High-scale image super-resolution (SR) has become increasingly important with the rapid growth of mobile devices and high-resolution displays. However, current SR methods primarily focus on lower scales and generalize poorly to high-scale scenarios due to severe information loss and complex real-world degradations. In this paper, we propose a novel Selective Diffusion Distillation (SDD) framework for real-world high-scale SR, which distills reliable knowledge from a low-scale diffusion teacher to a high-scale student. Specifically, considering severe information loss in high-scale inputs, directly distilling from low-scale models may result in feature misalignment. To address this, we introduce a Degradation-aware Metric Learning (DML) approach to align feature distributions across different degradation levels. In addition, since the diffusion-based teacher may hallucinate artifacts in ambiguous regions, blindly imitating these unreliable outputs can degrade the student’s fidelity. To tackle this, we propose a Region-aware Selective Distillation (RSD) strategy to filter out uncertain predictions and adaptively supervise only on reliable areas. To evaluate the effectiveness of our method, we introduce Real-UltraSR, a new real-world benchmark that contains diverse high-scale LR-HR pairs, including x8, x10, x12, and x14. Extensive experiments demonstrate that our SDD framework achieves state-of-the-art performance across multiple benchmarks.

Packet classification is a fundamental operation in modern network systems, playing a central role in traffic management, security enforcement, and policy execution. While traditional algorithms have achieved low lookup latency in static scenarios, emerging applications impose more demanding requirements—not only fast lookup, but also high-frequency online updates and strong scalability. Existing approaches often struggle to handle large rule sets or support rules with an increasing number of matching fields, making them unsuitable for dynamic and large-scale network environments.In this work, we propose TupleChain for fast on-line update table lookup with multifaceted scalability.We group rules based on their masks, each maintained using a hash table, and explore the connections among rule groups to skip unnecessary hash probes for faster searches. We show via theoretical analysis and extensive experiments that the proposed scheme offers competitive computational complexity, strong scalability, and high performance in both search and update operations. TupleChain can process millions of packets per second, while simultaneously handling millions of on-line updates per second at the same time, and its lookup speed remains stable even when processing large flow table with 10 million rules or entries containing up to 100 match fields.

Distributed Denial of Service (DDoS) attacks threaten the stability of online services by overwhelming them with excessive traffic. NetFlow-based DDoS detection systems are widely adopted by Internet Service Providers (ISPs) in upstream multi-point detection scenarios to provide robust detection for volumetric DDoS attacks. However, these systems face inherent delays, as NetFlow detection is non-instantaneous—routers aggregate and summarize flow records over a period before reporting, which impacts timely detection. Existing research primarily focuses on optimizing the NetFlow reporting mechanism at the router side. Unfortunately, the need for either software or hardware upgrades for routers would incur a high deployment cost, which is impractical for ISPs in the short term. In this paper, we propose FlowSentry, a novel NetFlow detection framework to accelerate DDoS attack identification at the server side. The system operates on a dual-layer filtering paradigm to handle the high-frequency NetFlow records, incorporating two core technologies: ADWindow and STAnalyzer. ADWindow is a sketch-based sliding window mechanism designed to retain possibly anomalous flow information, filtering out benign flows to reduce the computational overhead. STAnalyzer leverages the cross-router traffic correlation to efficiently infer abnormal growth patterns of potential malicious traffic based on partially reported flow records, thus significantly reducing the detection delay. Our extensive experiments in simulated backbone network environments demonstrate that FlowSentry achieves better detection accuracy while reducing the detection delay by up to 65.63% compared to existing methods.

Existing multi-view image compression methods often rely on 2D projection-based similarities between views to estimate disparities. While effective for small disparities, such as those in stereo images, these methods struggle with the more complex disparities encountered in wide-baseline multi-camera systems, commonly found in virtual reality and autonomous driving applications. To address this limitation, we propose 3D-LMVIC, a novel learning-based multi-view image compression framework that leverages 3D Gaussian Splatting to derive geometric priors for accurate disparity estimation. Furthermore, we introduce a depth map compression model to minimize geometric redundancy across views, along with a multi-view sequence ordering strategy based on a defined distance measure between views to enhance correlations between adjacent views. Experimental results demonstrate that 3D-LMVIC achieves superior performance compared to both traditional and learning-based methods. Additionally, it significantly improves disparity estimation accuracy over existing two-view approaches.

The evolution of mobile networks toward ubiquitous connectivity envisioned by International Mobile Telecommunications-2030 has caused a surge in control plane traffic. A deep understanding of the control plane's internal characteristics and mechanisms is crucial for delivering optimal services. However, existing measurements often neglect the control plane or treat it as an opaque box, focusing on overall performance instead of its intrinsic characteristics.

Exposure correction aims to restore underexposed and overexposed images to normal exposed images in a single network. However, conventional methods primarily focus on correcting non-extreme exposure cases and struggle to accurately restore lightness and structure information in extreme exposure scenarios. Through a thorough investigation, we observe that the extreme exposure correction task is limited by the lack of high-quality benchmark datasets. To address the above challenges, in this paper, we construct the first Extreme Exposure Dataset named EED by manually collecting a large number of diverse scenes. By introducing probabilistic blur kernel, EED not only ensures the rich diversity and brightness distribution of scenes but also approaches the degradation of the real world. To achieve exposure correction in extreme conditions, we propose a novel Extreme Exposure Correction Network by leveraging the mask-aware Fourier transform prior, which decouples lightness and structure components precisely. To restore severe abnormal lightness and lost structure information in extreme exposure scenes, we introduce a well-exposed referenced image to guide the coarse restoration and employ a Timestep-guided Frequency Diffusion Module for further refinement. Extensive experiments demonstrate the superiority of our dataset and method. The dataset will be available at https://github.com/juvenoia/EED.

The Internet of Things (IoT) offers vast potential to enhance the quality of life, but the excessive visual data generated during environmental monitoring presents significant challenges. Existing visual data minimization methods struggle with real-time data reduction, often applying uniform minimization ratios to compress already generated data, which leads to high computational overhead and distortion. To address these limitations, this paper introduces REDA, a real-time event-driven approach for minimizing visual data generation. REDA employs an event estimation method that integrates motion and multi-scale object detection to reduce false alarms, missed detections, and computational costs. Additionally, it introduces an Optimal-IoU loss function to handle gradient challenges and applies contextual optical flow and filtering techniques to minimize data loss and distortion. Theoretical analysis and experimental results demonstrate that REDA achieves superior real-time data minimization and efficiency compared to existing state-of-the-art solutions.

Service function chain (SFC) enables network service providers to provide low-latency services to end devices, such as computation-intensive task offloading services through SFC in device-edge-cloud (DEC) computing. However, DDoS attacks can render SFC unavailable and impact task offloading in DEC computing. In this paper, we propose a trust-cooperative virtualized network function (VNF) model based on coalition formation for SFC provisioning. The proposed model records coalition formation information as transactions in the blockchain to protect the VNF information from being tampered with by attackers. Coalition formation for SFC provisioning consists of two steps: VNF node identity verification and the decision to join the coalition. To address the issue of unreliability in SFC deployment due to attacks, we propose a cooperative SFC provisioning algorithm based on security-aware coalition formation to identify trustworthy VNFs for SFC. Moreover, to handle the instability of SFC provisioning caused by DDoS attacks, we present an SFC reprovisioning algorithm based on the stochastic evolutionary coalition game with reward machines (SECGRM) under the constraint of VNF service times. Experimental results show that our proposed algorithms effectively combat malicious attacks and significantly reduce cooperative SFC provisioning latency compared with existing leading approaches.

Detecting anomalous BGP (Border Gateway Protocol) messages is critical for securing inter-domain routing systems over autonomous system (AS)-level networks. The dynamic nature of routing policies, massive scale of global routes, and incomplete global topology visibility make BGP anomalies exceptionally challenging to identify—let alone trace back to malicious or misconfigured ASes. To effectively overcome these barriers, this paper proposesGraphBGP, a novel BGP anomaly detection method that dynamically constructs real-time AS-level topologies, achieves precise anomaly detection and classification, and accurately traces malicious or misconfigured ASes. Specifically, to address the evolving nature of BGP routing status,GraphBGPconstructs an attributed AS-level graph that dynamically integrates node and edge attributes. It intelligently tracks BGP updates to refresh this graph efficiently. Leveraging this enriched, up-to-date representation,GraphBGPemploys tailored detection and tracing models grounded in graph convolutional networks (GCNs), enabling precise anomaly identification and source tracing. Comprehensive experiments with real-world and synthetic datasets demonstrate thatGraphBGPachieves state-of-the-art anomaly detection accuracy while significantly reducing inference time, even under partial BGP network visibility. Furthermore,GraphBGPprecisely traces malicious or misconfigured ASes within a short time period of 7 milliseconds after anomaly detection, enabling rapid mitigation.

Low-light video enhancement is a critical task in computer vision with a wide range of applications. However, there is a lack of high-quality benchmark datasets in this field. To address this issue, we collect a high-quality low-light video dataset using a well-designed camera system. The videos in our dataset feature apparent camera motion and strict spatial alignment. In order to achieve general low-light video enhancement, we propose a Retinex-based method called Light Adjustable Network (LAN). LAN iteratively adjusts the brightness and adapts to different lighting conditions in various real-world scenarios, producing visually appealing results. We further develop a new dataset capture method and low-light video enhancement method to address the limitation of our previous dataset in capturing dynamic scenes and previous method. The new camera setup and capture method enable the recording of real continuous videos and generate the new dataset. Our new low-light video enhancement method, LAN++, leverages a new inter-frame relationship, difference images. It utilizes the texture information contained in the difference images of dynamic scenes to supplement the high-frequency details of the original features, which produce sharper and more realistic output images. The extensive experiments demonstrate the superiority of our low-light video dataset and enhancement method. Our dataset can be downloaded at https://pan.baidu.com/s/1d3EljvVduVM0wUOvzjWaqA?pwd=p45g.

Sparse data gathering has become a promising solution for reducing measurement costs by leveraging the inherent sparsity of data. However, most existing approaches rely on low-dimensional models such as compressive sensing or matrix completion, which are limited in capturing complex high-dimensional structures. To overcome these limitations, we proposeTensorMon, a novel tensor-based sparse data gathering framework that introduces a cuboid sampling strategy to more effectively exploit multidimensional correlations. Unlike traditional entry-based or tube-based sampling, TensorMon introduces the innovative concept ofcuboid sampling. We further develop a lightweight sampling scheduling algorithm and a non-iterative inference algorithm to ensure efficient measurement planning and accurate reconstruction of unmeasured data. Theoretical analysis establishes a new performance bound for our sampling strategy, which is significantly lower than those in existing literature. To validate our theoretical findings, we conduct extensive experiments on four real-world datasets: two network monitoring datasets, a city-scale crowd flow dataset, and a road traffic speed dataset. Experimental results demonstrate that TensorMon achieves substantial reductions in measurement cost, delivers high inference accuracy, and ensures rapid data recovery, highlighting its effectiveness and practicality across diverse application scenarios.

Existing multistage clustering methods independently learn the salient features from multiple views and then perform the clustering task. Particularly, multiview clustering (MVC) has attracted a lot of attention in multiview or multimodal scenarios. MVC aims at exploring common semantics and pseudo-labels from multiple views and clustering in a self-supervised manner. However, limited by noisy data and inadequate feature learning, such a clustering paradigm generates overconfident pseudo-labels that misguide the model to produce inaccurate predictions. Therefore, it is desirable to have a method that can correct this pseudo-label mistraction in multistage clustering to avoid bias accumulation. To alleviate the effect of overconfident pseudo-labels and improve the generalization ability of the model, this article proposes a novel multistage deep MVC framework where multiview self-distillation (DistilMVC) is introduced to distill dark knowledge of label distribution. Specifically, in the feature subspace at different hierarchies, we explore the common semantics of multiple views through contrastive learning and obtain pseudo-labels by maximizing the mutual information between views. Additionally, a teacher network is responsible for distilling pseudo-labels into dark knowledge, supervising the student network and improving its predictive capabilities to enhance its robustness. Extensive experiments on real-world multiview datasets show that our method has better clustering performance than the state-of-the-art (SOTA) methods.

