype is constructed in a MIMD architecture (32‐node transputer) with C++ which is composed of two parts: network configuration and a Communicating‐object service subsystem (library) providing various levels of message‐passing primitives. The initial prototype with good performance has shown its availability for C and C++ programming. The integrated system facilitates application software with tools of specification, design and implementation.