Deep neural networks (DNNs) have made great breakthroughs and seen applications in many domains. However, the incomparable accuracy of DNNs is achieved with the cost of considerable memory consumption and high computational complexity, which restricts their deployment on conventional desktops and portable devices. To address this issue, low-rank factorization, which decomposes the neural network parameters into smaller sized matrices or tensors, has emerged as a promising technique for network compression. In this article, we propose leveraging the emerging tensor ring (TR) factorization to compress the neural network. We investigate the impact of both parameter tensor reshaping and TR decomposition (TRD) on the total number of compressed parameters. To achieve the maximal parameter compression, we propose an algorithm based on prime factorization that simultaneously identifies the optimal tensor reshaping and TRD. In addition, we discover that different execution orders of the core tensors result in varying computational complexities. To identify the optimal execution order, we construct a novel tree structure. Based on this structure, we propose a top-to-bottom splitting algorithm to schedule the execution of core tensors, thereby minimizing computational complexity. We have performed extensive experiments using three kinds of neural networks with three different datasets. The experimental results demonstrate that, compared with the three state-of-the-art algorithms for low-rank factorization, our algorithm can achieve better performance with much lower memory consumption and lower computational complexity.

Network distance measurement is crucial for evaluating network performance, attracting significant research attention. However, conducting measurements for the entire network is exceedingly expensive and time-consuming, making the reduction of network distance measurement costs a top priority. The tensor completion method efficiently reduces measurement costs by utilizing a small amount of measured data to estimate the entire network’s distance data. Unfortunately, current tensor completion methods still suffer from issues such as complex sample selection, high measurement overhead, slow recovery, and low inference accuracy. To address the aforementioned challenges, we present an online network-wide distance measurement scheme. In this approach, continuous distance data are structured into sliding-window-based tensors. Our method incorporates a lightweight sample selection algorithm with a lowest sampling bound and a rapid, accurate unmeasured data inference algorithm. We have conducted extensive experiments using four real network distance datasets and two citywide crowd flow datasets. The empirical evaluations demonstrate the effectiveness of our approach, particularly in reducing measurement costs and enhancing data recovery accuracy.

Multi-hop cooperative task offloading (MCTO) allows resource-constrained edge clouds to collaborate and assist each other in completing computation-intensive tasks, such as training machine learning models through device-edge-cloud (DEC) computing. However, internal fake service attacks can pose a threat to the security and reliability of MCTO in DEC computing. In this paper, we propose a trust model based on a directed acyclic graph (DAG) and Proof-of-Work (PoW) to safeguard tasks against potential attacks. The edge node selection for task offloading involves two key steps: offloading confirmation and trust-based node selection. To mitigate the unreliability caused by internal fake service attacks during cooperative offloading, we propose a multi-hop offloading node selection algorithm based on the soft actor-critic (SAC) coalition. This algorithm helps identify trustworthy nodes for constructing secure offloading paths. Our experimental results demonstrate that the proposed algorithm effectively counters internal fake service attacks and significantly reduces cooperative offloading latency compared to existing leading approaches.

Accurate and efficient root cause identification in online service systems is critical for service stability and user experience. When a system failure occurs, numerous alerts are generated, but existing methods fail to effectively integrate all these multi-modal data to pinpoint the root causes. Moreover, most existing approaches are inefficient for large-scale online services due to their high reliance on handcrafted rules and domain expertise. This paper introduces AlertRCA, an algorithm for Root Cause Analysis (RCA) based on Alert events. It utilizes a pre-trained Alert2Vec module to encode multi-modal alert information into vectors, and implements an RCA-oriented causality prediction graph attention network (CPGAT) to automatically gauge causal relationships between alerts. Further, we devise a novel dispersing and aggregating graph neural network (DAGNN) to identify root causes. Experiments on a real-world dataset collected from a top-tier e-commerce company reveal AlertRCA’s superior performance, achieving 83.9% top-1 and 96.8% top-3 accuracy on average. Our codes are available at https://github.com/NetManAIOps/AlertRCA.

Most current arbitrary-scale image super-resolution (SR) methods has commonly relied on simulated data generated by simple synthetic degradation models (e.g., bicubic down-sampling) at continuous various scales, thereby falling short in capturing the complex degradation of real-world images. This limitation hinders the visual quality of these methods when applied to real-world images. To address this issue, we propose the Continuous Optical Zooming dataset (COZ), by constructing an automatic imaging system to collect images at fine-grained various focal lengths within a specific range and providing strict image pair alignment. The COZ dataset serves as a benchmark to provide real-world data for training and testing arbitrary-scale SR models. To enhance the model's robustness against real-world image degradation, we propose a Local Mix Implicit network (LMI) based on the MLP-mixer architecture and meta-learning, which directly learns the local texture information by simultaneously mixing features and coordinates of multiple independent points. The extensive experiments demonstrate the superior performance of the arbitrary-scale SR models trained on the COZ dataset compared to models trained on simulated data. Our LMI model exhibits the superior effectiveness compared to other models. This study is of great significance in developing more efficient algorithms and improving the performance of arbitrary-scale image SR methods in practical applications. Our dataset and codes are available at https://github.com/pf0607/COZ.

In recent years, efficient super-resolution research has focused on reducing model complexity and improving efficiency by leveraging deep small-kernel convolution, but it has the problem of a small receptive field, which leads to a limited ability of the network to reconstruct details. Large kernel convolution can provide a large receptive field and lead to a substantial enhancement in the quality of image reconstruction, but its computational cost is too high. To minimize the model’s parameter count and achieve efficient super-resolution reconstruction, this study introduces a symmetric visual attention network. The network decomposes the large kernel convolution into three different lightweight and efficient convolutions. It then forms a bottleneck structure by leveraging the varied receptive field sizes of these convolutions in combination. The attention mechanism is integrated to create a bottleneck attention module, enhancing the network’s feature awareness. Furthermore, the bottleneck attention modules are symmetrically arranged to construct a symmetric large kernel attention block, thereby further enhancing the network’s capability to extract deep features. The experimental results demonstrate that the proposed model achieves competitive quantitative metrics when compared to other lightweight super-resolution methods, and the details of the reconstructed images are enhanced. With only 183K parameters, the model achieves a lightweight yet high-quality super-resolution model, offering a novel solution approach for efficient super-resolution.

In large-scale online service systems, numerous Key Performance Indicators (KPIs), such as service response time and error rate, are gathered in a time-series format. KPI Anomaly Detection (KAD) is a critical data mining problem due to its widespread applications in real-world scenarios. However, KAD faces the challenges of dealing with KPI heterogeneity and noisy data. We propose KAD-Disformer, a KPI Anomaly Detection approach through Disentangled Transformer. KAD-Disformer pre-trains a model on existing accessible KPIs, and the pre-trained model can be effectively "fine-tuned" to unseen KPI using only a handful of samples from the unseen KPI. We propose a series of innovative designs, including disentangled projection for transformer, unsupervised few-shot fine-tuning (uTune), and denoising modules, each of which significantly contributes to the overall performance. Our extensive experiments demonstrate that KAD-Disformer surpasses the state-of-the-art universal anomaly detection model by 13% in F1-score and achieves comparable performance using only 1/8 of the finetuning samples saving about 25 hours. KAD-Disformer has been successfully deployed in the real-world cloud system serving millions of users, attesting to its feasibility and robustness. Our code is available at https://github.com/NetManAIOps/KAD-Disformer.

Due to the limitations of sensor, traditional cameras struggle to capture details within extremely dark areas of videos. The absence of such details can significantly impact the effectiveness of low-light video enhancement. In contrast, event cameras offer a visual representation with higher dynamic range, facilitating the capture of motion information even in exceptionally dark conditions. Motivated by this advantage, we propose the Real-Event Embedded Network for low-light video enhancement. To better utilize events for enhancing extremely dark regions, we propose an Event-Image Fusion module, which can identify these dark regions and enhance them significantly. To ensure temporal stability of the video and restore details within extremely dark areas, we design unsupervised temporal consistency loss and detail contrast loss. Alongside the supervised loss, these loss functions collectively contribute to the semi-supervised training of the network on unpaired real data. Experimental results on synthetic and real data demonstrate the superiority of the proposed method compared to the state-of-the-art methods.

Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to initiate diagnosis and remediation procedures. Variational Autoencoders (VAEs) have gained popularity in recent decades due to their superior de-noising capabilities, which are useful for anomaly detection. However, our study reveals that VAE-based methods face challenges in capturing long-periodic heterogeneous patterns and detailed short-periodic trends simultaneously. To address these challenges, we propose Frequency-enhanced Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for univariate time series. To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data. Together with a carefully designed "target attention" mechanism, our approach allows the model to pick the most useful information from the frequency domain for better short-periodic trend construction. Our FCVAE has been evaluated on public datasets and a large-scale cloud system, and the results demonstrate that it outperforms state-of-the-art methods. This confirms the practical applicability of our approach in addressing the limitations of current VAE-based anomaly detection models.

The distributed nature of multitier swarm attacks renders it more difficult for a single-tier intrusion detection system (IDS) to secure cross-tier computation offloading in multitier sensor edge cloud (SEC). To perceive and prevent such attacks, we model IDSs in different layers as an IDS federation network (IDFN) and present a generic framework to prevent cooperative attacks and reconfigure the defense strategy of IDFN across the three-tier SEC. The framework provides single-tier, two-tier, and three-tier dynamic awareness models based on the susceptible-infected-susceptible (SIS) dynamical equations to characterize the update process of message states to obtain the equilibrium solution between alarm messages and normal messages captured by IDSs. For swarm attack events from multitier SEC, we model the cross-tier cooperative interactions between IDSs and swarm attackers as an event–condition–action (ECA) regret learning game (ERLG) to achieve a distributed IDS reconfiguration to reduce the overall SEC alarm messages while ensuring the equilibrium of message states with the cooperation of IDSs. Simulation results demonstrate that our proposed scheme is superior to other reconfiguration mechanisms under swarm attacks in three-tier SEC.

Image-to-image (I2I) translation often requires establishing cycle consistency between the source and the translated images across different domains. However, cycle consistency requires redundant reconstruction, and is too restrictive to satisfy the bijection assumption between the two domains. In this paper, we propose SwinIT, a hierarchical Swin-transformer I2I Translation framework without using cycle consistency. Specifically, we carefully design symmetrical encoders for content and style flows, then explore newly proposed adaptive denormalization and normalization strategies. This framework can effectively capture and fuse content and style representations in a coarse-to-fine manner, ensuring our method achieves high performance without cycle consistency. Guided by element-wise feature adaptive denormalization, our model focuses on preserving semantic structure information. Due to the semantic mismatch between unpaired source and exemplar images, we introduce cross-attention adaptive instance normalization to help achieve better alignment. However, because the original optimization objective lacks direct supervision to preserve high-frequency information, rich edge details are lost during the translation. We propose a wavelet transformation matching loss to recover the details by converting the image into multi-frequency parts. We validate our proposed method in various I2I translation tasks, including arbitrary style transfer, multi-modal image synthesis, and semantic image synthesis, demonstrating its effectiveness in both qualitative and quantitative evaluations.

Robust PCA is a popular anomaly detection technique and has been widely used in many applications. Although Robust PCA is promising, it is usually designed in a two-order matrix form, which is inferior to the tensor that can capture multilinearity features of data. Moreover, the detection accuracy under Robust PCA further suffers due to its sensitivity to the rank parameter which is hard to set in practice and the limitation of PCA method in capturing the non-linear feature in the data. To address the issues, we propose a Robust Tensor Convolutional Autoencoder (RTCAE) where the autoencoder instead of SVD is exploited to recover the normal data from the corrupted measurement tensor data. However, directly exploiting deep autoencoder may suffer from the problem of high memory consumption and computation overhead due to the large number of parameters used in autoencoder. To make our anomaly detection lightweight, we further design a Light Convolutional Autoencoder (LightCAE) which contains a compressed autoencoder by exploiting tensor factorization to largely compress the parameters while significantly reducing the computation complexity. We conduct extensive experiments on three real data traces to compare the performance of our proposed schemes (RTCAE and lightCAE) with that of seven baseline algorithms. The experiment results demonstrate that our proposed RTCAE achieves the highest anomaly detection accuracy. Moreover, our LightCAE requires over 60 times smaller memory storage than that required in RTCAE while achieving the similar anomaly detection accuracy.

Traffic sensor networks are widely applied in smart cities to monitor traffic in real-time and record huge volumes of traffic data. Exploiting such data to forecast future traffic conditions have the potential to enhance the decision-making capabilities of intelligent transportation systems, which attracts widespread attention from both industries and academia. Among them, network-wide prediction based on graph convolutional neural networks(GCN) has become mainstream. It models the spatial dependencies of sensors in a graph with a pre-defined Laplacian matrix based on the distances among sensors. However, understanding spatio-temporal traffic patterns is quite challenging as there is a huge difference in terms of traffic patterns during different periods or in different regions. In addition, the actual data collected can be polluted due to unavoidable data loss from severe communication conditions or sensor failures. Considering these issues, we propose a novel dynamic multiview spatial-temporal prediction framework which takes into consideration various factors, including local/global, short/long term spatio-temporal dependencies and their dynamic changes. To comprehensively track the dynamic spatio-temporal dependencies among traffic data, we creatively design two different modules to perceive the changes in traffic patterns. We first propose a dynamic Laplacian matrix learning module based on our theoretical derivation to estimate the Laplacian matrix of the graph for GCN timely. We creatively incorporate tensor decomposition into this module, where real-time traffic data are decomposed into a global component that is stable and depends on long-term temporal-spatial traffic relationships and a local component that captures the traffic fluctuations. We also design a self-attention based module to dynamically assign a weight to each part in traffic data. The spatio-temporal features from multiple views are deeply fused by a feature fusion module. The forecasting performance is evaluated with 5 real-time traffic datasets. Experiment results demonstrate that our framework can consistently outperform the state-of-the-art baselines and be more robust under noisy environments.

Antenna tuning plays an essential role in ensuring high quality wireless communications. Targeting for higher Quality of Service (QoS), many existing network antenna tuning schemes are based on expert knowledge, rule-based policies or conventional optimization theory. However, maximizing the traffic-related QoS does not guarantee that all customers experience good services. In addition, existing schemes are often limited to some handcrafted rules or heuristics and lack of adaptability especially in a time-varying environment. Quality of Experience (QoE), a user-centric metric, can better measure users' satisfaction for services in wireless networks. This paper proposes the cooperative tuning of antennas based on QoE, a paradigm shift from network-centric QoS to user-centric QoE domain. In a normal cellular network, besides the need of improving the overall QoE, it requires handling faults from different cells. As Multi-agent Reinforcement Learning (MARL) has the capability of self-learning the dynamics of environment, we propose an antenna configuration algorithm based on multi-goal MARL. In our framework, agents from different cells not only need to cooperate with each other to achieve the global goal of increasing the overall QoE of the wireless network but also complete some personal goals by combating the faults encountered in their own cells. To accelerate the training efficiency, we introduce a novel two-stage curriculum learning. To reduce the collection time of each QoE sample, we develop an accurate and timely QoE/QoS mapping model with the cascading of a Random Forest Classifier (RFC) and a Deep Neural Network (DNN) (abbreviated as RFC-DNN), which can help us obtain QoE by collecting QoS measurements and perform QoE-based antenna configurations with smaller time granularity. Our proposed RFC-DNN model can reduce the time by 70% when predicting the QoE of a single sample. A huge amount of time will be saved in MARL when tens of thousands of transitions/samples need to be collected. The performance results show that our proposed antenna tuning schemes can not only address specific faults in each cell, but also significantly improve the global average QoE with a faster and more stable convergence speed.

The objective of multi-domain image-to-image translation is to learn the mapping from a source domain to a target domain in multiple image domains while preserving the content representation of the source domain. Despite the importance and recent efforts, most previous studies disregard the large style discrepancy between images and instances in various domains, or fail to capture instance details and boundaries properly, resulting in poor translation results for rich scenes. To address these problems, we present an effective architecture for multi-domain image-to-image translation that only requires one generator. Specifically, we provide detailed procedures for capturing the features of instances throughout the learning process, as well as learning the relationship between the style of the global image and that of a local instance in the image by enforcing the cross-granularity consistency. In order to capture local details within the content space, we employ a dual contrastive learning strategy that operates at both the instance and patch levels. Extensive studies on different multi-domain image-to-image translation datasets reveal that our proposed method outperforms state-of-the-art approaches.

Accurately identifying IoT device types is crucial for IoT security and resource management. However, existing traffic-based device identification algorithms incur high measurement, storage, and computation costs, as they continuously need to capture, store, and parse device traffic. To overcome these challenges, we propose an innovative framework that employs a discontinuous traffic measurement strategy, reducing the number of packets captured, stored, and parsed. To ensure accurate identification, we introduce several novel techniques. First, we propose a graph neural network-based tensor completion model to estimate missing traffic features in unmeasured time slots. Our model can utilize historical information to flexibly and efficiently estimate missing features. Second, we propose a convolutional neural network-based classifier for device identification. The classifier utilizes traffic features and node embeddings learned from the tensor completion model to achieve precise device identification. Through extensive experiments on real IoT traffic traces, we demonstrate that our framework achieves high accuracy while significantly reducing costs. For instance, by capturing only 30% of the packets, our framework can identify devices with a high accuracy of 0.9558. Moreover, compared to current tensor completion methods, our method can estimate missing values with higher accuracy and achieve a 1.53-fold speedup over the next-fastest baseline.

Images captured in low-light conditions often suffer from significant quality degradation. Recent works have built a large variety of deep Retinex-based networks to enhance low-light images. The Retinex-based methods require decomposing the image into reflectance and illumination components, which is a highly ill-posed problem and there is no available ground truth. Previous works addressed this problem by imposing some additional priors or regularizers. However, finding an effective prior or regularizer that can be applied in various scenes is challenging, and the performance of the model suffers from too many additional constraints. We propose a contrastive learning method and a self-knowledge distillation method for Retinex decomposition that allow training our Retinex-based model without elaborate hand-crafted regularization functions. Rather than estimating reflectance and illuminance images and representing the final images as their element-wise products as in previous works, our regularizer-free Retinex decomposition and synthesis network (RFR) extracts reflectance and illuminance features and synthesizes them end-to-end. In addition, we propose a loss function for contrastive learning and a progressive learning strategy for self-knowledge distillation. Extensive experimental results demonstrate that our proposed methods can achieve superior performance compared with state-of-the-art approaches.

Traffic modeling of Datacenter Network (DCN) today is over-simplified, deviating from the ground truth. Adopted by numerous researchers, the common practice relies on the assumptions of traffic homogeneity and independence for ease of use. Based on our investigation of a real-world traffic dataset, we disprove these assumptions and point out the severe fidelity issue of the common practice that could invalidate many motivations and conclusions from influential research works. In this paper, we present Encore, a DCN traic modeling framework for ine-grained traic modeling and high-fidelity synthetic traffic generation. Leveraging machine learning techniques, Encore effectively extracts and preserves essential distribution and sequential features from raw traic. Preliminary experiments demonstrate that the traic generated by Encore not only restores the key features of real traffic but also achieves high consistency when used to evaluate network performance. We envision further expanding Encore to full-process traffic modeling and generation, and expect these critical improvements in traffic models can facilitate the DCN performance evaluation and optimization.

Recent years have witnessed a tremendous growth in the scale and resolution of point clouds. To facilitate the applications of point cloud in downsampling tasks (e.g., point cloud classification), several task-oriented downsampling works have been developed by training with the task-specific loss with one-hot encoded label. However, these methods still suffer from performance degradation at high downsampling scales. In this paper, we propose a general semantic-preserved downsampling framework (SPDF) for point clouds by exploiting the rich knowledge inherent in the task network. Specifically, we firstly refine the previous pipeline to generate richer semantic supervised information. Then, the semantic feature learning is subdivided into label-level and feature-level to guide the training of downsampling network, which can better limit the semantic loss during downsampling. Extensive experiments on the benchmark dataset show that SPDF outperforms state-of-the-art downsampling methods.

Multi-view projection methods have demonstrated their ability to reach state-of-the-art performance in point cloud downstream tasks(e.g., classification and retrieval). These methods first require rendering the point cloud into 2D multi-view images. However, conventional methods only project the geometry of the point cloud, and such projections inevitably suffer from a loss of point cloud semantic information due to dimensionality reduction. We propose a semantic-aware and task-oriented differentiable feature rendering (SFR), which reduces the information loss during projection by generating rendered images with more point cloud semantic information for downstream tasks. Our SFR method can be applied as a plug-and-play module added to any multi-view-based backbone network for end-to-end training. Extensive experiments on benchmark datasets show that our SFR method reaches state-of-the-art performance and brings general improvements to point cloud classification and retrieval tasks.

Recently, human pose estimation has attracted more and more attention due to its importance in many real applications. Although many efforts have been put on extracting 2D poses from static images, there are still some severe problems to be solved. A critical one is occlusion, which is more obvious in multi-person scenarios and makes it even more difficult to recover the corresponding 3D poses. When we consider a sequence of images, the temporal correlation among the contexts can be utilized to help us ease the problem, but most of the current works only rely on discrete-time models and estimate the joint locations of all people within a whole sparse graph. In this paper, we propose a new framework, Hierarchical Dynamic Graph Ordinary Differential Equation (HDG-ODE), to tackle the 3D pose forecasting task from 2D skeleton representations in videos. Our framework adopts ODE, a continuous-time model, as the base to predict the 3D joint positions at any time. Considering the structural-property of the skeleton data in representing human poses and the possible irregularity caused by occlusion, we propose the use of dynamic graph convolution as the basic operator. To reduce the computational complexity introduced by the sparsity of the pose graph, our model takes a hierarchical structure where the encoding process at the observation timestamp is done in a cascade manner while the propagation between observations is conducted in parallel. The performance studies on several datasets demonstrate that our model is effective and can out-perform other methods with fewer parameters.

Lipreading, also called visual speech recognition, is an excellent technique to understand what a speaker says without audios. Based on deep learning, many studies have gained outstanding achievements in English lipreading. English is dominated by polysyllables, with the proportion of homonyms as low as 1%. Compared to English, Mandarin (the official language of Chinese) is dominated by monosyllables, with the ratio of homonyms as high as 72%. The high ratio of homonyms makes the lipreading in Mandarin much more challenging. However, little attention has been paid to Mandarin lipreading within the deep learning framework, especially at the sentence level. In this paper, we first introduce a dataset, named Mandarin-Lipreading, which is recorded in the controlled lab environment and is the largest dataset so far for sentence-level lipreading in Mandarin. We further investigate the modeling units and propose an end-to-end Mandarin lipreading system. The experimental results show that the proposed system achieves 14.77% CER on Mandarin-Lipreading and 8.7% CER for unseen speakers evaluation on the GRID corpus.

Emotion recognition, particularly contactless recognition via wireless sensing, has shown its promise in diverse applications. However, the previous works only focus on emotions rather than the person, i.e., the premise is already knowing who the subject is, without considering the issue of identifying subjects. We envision that user identification and emotion recognition together will bring more adaptive and personalized Internet of Things applications, e.g., a smart home system can react to specific emotions of a specific user, independently. In this work, we move forward to investigate the problem of simultaneous user identification, using only physiological indicators embedded in wireless signals reflected off from targets. Toward the objective, in this article, we first carry out a comprehensive measurement study, which validates the feasibility of simultaneous user identification and emotion recognition. Moreover, the measurement also discovers that the key challenge lies in the limitation of artificial features and the substantial emotion feature deviation across different days, which hinders accurate and robust sensing. To resolve the challenge, we design two multiscale neural networks, incorporated with a custom-built feature attention mechanism, so as to obtain rich feature expression and, thus, enhance the important features for accurate recognition. We prototype mmEMO using a commercial off-the-shelf millimeter-wave radar and experimental evaluation shows that mmEMO can achieve 87.68% user identification accuracy and 80.59% emotion recognition accuracy, respectively.

As a promising architectural design for future Internet, Named Data Networking (NDN) relies on data names, instead of destination IP addresses, to deliver data. NDN supports data authenticity and integrity by making public key signatures mandatory on data content and data names. This handles the primary security concern in NDN, but is still vulnerable to new DDoS attacks, including Cache Pollution attacks and Interest Flooding attacks, which degrade NDN transmission significantly, by violating the crucial components of NDN routers. To defend against DDoS attacks in NDN, the most effective way is to persistently detect the malicious traffic and then throttle them. Except for the usual concern of the accuracy and efficiency in attack detection, since these attacks themselves have already imposed a huge burden on victims, to avoid exhausting the remaining resources on the victims for detection purpose, a lightweight detection solution is highly desired. We study DDoS attacks and propose a persistent detection solution based on an observed malicious traffic pattern, which leverages a novel sketch to monitor the malicious traffic in a timely and lightweight way. Additionally, our analysis and experiments demonstrate that, with fixed low resource consumption, the proposed solution can persistently detect DDoS attacks in NDN.

Content Centric Network (CCN) can be extended to efficiently and reliably support content delivery and solve the network performance degradation caused by dynamic topology and intermittent connectivity of Vehicle Ad hoc NETwork (VANET). However, the in-network caching mechanism of Vehicular Content Centric Network (VCCN) is vulnerable against Cache Pollution Attack (CPA), where attackers aim to fill the buffer space with non-popular contents by releasing fake requests. Unavoidably, the cache hit ratio of content requests from legal users is degraded and the content retrieval latency is increased under CPA. Hence, it is critical to detect and mitigate CPA. The current solutions for static CCN cannot be directly applied into dynamic VCCN. In this article, we propose a detection scheme based on hybrid heterogeneous multi-classifier ensemble learning, where CPA is determined by the cooperation of multiple vehicles. In our scheme, each vehicle can build or join a cluster whose head possesses more common moving attributes of position, speed and direction with other members. Besides, the cluster head as a base learner is responsible for training its own classifier by making some relevant statistics on requests and hit ratio. Specifically, the problem of ensemble classifier making from the individual classifiers is formulated as a linear optimization problem, with the goal of minimizing the false ratio of detecting CPA. The generalization ability of ensemble learning can make very accurate predictions on CPA. By comparison, our detection scheme outperforms the existing schemes in terms of detection ratio, hit ratio, retrieval delay. Besides, simulations have proved that the overfitting problem of adopting a singe base learning algorithm can be alleviated in our scheme.

Quality of experience (QoE) is a vital metric that indicates how well the wireless network provides transmission services to users, while quality of service (QoS) help better configure the network parameters for higher performance. The evaluation time of QoE is usually several orders of magnitude larger than that of QoS, because QoE is the perception of users over a period of time, but QoS can be collected every millisecond. Therefore, the implementation of QoE/QoS mapping model can help us obtain QoE by collecting the QoS measurements, and perform QoE-based network configurations with smaller time granularity. Many studies are made to obtain the QoS to QoE mapping, including the use of machine learning (ML) methods. However, traditional ML-based regression methods for QoE/QoS mapping face the challenge of high regression error and catastrophic forgetting in dealing with continuously arriving data. In this paper, we propose a novel QoE model based on continual deep learning in wireless network. This model is formed with two deep neural networks (DNNs) concatenated. The first DNN classifies data into different subsets, which are then fed into the second DNN for regression. The second DNN dynamically form the corresponding subnets, each with nodes and connections adaptively selected in each new time period with new arriving data. We solve the catastrophic forgetting problem with the use of node splitting and hidden state augmentation. Our proposed learning framework greatly reduces the regression error to as low as 0.9314%. The experimental results demonstrate that our proposed model reduces the root mean square error (RMSE) by$21 \sim 86$times compared with several existing approaches, specially, the testing error of our proposed model is more than 80 times lower than that of traditional DNN. Compared with other DNN-based cascade models, our proposed method provides good performance in both training time and RMSE.

Network trouble shooting, failure location, and anomaly detection rely heavily on network traffic measurement data. Due to the lack of measurement infrastructure, the high measurement cost, and the unavoidable transmission loss, network monitoring systems suffer from the problem that the network traffic data are incomplete. This article models the traffic data as a tensor to exploit its strong ability of feature extraction to recover the missing data. Different from traditional tensor completion which relies on tensor factorization, we design a novel Deep Adversarial Tensor Completion (DATC) scheme based on Deep Learning (DL) techniques. DATC is the first scheme that exploits the data reconstruction ability of autoencoder and the power of adversarial training from Generative Adversarial Networks to infer the missing data. Despite that DL techniques achieve great success in the image field, designing an algorithm based on DL techniques to recover the traffic data with missing entries faces additional challenges due to the skewed distribution and the sparsity of traffic data. To conquer these challenges, we propose the use of two techniques, adversarial training and missing data aware convolution. These techniques help DATC to learn the complex features of the traffic data and infer the missing data following the data distribution of traffic data. Our extensive experimental results using two public real-world network traffic datasets and running both offline and online demonstrate that DATC can achieve significantly better recovery accuracy while capturing the data distribution of the traffic data even when the sampling ratio is very low.

Detecting anomalous traffic is a crucial task for network management. Although many anomaly detection algorithms have been proposed recently, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this paper uses the tensor model for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low-rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). Besides only detecting random anomalies, our algorithm can also effectively detect structured anomalies that appear as bursts. We have conducted extensive experiments using Internet traffic trace data Abilene and GÈANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-TR can achieve higher Accuracy and Recall.

Network measurements provide critical inputs for a wide range of network management. Existing network-wide monitoring methods face the challenge of incurring a high measurement cost. Some recent studies show that network-wide measurement data such as end-to-end latency and flow traffic, have hidden spatio-temporal correlations and thus low-rank features. Taking advantage of the low-rank feature, enlightened by tensor model's strong capability of information representation and extracting, this paper studies a novel sparse measurement scheduling problem which selects a proportion of Origin and Destination (OD) pairs to take measurements in the future time slots, while ensuring the data of the remaining un-measured OD pairs be accurately inferred through tensor completion. It is challenging to find the optimal sampling points (OD pairs) without knowing the structure of the future data and also infer the un-measured data in the presence of noise in the measurement samples. To conquer the challenges, we propose several techniques: a tripartite graph to illustrate the relationship between sample locations and tensor factorization, a graph-based sample selection algorithm, and a graph-based robust tensor completion algorithm. We have conducted extensive experiments based on two real network latency monitoring traces (PlanetLab and Harvard) and two other network monitoring traces (including a traffic trace Abilene and a throughput trace WS-Dream). Our results demonstrate that, even with a sampling ratio of less than 5%, our scheme can accurately obtain the complete network-wide monitoring data by inferring the missing ones based on the samples taken. To achieve similar recovery performance, the best peer tensor completion algorithm needs a significantly larger number of samples, with the sampling ratio up to 25-150 times ours.

Network traffic engineering and anomaly detection rely heavily on network traffic measurement. Due to the lack of infrastructure to measure all points of interest, the high measurement cost, and the unavoidable transmission loss, network monitoring systems suffer from the problem that the network traffic data are incomplete with only a subset of paths or time slots measured. Recent studies show that tensor completion can be applied to infer the missing traffic data from partial measurements. Although promising, the interaction model adopted in current tensor completion algorithms can only capture linear and simple correlations in the traffic data, which compromises the recovery performance. To solve the problem, we propose a new tensor completion scheme based on Lightweight Trilinear Pooling, which designs (1) a Trilinear Pooling, a new multi-modal fusion method to model the interaction function to capture the complex correlations, (2) a low-rank decomposition based neural network compression method to reduce the storage and computation complexity, (3) an attention enhanced LSTM to encode and incorporate the temporal patterns in the tensor completion scheme. The extensive experiments on three real-world network traffic datasets demonstrate that our scheme can significantly reduce the error in missing data recovery with fast speed using small storage.

Recovery of missing network monitoring data is of great significance for network operation and maintenance tasks such as anomaly detection and traffic prediction. To exploit historical data for more accurate missing data recovery, some recent studies combine the data together as a tensor to learn more features. However, the need of performing high cost data decomposition compromises their speed and accuracy, which makes them difficult to track dynamic features from streaming monitoring data. To ensure fast and accurate recovery of network monitoring data, this paper proposes NMMF-Stream, a stream-processing scheme with a context extraction module and a generation module. To achieve fast feature extraction and missing data filling with a low sampling rate, we propose several novel techniques, including the context extraction based on both positive and negative monitoring data, context validation via measuring the Pointwise Mutual Information, GRU-based temporal feature learning and memorization, and a new composite loss function to guide the fast and accurate data filling. We have done extensive experiments using two real network traffic monitoring data sets and one network latency data set. The experimental results demonstrate that, compared with three baselines, NMMF-Stream can fill the newly arrived monitoring data very quickly with much higher accuracy.

Interactive applications have deadline requirements, e.g. video conferencing and online gaming. Compared with a single path, which may be less stable or bandwidth insufficient, using multiple network paths simultaneously (e.g., WiFi and cellular network) can leverage the ability of multiple paths to service for the deadline. However, existing multipath schedulers usually ignore the deadline and the influence from subsequent blocks to the current scheduling decision when multiple blocks exist at the sender. In this paper, we propose DAMS, a Deadline-Aware Multipath Scheduler aiming to deliver more blocks with heterogeneous attributes before their deadlines. DAMS carefully schedules the sending order of blocks and balances its allocation on multiple paths to reduce the waste of bandwidth resources with the consideration of the block’s deadline. We implement DAMS with the inspiration of MPQUIC in user space. The extensive experimental results show that DAMS brings 41%-63% performance improvement on average compared with existing multipath solutions.

Network-wide monitoring is important for many network functions. However, monitoring data are often incomplete due to the need of sampling to reduce high measurement cost, system failure, and unavoidable transmission loss under severe communication. Instead of only targeting to estimate all missing monitoring data entries with a small set of measurement samples, we study a new order-preserved monitoring data estimation problem to accurately estimate the missing data entries while preserving the data entries’ order in the dataset. We propose a novel order-preserved tensor completion model that integrates both the low rank property and the order information into a joint learning problem to estimate the missing data. With well designed non-convex function to directly approximate the tensor rank and order-preserved constraint under the linear self-recovery method, our model can not only more accurately capture the low-rank property of monitoring data to increase the estimation performance of missing data, but also can capture the order information in monitoring data to ensure the estimation accuracy. Extensive experiments using four real datasets demonstrate that compared with the state-of-the-art tensor completion algorithms, our proposed algorithm can provide more accurate estimation and keep the value order of recovered entries to more effectively retrieve top-k large entries.

One key challenge to maintain a large-scale Content Delivery Network (CDN) is to minimize the service downtime when severe system problems happen (e.g., hardware failures). In this case, a critical step is to quickly and accurately identify the range of users with performance degradation, termed impact identification. Successful impact identification not only helps identify impacted users but also provides meaningful information for troubleshooting. However, current practice of impact identification usually takes network engineers several hours to manually identify impacted users, which may lead to a huge business loss. The main challenges for automatic impact identification in large CDNs include the inaccuracy of underlying anomaly detection, huge search space of impact identification and severe long-tail distribution of user traffic. In this paper we propose iSwift, a system that is specifically designed for impact identification in large-scale CDNs in order to address aforementioned challenges. We evaluate the performance of iSwift on semi-synthetic datasets and the results show that iSwift can achieve a F1-score greater than 0.85 within ten seconds, which significantly outperforms state-of-the-art solutions. Furthermore, iSwift has been deployed in a production CDN around one year as a pilot project and demonstrated its online performance confirmed by the network operators.

Image captioning is shown to be able to achieve a better performance by using scene graphs to represent the relations of objects in the image. The current captioning encoders generally use a Graph Convolutional Net (GCN) to represent the relation information and merge it with the object region features via concatenation or convolution to get the final input for sentence decoding. However, the GCN-based encoders in the existing methods are less effective for captioning due to two reasons. First, using the image captioning as the objective (i.e., Maximum Likelihood Estimation) rather than a relation-centric loss cannot fully explore the potential of the encoder. Second, using a pre-trained model instead of the encoder itself to extract the relationships is not flexible and cannot contribute to the explainability of the model. To improve the quality of image captioning, we propose a novel architecture ReFormer- a RElational transFORMER to generate features with relation information embedded and to explicitly express the pair-wise relationships between objects in the image. ReFormer incorporates the objective of scene graph generation with that of image captioning using one modified Transformer model. This design allows ReFormer to generate not only better image captions with the benefit of extracting strong relational image features, but also scene graphs to explicitly describe the pair-wise relationships. Experiments on publicly available datasets show that our model significantly outperforms state-of-the-art methods on image captioning and scene graph generation.

To ensure an accurate power allocation against increasing intelligent jamming attacks on the offloading link of computation tasks, we investigate interactions between a cluster head node and an intelligent jammer using a Stackelberg game framework, under the constraint of the total power to use and the limited knowledge of its own channel gain for each player. In this game, the intelligent jammer gathers channel gain information and processes it using a deep neural network (DNN) to infer the accurate jamming power as an attack strategy. The cluster head node also exploits DNN to infer an accurate transmission power as a defense strategy according to the varying channel gain. We model the optimization of the attack and defense strategies using single channel jamming DNN (SJnet), multiple channel jamming DNN (MJnet), single channel sensor DNN (SSnet), and multiple channel sensor DNN (MSnet) for the single (multiple) channel jamming attacks. In addition, we extend the design to the scenario where the intelligent jammer can launch a hybrid mode jamming attack, and propose a DNN Stackelberg game-based defense scheme. Numerical simulation results demonstrate that our proposed mechanism is superior to other power allocation mechanisms under different scenarios in the sensor edge cloud.

Nowadays cloud providers purchase a good deal of bandwidth from Internet service providers to satisfy the growing requests from corporate customers for the exclusive use of inter-datacenter bandwidth. For exclusive bandwidth services, neither maximizing the revenue nor minimizing the cost can bring the maximal profit to cloud providers. The diversity of bandwidth prices and the random arrival time of user requests further increase the difficulty in economically scheduling the services to meet user requests from cloud providers. In this article, we propose to help cloud providers maximize their service profits by properly selecting user requests to serve rather than satisfying them all. We formulate the problem of service profit maximization and prove its NP-hardness. To handle offline request submission, we propose a solution that maximizes the service profit by alternately maximizing the service revenue and minimizing the service cost. To maximize service profit under online request submission, we propose an online scheduling algorithm that carefully handles the risk of not being able to pay off the incremental service cost and makes scheduling decisions in real time. Our extensive evaluations demonstrate that our solutions can achieve more than 1.6x the service profits of existing solutions.

Multi-set membership query is a fundamental issue for network functions such as packet processing and state machines monitoring. Given the rigid query speed and memory requirements, it would be promising if a multi-set query algorithm can be designed based on Bloom filter (BF), a space-efficient probabilistic data structure. However, existing efforts on multi-set query based on BF suffer from at least one of the following drawbacks: low query speed, low query accuracy, limitation in only supporting insertion and query operations, or limitation in the set size. To address the issues, we design a novel B h sequence-based Bloom filter (B h BF) for multi-set query, which supports four operations: insertion, query, deletion, and update. In B h BF, the set ID is encoded as a code in a B h sequence. Exploiting good properties of B h sequences, we can correctly decode the BF cells to obtain the set IDs even when the number of hash collisions is high, which brings high query accuracy. In B h BF, we propose two strategies to further speed up the query speed and increase the query accuracy. On the theoretical side, we analyze the false positive and classification failure rate of our B h BF. Our results from extensive experiments over two real datasets demonstrate that B h BF significantly advances state-of-the-art multi-set query algorithms.

Spectrum map is an essential tool for a range of emerging applications of 5G and 6G networks. Despite the great efforts that have been put on the construction of spectrum maps, access to accurate and valid spectrum data in dynamically changing environments emphasizes the need for more advanced solutions tailored to such rapidly varying scenarios. To this end, the idea of spectrum map prediction is introduced. In this paper, we address the problem of spectrum map prediction from historical spectrum observations in the dynamically changing environments. The problem is particularly challenging when the available historical spectrum observations are incomplete and corrupted by anomalies. We propose three techniques to solve the problem. First, we combine the spectrum map with prediction functionalities so as to offer a huge potential for efficient resource management and flexible sharing of resources in dynamically changing environments. Second, by fully exploiting the hidden spatial-temporal-spectral structures of the spectrum data and the sparsity of anomalies and missing data, we model the spectrum map as a 3rd-order spectrum tensor and formulate the spectrum map prediction problem as a low-rank tensor completion problem. Third, we design a robust online spectrum map prediction (ROSMP) algorithm based on the alternating direction minimization method, which derives the tensor decomposition factors for a new timeslot based on the update of existing ones rather than re-computing from the scratch. By gradually learning the hidden spatial-temporal-spectral structures of the spectrum data, ROSMP is able to predict and obtain the complete spectrum map with high accuracy. Finally, extensive numerical evaluations using a real spectrum measurement dataset confirm the efficacy and efficiency of ROSMP and show the superiority of ROSMP over the baselines.

In multi-party interactive live streaming, each user can act as both the sender and the receiver of a live video stream. Designing adaptive bitrate (ABR) algorithm for such applications poses three challenges: (i) due to the interaction requirement among the users, the playback buffer has to be kept small to reduce the end-to-end delay; (ii) the algorithm needs to decide what is the bitrate to receive and what is the set of bitrates tosend; (iii) the delay and quality requirements between each pair of users may differ, for instance, depending on whether the pair is interacting directly with each other. To address these challenges, we first develop a quality of experience (QoE) model for multi-party live streaming applications. Based on this model, we designMultiLive, an adaptive bitrate control algorithm for the multi-party scenario. MultiLive models the many-to-many ABR selection problem as a non-linear programming problem. Solving the non-linear programming equation yields the target bitrate for each pair of sender-receiver. To alleviate system errors during the modeling and measurement process, we update the target bitrate through the buffer feedback adjustment. To address the throughput limitation of the uplink, we cluster the ideal streams into a few groups, and aggregate these streams through scalable video coding for transmissions. We also deploy the algorithm on a commercial live streaming platform that provides such services for more than 2300 users. The experimental results show that MultiLive outperforms the fixed bitrate algorithm, with 2-$5\times $improvement in average QoE. Furthermore, the end-to-end delay is reduced to around 100 ms, much lower than the 400 ms threshold recommended for video conferencing.

In network-wide monitoring, finding the large monitoring data entries is a fundamental network management function. However, the retrieval of large entries is extremely difficult and challenging as a result of incompleteness of network measurement data. Enlightened by tensor model’s strong capability of information representation and extraction, we model the network-wide monitoring data as a 3-way tensor. With tensor completion, the retrieval can be performed after recovering all missing entries. However, this not only incurs an extremely high cost when the tensor is large, but is also unnecessary. Instead, to quickly retrieve large entries at low cost, we transform the large entry retrieving problem to a cosine similarity searching problem, and propose two algorithms: 1) Quickly reordering the factor vectors based on Locality Sensitive Hashing (LSH) hash table so that vectors with small cosine distances are placed in the same hash bucket; 2) Quickly finding the similar vector of a queried one that the two together determine a large entry without incurring the high cost of recovering all entries through the dot products. In the process of LSH table building and similarity query, several novel techniques are proposed, including LSH table representation with the LSH forest, good hash table building to support the flexible search of cosine similarity, and bit-shifting-based quick similarity query. Our experimental studies on 4 real world datasets indicate that our technique is at least up to 60 times faster than the approach based on direct tensor completion.

The need of highly directional communications at mmWave band introduces high overhead for beam training and alignment, which also makes the medium access control (MAC) a grand challenge. However, the need of supporting highly directional multiple beams between transmitters and receivers makes the MAC design even harder. To harvest the gain of multi-beam mmWave communications, which benefits not only from the large bandwidth of mmWave spectrum but also the diversity of concurrent multi-user multi-beam transmissions, this paper studies the medium access control (MAC) layer related issues in multi-beam mmWave networks, including (1) efficient multi-beam training schemes to enable lower overhead thus faster AP association and beam alignment, (2) block-sparse mmWave channel estimation in different beam resolutions, and (3) effective concurrent radio resource allocation to facilitate better multi-user multi-beam transmissions. Simulation results demonstrate that the proposed schemes outperform existing techniques in improving the efficiency of mmWave communications thus achieving significantly higher network performances. To our best knowledge, we are the first to comprehensively consider both efficient training for beam alignment and resource scheduling in the MAC design to enable highly directional multi-user multi-beam concurrent transmissions in a mmWave network.

More and more artificial intelligence (AI) applications, such as virtual reality (VR) and video analytics, are rapidly progressing towards enterprise and end-users with the promise of bringing immersive experience. Driven by the desire to improve users’ experience and promote business scenarios, such AI applications have unprecedented requirements for ultra-low latency as well as abundant computing resource in networks. Data centers in the core network can meet these demands by deploying various AI services and providing abundant resources. However, data transmission delay from data centers to end-users is too time-consuming because of traffic congestion in the core network, which compromises the performance of the AI applications. 5G and edge computing are emerging technologies to guarantee the timeliness for the delay-sensitive applications. The delay experienced by AI users can be significantly reduced, by ‘caching’ various services that are initially deployed at data centers to cloudlets in edge networks. Although ubiquitous edge service caching is always preferable for improving user experiences, it is impractical to cache all services from data centers to edge cloudlets, due to often limited caching budget of service providers and resource capacity constraints of cloudlets. Therefore, a service provider has to cautiously decide how many instances of a service can be cached, and where to cache the service instances. In this article, we investigate a fundamental problem ofservice cachingfrom remote data centers to edge cloudlets in a multi-tiered edge cloud network. We first develop two approximation algorithms with approximation ratios to solve the problem for users demanding a single type of service. We then devise an efficient heuristic to solve the problem that users require different types of services. We finally conduct extensive experiments on a real test-bed to evaluate the performance of the proposed algorithms, and experimental results demonstrate that our algorithms can outperform some existing algorithms significantly.

Network applications, such as network state tracking and forecasting, anomaly detection, and failure recovery, require complete network monitoring data. However, the monitoring data are often incomplete due to the use of partial measurements and the unavoidable loss of data during transmissions. Tensor completion has attracted some recent attentions with its capability of exploiting the multi-dimensional data structure for more accurate un-measurement/missing data inference. Although conventional tensor completion algorithms can work well when the application data follow the symmetric normal distribution, it cannot well handle network monitoring data which are highly skewed with heavy tails. To better follow the data distribution for more accurate recovery of the missing entries with large values, we propose a novel expectile tensor completion (ETC) formulation and a simple yet efficient tensor completion algorithm without hard-setting parameters for easy implementation. From both experimental and theoretical ways, we prove the convergence of the proposed algorithm. Extensive experiments on two real-world network monitoring datasets demonstrate the effectiveness of the proposed ETC.

Time series forecasting is very important and plays critical roles in many applications. However, making accurate forecasting is a challenge task due to the requirements of learning complex temporal and spatial patterns and combating noise during the feature learning. To address the challenge issues, we propose TEGMNet, a Tensor projection Embedding and Gated Memory Network for multivariate time series forecasting. To more accurately extract local features and reduce the influence of noise, we propose to amplify the data using several data transformation techniques based on MDT (Multi-way delay embedding transform) and TFNN (tensor factorized neural network) to transform the original 2D matrix data to low dimensional 3D tensor data. The local features are then extracted through convolution and LSTM upon the 3D tensor. We also design a long-term feature extraction module based on the structure of gated memory network, which can largely enhance the longterm pattern feature learning ability when the multivariate time series has complex long-term dependencies with dynamic-period patterns. We have done extensive experiments by comparing our TEGMNet with 7 baseline algorithms using 4 real data sets. The experiment results demonstrate that TEGMNet can achieve very good prediction performance even through the data are polluted with noise.

Image de-distortion is very important because distortions will degrade the image quality significantly. It can benefit many computational visual media applications that are primarily designed for high-quality images. In order to address this challenging issue, we propose a stacked semantically-guided network, which is the first try on this task. It can capture and restore the distortions around the humans and the adjacent background effectively with the stacked network architecture and the semantically-guided scheme. In addition, a discriminative restoration loss function is proposed to recover different distorted regions in the images discriminatively. As another important effort, we construct a large-scale dataset for image de-distortion. Extensive qualitative and quantitative experiments show that our proposed method achieves a superior performance compared with the state-of-the-art approaches.

To enhance dependable resource allocation against increasing distributed denial-of-service (DDoS) attacks, in this article, we investigate interactions between a sensor device-edgeVM pair and a DDoS attacker using a game-theoretic framework, under the constraints of the task time, resource budget, and incomplete knowledge of the processing time of machine learning tasks. In this game, the sensor device expects an edgeVM to cooperate and choose its resource allocation strategy with the objective of satisfying the minimum resource required of machine learning tasks at the corresponding sensor device. Similarly, the attacker's objective is to strategically allocate resources so that the resource constraint of the machine learning tasks is not satisfied. Owing to a lack of complete information of the processing time of the machine learning tasks, this strategic resource allocation problem between the two players is modeled as a Bayesian Q-learning game, in which the optimal strategies of the sensor device-edgeVM pair and the attacker are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game and a greedy Q-learning algorithm is proposed to dependable resource allocation against DDoS attacks. Numerical simulation results demonstrate that the proposed mechanism is superior to other dependable resource allocation mechanisms under incomplete information for DDoS attacks in the sensor edge cloud.

Data in its original form, however, typically contain sensitive information about individuals. Directly publishing raw data will violate the privacy of people involed. Consequently, it becomes increasingly important to preserve the privacy of published data. An attacker is apt to identify an individual from the published tables, with attacks through the record linkage, attribute linkage, table linkage or probabilistic attack. Although algorithms based on generalization and suppression have been proposed to protect the sensitive attributes and resist these multiple types of attacks, they often suffer from large information loss by replacing specific values with more general ones. Alternatively, anatomization and permutation operations can de-link the relation between attributes without modifying them. In this paper, we propose a scheme Sensitive Label Privacy Preservation with Anatomization (SLPPA) to protect the privacy of published data. SLPPA includes two procedures, table division and group division. During the table division, we adopt entropy and mean-square contingency coefficient to partition attributes into separate tables to inject uncertainty for reconstructing the original table. During the group division, all the individuals in the original table are partitioned into non-overlapping groups so that the published data satisfies the pre-defined privacy requirements of our (α; β; γ; δ) model. Two comprehensive sets of real-world relationship data are applied to evaluate the performance of our anonymization approach. Simulations and privacy analysis show our scheme possesses better privacy while ensuring higher utility.

Named Data Networking (NDN) is one of the most promising information-centric networking architectures that can improve the network performance by supporting the large scale content distribution. However, the use of in-network caching mechanism increases the opportunity of cache pollution attack, where the attackers intend to reduce the cache hit of legal users by releasing fake requests to fill the precious cache with non-popular contents. To prevent the degradation of network performance caused by such an attack, it is becoming particularly important to detect the attack and then throttle it. In this article, we propose a detection and defense scheme with the help of grey forecast, which can effectively exploit the regularity of past Interests and popularity by comprehensively considering three major factors to predict the future popularity of each cached content. If the predicted popularity of any content differs too much from the actually calculated one in several consecutive slices, the pollution attack will be determined. Once the attack is detected, the defense will be taken by suppressing the popularity increase of the suspicious content to mitigate the damage of the pollution attack. We also consider a special case, where there exists a sudden burst of traffic from legal users that cannot be simply dropped. The simulations in ndnSIM indicate that our proposed method is effective in detecting and defending the pollution attack with higher cache hit, higher detecting ratio, and lower hop count compared to other state-of-the-art schemes.

Communications in vehicular ad-hoc network (VANET) are subject to performance degradation as results of channel fading and intermittent network connectivity. The emerging Vehicular Content Centric Network (VCCN) is promising in supporting the needs of contents and alleviating the communication problems in VANET. Specifically, to improve the cache hit ratio and reduce the access delay of content retrieval, it helps to choose the appropriate vehicles to cache the frequently accessed data items. In this paper, we propose a Cooperative Caching scheme based on Social Attributes and Mobility Prediction (CCSAMP) for VCCN. CCSAMP is based on the observation that vehicles move around and are liable to contact each other according to drivers' common interests or social similarities. A caching node sharing more social attributes with the content requester is more likely to be interested in the same contents and distribute the contents to others with similar interests. Furthermore, a caching node that frequently meets other nodes is a better candidate to keep cache copies. To increase the network performance, CCSAMP also exploits the regularity of vehicle moving behaviors to predict the chance for a vehicle to reach hot zones based on Hidden Markov Model (HMM). We evaluate CCSAMP through the ONE simulator to demonstrate its higher cache hit ratio and lower content access delay compared to other state-of-the-art schemes.

Network transmission over millimeter-wave (mmW) bands has a big potential to provide orders of higher bandwidth. However, beamforming is generally needed to compensate for the high path loss. As mmW antennas have a potentially large number of candidate beamforming directions, to achieve high network throughput, the finding of a high gain direction between a base station and each mobile in the mmW network may involve a large overhead if training signals are directly sent along all possible directions or according to a large volume of codebook. Taking advantage of the block sparse characteristics of the mmW channel and coexistence of legacy antennas, we propose a comprehensive design for more efficient beam direction finding. Different from existing compressive-sensing-based schemes which just take a random subset of directions to measure, taking advantage of the path clustering feature of the mmW channel, we develop a self-adaptive block sparse algorithm which can benefit from preliminary channel estimation during each iteration of the problem solving to significantly improve the overall channel estimation accuracy thus the beam alignment gain. We also explore two methods to exploit co-located legacy antennas to provide further guidance for transmission direction finding. Simulation results indicate that our proposed beam alignment scheme outperforms the baseline and peer schemes in terms of the beamforming gain and training cost. By taking advantage of the block sparse properties of mmW channel, our proposed design is able to achieve the transmission throughput comparable with the exhaustive direction search at much lower overhead.

Network-wide monitoring is important for many network functions. Due to the need of sampling to reduce high measurement cost, system failure, and unavoidable data transmission loss, network monitoring systems suffer from the incompleteness of network monitoring data. Different from the traditional network monitoring data estimation problem which aims to infer all missing monitoring data entries with incomplete measurement data, we study a challenging problem of inferring the top-$k$largest monitoring data entries. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor compared to that based on a 2-D matrix. Taking full advantage of the multilinear structures, we apply tensor completion to first recover the missing data and then find the top-$k$data entries. To reduce the computational overhead, we propose a novel discrete tensor completion model which uses binary codes to represent the factor matrices. Based on the model, we further propose three novel techniques to speed up the whole top-$k$entry inference process: a discrete optimization algorithm to train the binary factor matrices, bit operations to facilitate quick missing data inference, and simplifying the finding of top-$k$largest entries with binary code partition. In our discrete tensor completion model, only one bit is needed to represent the entry in the factor matrices instead of a real value (32 bits) needed in traditional tensor completion model, thus the storage cost is reduced significantly. To quickly infer the top-$k$largest data entries when measurement data arrive sequentially, we also propose a sliding window based online algorithm using the discrete tensor completion model. Extensive experiments using five real data sets and one synthetic data set demonstrate that compared with the state of art tensor completion algorithms, our discrete tensor completion algorithm can achieve similar top-$k$entry inference accuracy using significantly smaller time and storage space.

Packet classification is a critical component in network appliances. Software Defined Networking and cloud computing update the rulesets frequently for flexible policy configuration. Tuple Space Search (TSS), implemented in Open vSwitch (OVS), achieves fast rule updating at the sacrifice of the classification rate. In TSS, each tuple is managed by a hash table and classifying a packet needs to go through all hash tables. Merging tuples can reduce the number of hash tables, but inevitably increases the hash conflicts that may even worsen the classification performance in some cases. No existing algorithm meets the need of both fast packet classification and online rule updating. In this paper, we propose Convolutional Neural Network (CNN)-based Range Partition (CRP) to achieve fast packet classification and online update simultaneously. CRP exploits CNN-based image recognition to quickly partition tuples into range spaces upon the change of ruleset distribution, which reduces hash operations while avoiding rule overlapping caused by hashing many rules to the same location of the hash table. Experimental results demonstrate that CRP achieves$3.2\times $classification speed and$4.2\times $update speed on average compared with state-of-the-art algorithms. We also implement CRP in OVS. The throughput of CRP-OVS is$10\times $that of native OVS.

Sharing a service among multiple users could bring benefit to users by reducing their service price and also benefit service providers by allowing them to make more profit. Shared services usually have a capacity limit. To construct a win-win situation between users and providers through service sharing, it is necessary to reasonably organize users with similar service requests into groups under the limits of group sizes. This paper explores methods to enable user grouping for sharing services with the limit on the group size. Based on user request descriptions and evaluation, a connection relation is built to present which two users could be in a group. Semi-groups can be derived from the connection relation and two grouping schemes satisfying the group size limit are further determined through proposed algorithms to meet different service expectations. Finally, a case study and a simulation are given to demonstrate the effectiveness of the proposed methods in grouping users to provide higher service benefits to both users and providers.

Multi-task learning (MTL) is a common paradigm that seeks to improve the generalization performance of task learning by training related tasks simultaneously. However, it is still a challenging problem to search the flexible and accurate architecture that can be shared among multiple tasks. In this paper, we propose a novel deep learning model called Task Adaptive Activation Network (TAAN) that can automatically learn the optimal network architecture for MTL. The main principle of TAAN is to derive flexible activation functions for different tasks from the data with other parameters of the network fully shared. We further propose two functional regularization methods that improve the MTL performance of TAAN. The improved performance of both TAAN and the regularization methods is demonstrated by comprehensive experiments.

Monitoring the performance of a large network is very costly. Instead, a subset of paths or time intervals of the network can be measured while inferring the remaining network data by leveraging their spatiotemporal correlations. The quality of missing data recovery highly relies on the inference algorithms. Tensor completion has attracted some recent attentions with its capability of exploiting the multi-dimensional data structure for more accurate missing data inference. However, current tensor completion algorithms only model the three-order interaction of data features through the inner product, which is insufficient to capture the high-order, nonlinear correlations across different feature dimensions. In this paper, we propose a novel Neural Tensor Completion (NTC) scheme to effectively model three-order interaction among data features with the outer product and build a 3D interaction map. Based on which, we apply 3D convolution to learn features of high-order interaction from the local range to the global range. We demonstrate this will lead to good learning ability. We conduct extensive experiments on two real-world network monitoring datasets, Abilene and WS-DREAM, to demonstrate that NTC can significantly reduce the error in missing data recovery. When the sampling ratio is low at 1%, the recovery error ratios on the testing data are around 0.05 (Abilene) and 0.13 (WS-DREAM) when using NTC, but are 0.99 (Abilene) and 0.99 (WS-DREAM) using the best current tensor completion algorithms, which are 21 times and 8 times larger.

In multi-party interactive live streaming, each user can act as both the sender and the receiver of a live video stream. Designing adaptive bitrate (ABR) algorithm for such applications poses three challenges: (i) due to the interaction requirement among the users, the playback buffer has to be kept small to reduce the end-to-end delay; (ii) the algorithm needs to decide what is the bitrate to receive and what is the set of bitrates to send; (iii) the delay and quality requirements between each pair of users may differ, for instance, depending on whether the pair is interacting directly with each other. To address these challenges, we first develop a quality of experience (QoE) model for multi-party live streaming applications. Based on this model, we design MultiLive, an adaptive bitrate control algorithm for the multi-party scenario. MultiLive models the many-to-many ABR selection problem as a non-linear programming problem. Solving the non-linear programming equation yields the target bitrate for each pair of sender-receiver. To alleviate system errors during the modeling and measurement process, we update the target bitrate through the buffer feedback adjustment. To address the throughput limitation of the uplink, we cluster the ideal streams into a few groups, and aggregate these streams through scalable video coding for transmissions. We conduct extensive trace-driven simulations to evaluate the algorithm. The experimental results show that MultiLive outperforms the fixed bitrate algorithm, with 2-5× improvement in average QoE. Furthermore, the end-to-end delay is reduced to around 100 ms, much lower than the 400 ms threshold recommended for video conferencing.

Anomaly diagnosis is a critical task for building a reliable cloud system and speeding up the system recovery form failures. With the increase of scales and applications of clouds, they are more vulnerable to various anomalies, and it is more challenging for anomaly troubleshooting. System logs that record significant events at critical time points become excellent sources of information to perform anomaly diagnosis. Never-theless, existing log-based anomaly diagnosis approaches fail to achieve high precision in highly concurrent environments due to interleaved unstructured logs. Besides, transient anomalies that have no obvious features are hard to detect by these approaches. To address this gap, this paper proposes LogSayer, a log pattern-driven anomaly detection model. LogSayer represents the system state by identifying suitable statistical features (e.g. frequency, surge), which are not sensitive to the exact log sequence. It then measures changes in the log pattern when a transient anomaly occurs. LogSayer uses Long Short-Term Memory (LSTM) neural networks to learn the historical correlation of log patterns and applies a BP neural network for adaptive anomaly decisions. Our experimental evaluations over the HDFS and OpenStack data sets show that LogSayer outperforms the state-of-the-art log-based approaches with precision over 98%.

Image translation across diverse domains has attracted more and more attention. Existing multi-domain image-to-image translation algorithms only learn the features of the complete image without considering specific features of local instances. To ensure the important instance to be more realistically translated, we propose a cross-granularity learning model for multi-domain image-to-image translation. We provide detailed procedures to capture the features of instances during the learning process, and specifically learn the relationship between style of the global image and the style of an instance on the image through the enforcing of the cross-granularity consistency. In our design, we only need one generator to perform the instance-aware multi-domain image translation. Our extensive experiments on several multi-domain image-to-image translation datasets show that our proposed method can achieve superior performance compared with the state-of-the-art approaches.

Outfit recommendation requires the answers of some challenging outfit compatibility questions such as 'Which pair of boots and school bag go well with my jeans and sweater?'. It is more complicated than conventional similarity search, and needs to consider not only visual aesthetics but also the intrinsic fine-grained and multi-category nature of fashion items. Some existing approaches solve the problem through sequential models or learning pair-wise distances between items. However, most of them only consider coarse category information in defining fashion compatibility while neglecting the fine-grained category information often desired in practical applications. To better define the fashion compatibility and more flexibly meet different needs, we propose a novel problem of learning compatibility among multiple tuples (each consisting of an item and category pair), and recommending fashion items following the category choices from customers. Our contributions include: 1) Designing a Mixed Category Attention Net (MCAN) which integrates both fine-grained and coarse category information into recommendation and learns the compatibility among fashion tuples. MCAN can explicitly and effectively generate diverse and controllable recommendations based on need. 2) Contributing a new dataset IQON, which follows eastern culture and can be used to test the generalization of recommendation systems. Our extensive experiments on a reference dataset Polyvore and our dataset IQON demonstrate that our method significantly outperforms state-of-the-art recommendation methods.

The attacks, faults, and severe communication/system conditions in Mobile Crowd Sensing (MCS) make false data detection a critical problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Depending on the type of data corruption, random or successive/mass, we design two versions of LightLRFMS. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 20 times faster speed thanks to its lower computation cost.

Gaining a full knowledge of end-to-end network performance is important for some advanced network management and services. Although it becomes increasingly critical, end-to-end network monitoring usually needs active probing of the path and the overhead will increase quadratically with the number of network nodes. To reduce the measurement overhead, matrix completion is proposed recently to predict the end-to-end network performance among all node pairs by only measuring a small set of paths. Despite its potential, applying matrix completion to recover the missing data suffers from low recovery accuracy and long recovery time. To address the issues, we propose MC-GPU to exploit Graphics Processing Units (GPUs) to enable parallel matrix factorization for high-speed and highly accurate Matrix Completion. To well exploit the special architecture features of GPUs for both task independent and data-independent parallel task execution, we propose several novel techniques: similar OD (origin and destination) pairs reordering taking advantage of the locality-sensitive hash (LSH) functions, balanced matrix partition, and parallel matrix completion. We implement the proposed MC-GPU on the GPU platform and evaluate the performance using real trace data. We compare the proposed MC-GPU with the state of the art matrix completion algorithms, and our results demonstrate that MC-GPU can achieve significantly faster speed with high data recovery accuracy.

Monitoring the performance of a large network would involve a high measurement cost. To reduce the overhead, sparse network monitoring techniques may be applied to select paths or time intervals to take the measurements, while the remaining monitoring data can be inferred leveraging the spatial-temporal correlations among data. The quality of missing data recovery, however, highly relies on the specific inference technique adopted. Tensor completion is a promising technique for more accurate missing data inference by exploiting the multi-dimensional data structure. However, data processing for higher dimensional tensors involves a large amount of computation, which prevents conventional tensor completion algorithms from practical application in the presence of large amount of data. This work takes the initiative to investigate the potential and methodologies of performing parallel processing for high-speed and high accuracy tensor completion over Graphics Processing Units (GPUs). We propose a GPU-accelerated parallel Tensor Completion scheme (GPU-TC) for accurate and fast recovery of missing data. To improve the data recovery accuracy and speed, we propose three novel techniques to well exploit the tensor factorization structure and the GPU features: grid-based tensor partition, independent task assignment based on Fisher-Yates shuffle, sphere facilitated and memory-correlated scheduling. We have conducted extensive experiments using network traffic trace data to compare the proposed GPU-TC with the state of art tensor completion algorithms and matrix-based algorithms. The experimental results demonstrate that GPU-TC can achieve significantly better performance in terms of two relative error ratio metrics and computation time.

Graph convolutional neural networks (GCNN) have become an increasingly active field of research. It models the spatial dependencies of nodes in a graph with a pre-defined Laplacian matrix based on node distances. However, in many application scenarios, spatial dependencies change over time, and the use of fixed Laplacian matrix cannot capture the change. To track the spatial dependencies among traffic data, we propose a dynamic spatio-temporal GCNN for accurate traffic forecasting. The core of our deep learning framework is the finding of the change of Laplacian matrix with a dynamic Laplacian matrix estimator. To enable timely learning with a low complexity, we creatively incorporate tensor decomposition into the deep learning framework, where real-time traffic data are decomposed into a global component that is stable and depends on long-term temporal-spatial traffic relationship and a local component that captures the traffic fluctuations. We propose a novel design to estimate the dynamic Laplacian matrix of the graph with above two components based on our theoretical derivation, and introduce our design basis. The forecasting performance is evaluated with two realtime traffic datasets. Experiment results demonstrate that our network can achieve up to 25% accuracy improvement.

Restricted Boltzmann Machine (RBM) is an essential component in many machine learning applications. As a probabilistic graphical model, RBM posits a shallow structure, which makes it less capable of modeling real-world applications. In this paper, to bridge the gap between RBM and artificial neural network, we propose an energy-based probabilistic model that is more flexible on modeling continuous data. By introducing the pair-wise inverse autoregressive flow into RBM, we propose two generalized continuous RBMs which contain deep neural network structure to more flexibly track the practical data distribution while still keeping the inference tractable. In addition, we extend the generalized RBM structures into sequential setting to better model the stochastic process of time series. Performance improvements on probabilistic modeling and representation learning are demonstrated by the experiments on diverse datasets.

Xuewen Yang, Yingru Liu, Dongliang Xie, Xin Wang, Niranjan Balasubramanian. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

With the proliferation of globally-distributed services and the quick growth of user requests for inter-datacenter bandwidth, cloud providers have to lease a good deal of bandwidth from Internet service providers to satisfy the user demands. Neither maximizing the service revenue nor minimizing the service cost can bring the maximal service profit to cloud providers. The diversity of user requests and the large unit of inter-datacenter bandwidth further increase the difficulty of scheduling user requests. In this paper, we propose a cloud operational model to help cloud providers to make more service profit by properly selecting requests to serve rather than serving all user requests. We formulate the problem of service profit maximization and prove its NP-hardness. Considering the complicated coupling between maximizing revenue and minimizing cost, we propose a framework, Metis, for the efficient scheduling of user requests over inter-datacenter networks to maximize the service profit for cloud providers. Metis is formed with the alternate operations of two algorithms derived from randomized rounding techniques and Chernoff-Hoeffding bound. We prove that they can provide the guarantees on approximation ratios. Our extensive evaluations demonstrate that Metis can achieve more than 1.3x the service profits of existing solutions.

The aim of this work is to more accurately model the stochastic process of music-related data, which is essential for many AI applications in musicology. When music is naturally represented as a sequence of vectorized frames, existing models generally cannot well capture the correlation of the elements inside each frame. We propose an energy-based model called Chain Graphical Recurrent Neural Network (CGRNN) to explore the correlation of elements for more accurate modeling of the dynamics of music. In CGRNN, a probabilistic substructure named Conditional spike and slab Restricted Boltzmann Machine (C-ssRBM) is defined to better model the conditional covariance and joint distribution of elements in a frame. Besides, CGRNN is capable of tracking the evolution of music and extracting sparse features with an efficient design of temporal transition. With the estimated stochastic process of music, we further implement CGRNN to generate melodious music automatically. Extensive empirical evaluations of multiple unsupervised learning tasks are conducted on symbolic MIDI and audio sounds to demonstrate the performance of our model.

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms usually work off-line and cannot timely detect anomalies. They also suffer from high cost for storage and computation. Although online and accurate traffic anomaly detection is very important, it very difficult to achieve. We propose to utilize tensor model to well exploit the multi-dimensional information hidden in the traffic data for more accurate online Internet anomaly detection. We decouple the tensor recovery problem to iteratively solve two sub problems, a tensor factorization sub-problem and an anomaly detection sub-problem. To reduce the high cost for computation and storage involved in tensor factorization, we propose two lightweight techniques to effectively derive factor matrices of tensor in the current window and iteration, taking advantage of tensor decomposition results of the previous window and iteration. We have done extensive experiments using two real traffic traces to compare with three tensor based algorithms and three matrix based algorithms. The experiment results demonstrate that our online anomaly detection algorithm can achieve the same anomaly detection accuracy as that of the best offline tensor based algorithm, but at 6100 times faster speed and with very low storage cost.

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.

Finding top- k elephant flows is a critical task in network measurement, with applications such as congestion control, anomaly detection, and traffic engineering. Traditional top- k flow detection problem focuses on using a small amount of memory to measure the total number of packets or bytes of each flow. Instead, we study a challenging problem of inferring the top- k elephant flows in a practical system with incomplete measurement data as a result of sub-sampling for scalability or data missing. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor compared to that based on a 2-D matrix. Taking full advantage of the multilinear structures, we apply tensor completion to first recover the missing data and then find the top- k elephant flows. To reduce the computational overhead, we propose a novel discrete tensor completion model which uses binary codes to represent the factor matrices. Based on the model, we further propose three novel techniques to speed up the whole top- k flow inference process: a discrete optimization algorithm to train the binary factor matrices, bit operations to facilitate quick missing data inference, and simplifying the finding of top- k elephant flows with binary code partition. In our discrete tensor completion model, only one bit is needed to represent the entry in the factor matrices instead of a real value (32 bits) needed in traditional tensor completion model, thus the storage cost is reduced significantly. Extensive experiments using two real traces demonstrate that compared with the state of art tensor completion algorithms, our discrete tensor completion algorithm can achieve similar data inference accuracy using significantly smaller time and storage space.

With the proliferation of location-aware devices, trajectory data have been widely collected, published, and analyzed in real-life applications. However, published trajectory data often contain sensitive attributes, so an attacker who can identify an individual from such data through record linkage, attribute linkage, or similarity attacks can gain sensitive information about this individual. To resist from these attacks, we propose a scheme called Data Privacy Preservation with Perturbation (DPPP). To protect the privacy of sensitive information, we first determine those critical location sequences that can identify specific individuals. Then we perturb these sequences by adding or deleting some moving points while ensuring the published data satisfy (l, α, β)-privacy, an enhanced privacy model from ldiversity. Our experiments on both synthetic and real-life datasets suggest that DPPP achieves better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory.

A major factor that prevents the large scale deployment of Mobile Crowd Sensing (MCS) is its sensing and communication cost. Given the spatio-temporal correlation among the environment monitoring data, matrix completion (MC) can be exploited to only monitor a small part of locations and time, and infer the remaining data. Rather than only taking random measurements following the basic MC theory, to further reduce the cost of MCS while ensuring the quality of missing data inference, we propose an Active Sparse MCS (AS-MCS) scheme which includes a bipartite-graph-based sensing scheduling scheme to actively determine the sampling positions in each upcoming time slot, and a bipartite-graph-based matrix completion algorithm to robustly and accurately recover the un-sampled data in the presence of sensing and communications errors. We also incorporate the sensing cost into the bipartite-graph to facilitate low cost sample selection and consider the incentives for MCS. We have conducted extensive performance studies using the data sets from the monitoring of PM 2.5 air condition and road traffic speed, respectively. Our results demonstrate that our AS-MCS scheme can recover the missing data at very high accuracy with the sampling ratio only around $11%$, while the peer matrix completion algorithms with similar recovery performance requires up to 4-9 times the number of samples of ours for both the data sets.

The need of highly directional communications at mmWave frequencies introduces high overhead for beam training and alignment, which makes the medium access control (MAC) a grand challenge. To harvest the gain for high performance transmissions in mmWave networks, we propose an efficient and integrated MAC design with the concurrent support of three closely interactive components: 1) an accurate and low-cost beam training methodology with a) multiuser, multi-level, bi-directional coarse training for fast user association and beam alignment and b) adaptive fine beam training with compressed channel measurement and multi-resolution block-sparse channel estimation in response to the channel condition and the learning from past measurements; 2) an elastic virtual resource scheduling scheme that jointly considers beam training, beam tracking and data transmissions while enabling burst data transmissions with the concurrent allocation of transmission rate and duration; and 3) a flexible and efficient beam tracking strategy to enable stable beam alignment with beamwidth adaptation and mobility estimation. Compared with literature studies, our performance results demonstrate that our design can effectively reduce the training overhead and thus significantly improve the throughput. Compared to 802.11ad, the training overhead can be reduced more than 60%, and the throughput can be more than 75% higher. In low SNR case, the throughput gain can be more than 90%. Our scheme can also achieve about 50% higher throughput in the presence of user mobility.

The prediction of short-term volatile traffic becomes increasingly critical for efficient traffic engineering in intelligent transportation systems. Accurate forecast results can assist in traffic management and pedestrian route selection, which will help alleviate the huge congestion problem in the system. This paper presents a novel hybrid DTMGP model to accurately forecast the volume of passenger flows multi-step ahead with the comprehensive consideration of factors from temporal, origin-destination spatial, and frequency and self-similarity perspectives. We first apply discrete wavelet transform to decompose the traffic volume series into an appropriation component and several detailed components. Then we propose a more efficient tracking model to forecast the appropriation component and a novel Gaussian process model to forecast the detailed components. The forecasting performance is evaluated with real-time passenger flow data in Chongqing, China. Simulation results demonstrate that our hybrid model can achieve on average 20%-50% accuracy improvement, especially during rush hours.

The inference of the network traffic data from partial measurements data becomes increasingly critical for various network engineering tasks. By exploiting the multi-dimensional data structure, tensor completion is a promising technique for more accurate missing data inference. However, existing tensor completion algorithms generally have the strong assumption that the tensor data have a global low-rank structure, and try to find a single and global model to fit the data of the whole tensor. In a practical network system, a subset of data may have stronger correlation. In this work, we propose a novel localized tensor completion model (LTC) to increase the data recovery accuracy by taking advantage of the stronger local correlation of data to form and recover sub-tensors each with a lower rank. Despite that it is promising to use local tensors, the finding of correlated entries faces two challenges, the data with adjacent indexes are not ones with higher correlation and it is difficult to find the similarity of data with missing tensor entries. To conquer the challenges, we propose several novel techniques: efficiently calculating the candidate anchor points based on locality-sensitive hash (LSH), building sub-tensors around properly selected anchor points, encoding factor matrices to facilitate the finding of similarity with missing entries, and similarity-aware local tensor completion and data fusion. We have done extensive experiments using real traffic traces. Our results demonstrate that LTC is very effective in increasing the tensor recovery accuracy without depending on specific tensor completion algorithms.

With the quick growth of traffic between data centers, inefficient transfer scheduling in inter-datacenter networks can lead to a huge waste of bandwidth thus significant bandwidth cost. Previous work have explored different ways, such as software-defined WANs and dynamic pricing mechanisms, to overcome the inefficiency of inter-datacenter networks. However, there is a big challenge in addressing the fundamental conflicts between the deadline-aware transfer scheduling and minimizing the bandwidth cost. Unlike existing efforts that schedule inter-datacenter transfers under fixed link capacities, wherein some deadlines are violated and the service quality is degraded, we aim to finish all the transfers on time with as little bandwidth as possible to minimize the bandwidth cost. We take into account the variation of bandwidth price and the deadline requirements of services, and formulate the problem of cost-efficient scheduling of bulk transfers with deadline guarantee, which is shown to be NP-hard. Benefitting from the relax-and-round method, we propose a progressively-descending algorithm (PDA) to schedule bulk transfers and meet the above goals with a guaranteed approximation ratio. We apply our algorithm in a bulk transfer scheduler, Butler, and build a small-scale testbed to evaluate its efficiency. Both large-scale simulation and testbed experiment results validate the ability of our scheme on cutting down the bandwidth cost. Compared with existing approaches, it reduces up to 60% bandwidth cost and increases the network utilization by up to 140%.

Offloading computation intensive applications to mobile cloud is promising for overcoming the problems of limited computational resources and energy of mobile devices. However, without considering the competition relationship of mobile users and cloudlets in the mobile cloud computing system, existing studies lack an incentive mechanism for the system to achieve efficient application offloading and cloud resource provisioning. In this paper, we design MPTMG, a Multi-dimensional Pricing mechanism based on Two-sided Market Game. We propose three types of prices: a multi-dimensional price corresponding to multi-dimensional resource allocation, a penalty price to encourage fair and high quality cloud services, and a benefit discount factor to motivate more even provisioning of resources on different dimensions in the cloud. Based on these prices, we propose a distributed price-adjustment algorithm for efficient resource allocation and QoS-aware offloading scheduling. We prove that the algorithm can converge in a finite number of iterations to the equilibrium core allocation at which the mobile cloud system achieves the Pareto efficiency by maximizing the total system benefit. To the best of our knowledge, this is the first paper that applies economic theories and pricing mechanisms to manage application offloading in mobile cloud systems. The simulation results demonstrate that our proposed pricing mechanism can significantly improve the system performance.

Ensuring transmission survivability is a crucial problem for high-speed networks. Path protection is a fast and capacity-efficient approach for increasing the availability of end to end connections. The emerging SDN infrastructure makes it feasible to provide diversity routing in a practical network. For more robust path protection, it is desirable to provide an alternative path that does not share any risk resource with the active path. We consider finding the SRLG-Disjoint paths, where a Shared Risk Link Group (SRLG) is a group of network links that share a common physical resource whose failure will cause the failure of all links of the group. Since the traffic is carried on the active path most of time, it is useful that the weight of the shorter path of the disjoint path pair is minimized, and we call it Min-Min SRLG-Disjoint routing problem. The key issue faced by SRLG-Disjoint routing is the trap problem, where the SRLG-disjoint backup path (BP) can not be found after an active path (AP) is decided. Based on the min-cut of the graph, we design an efficient algorithm that can take advantage of existing search results to quickly look for the SRLG-Disjoint path pair. Our performance studies demonstrate that our algorithm can outperform other approaches with a higher routing performance while also at a much faster speed.

Tensor completion can be applied to fill in the missing data, which is import for many data applications where the data are incomplete. To infer the missing data, existing tensor-completion algorithms generally assume that the tensor data have global low-rank structure and apply a single model to fit the overall observed data through the global optimization. However, there are different correlation levels among application data, thus the ranks of some sub-tensors can be even lower relative to that of the large tensor. Fitting a single model to all data will compromise the performance of data recovery. To increase the accuracy in missing data recovery, we propose to apply local tensor completion (Local-TC) to recover data from sub-tensors, with each containing data of higher correlations. Although promising, as the tensor data are only organized logically, it is difficult to determine the relationship among data. We propose to exploit locality-sensitive hash (LSH) to quickly find the data correlation and reorganize tensor data, based on which data entries with high correlations are put into the same sub-tensor. The experiment results demonstrate that Local-TC is very effective in increasing the recovery accuracy.

Modern communication technologies are expected to be available in the future Smart Grids to enable the control of equipments over the whole power grid. In this paper, we consider such networked control approach to address failures that may occur at any location of the grid, due to attacks or unit malfunction, and provide a wide-scale solution that prevent the failure impacts from spreading over a large area. Different from literature work that focuses on modifying power equations under the standard constraints of the power system, we estimate the impact of controlling different nodes on topological areas of the grid based on social metrics, which are derived from the graph capturing both the topological and electrical properties of the power grid. We propose a failure control algorithm for topological containment of failures in smart grid. Our algorithm also takes careful consideration of the impact the planned control has on the grid to avoid the possibly involuntary failure extension. We show that social metrics can efficiently trade off between the topological and electrical characteristics revealed by the power grid graph representation. We evaluate the performance against networked control strategies that only use power models to determine the actions to be performed at power nodes. Our results show that the proposed control scheme can effectively contain failures within their original location range.

Detecting anomalous traffic is a crucial task of managing networks. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this paper takes an initiative to investigate the potential and methodologies of performing tensor factorization for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). We have conducted extensive experiments using Internet traffic trace data Abilene and GEANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-Trcan achieve significantly lower False Positive Rate and higher True Positive Rate.

State-of-the-art techniques in Generative Adversarial Networks (GANs) have shown remarkable success in image-to-image translation from peer domain X to domain Y using paired image data. However, obtaining abundant paired data is a non-trivial and expensive process in the majority of applications. When there is a need to translate images across n domains, if the training is performed between every two domains, the complexity of the training will increase quadratically. Moreover, training with data from two domains only at a time cannot benefit from data of other domains, which prevents the extraction of more useful features and hinders the progress of this research area. In this work, we propose a general framework for unsupervised image-to-image translation across multiple domains, which can translate images from domain X to any a domain without requiring direct training between the two domains involved in image translation. A byproduct of the framework is the reduction of computing time and computing resources since it needs less time than training the domains in pairs as is done in state-of-the-art works. Our proposed framework consists of a pair of encoders along with a pair of GANs which learns high-level features across different domains to generate diverse and realistic samples from. Our framework shows competing results on many image-to-image tasks compared with state-of-the-art techniques.

It is very difficult to establish and maintain end-to-end connections in a vehicle ad hoc network (VANET) as a result of high vehicle speed, long inter-vehicle distance, and varying vehicle density. Instead, a store-and-forward strategy has been considered for vehicle communications. The success of this strategy, however, depends heavily on the cooperation among nodes. Different from exiting store-and-forward solutions, we propose predictive routing based on the hidden Markov model (PRHMM) for VANETS, which exploits the regularity of vehicle moving behaviors to increase the transmission performance. As vehicle movements often exhibit a high degree of repetition, including regular visits to certain places and regular contacts during daily activities, we can predict a vehicle's future locations based on the knowledge of past traces and the hidden Markov model. Consequently, the short-term route of a vehicle and its packet delivery probability for a specific mobile destination can be predicted. Moreover, PRHMM enables seamless handoff between vehicle-to-vehicle and vehicle-to-infrastructure communications so that the transmission performance will not be constrained by the vehicle density and moving speed. Simulation evaluation demonstrates that PRHMM performs much better in terms of delivery ratio, end-to-end delay, traffic overhead, and buffer occupancy.

Matrix completion has emerged very recently and provides a new venue for low cost data gathering in Wireless Sensor Networks (WSNs). Existing schemes often assume that the data matrix has a known and fixed low-rank, which is unlikely to hold in a practical system for environment monitoring. Environmental data vary in temporal and spatial domains. By analyzing a large set of weather data collected from 196 sensors in ZhuZhou, China, we reveal that weather data have the features of low-rank, temporal stability, and relative rank stability. Taking advantage of these features, we propose an on-line data gathering scheme based on matrix completion theory, named MC-Weather, to adaptively sample different locations according to environmental and weather conditions. To better schedule sampling process while satisfying the required reconstruction accuracy, we propose several novel techniques, including three sample learning principles, an adaptive sampling algorithm based on matrix completion, and a uniform time slot and cross sample model. With these techniques, our MC-Weather scheme can collect the sensory data at required accuracy while largely reducing the cost for sensing, communication, and computation. We perform extensive simulations based on the data traces from weather monitoring and the simulation results validate the efficiency and efficacy of the proposed scheme.

In this paper, we present a cross-layer design for a family of OFDM-based video communications by jointly considering application layer information and the wireless channel conditions. Compared with traditional cross-layer designs, our proposed method targets to efficiently transmit video data generated with some emerging techniques for better wireless transmissions, where the video data are divided into multiple chunks and each chunk contributes independent distortion to the entire video quality. To minimize the end-to-end distortion, we formulate a generalized optimization problem and derive a joint optimal carrier matching and power allocation scheme. Rather than depending on a specific video encoding method as done in the conventional work, we intend our design to be applicable to a general family of new video schemes. We apply our proposed method to two applications, the enhanced analog coding and the uncompressed video transmission over OFDM. In both applications, the performance can be improved by adopting our scheme. Simulation results validate the effectiveness of our approach in achieving significantly better PSNR and visual quality compared to reference schemes.

The support for high data rate applications with the cognitive radio technology necessitates wideband spectrum sensing. However, it is costly to apply long-term wideband sensing and is especially difficult in the presence of uncertainty, such as high noise, interference, outliers, and channel fading. In this work, we propose scheduling of sequential compressed spectrum sensing which jointly exploits compressed sensing (CS) and sequential periodic detection techniques to achieve more accurate and timely wideband sensing. Instead of invoking CS to reconstruct the signal in each period, our proposed scheme performs backward grouped-compressed-data sequential probability ratio test (backward GCD-SPRT) using compressed data samples in sequential detection, while CS recovery is only pursued when needed. This method on one hand significantly reduces the CS recovery overhead, and on the other takes advantage of sequential detection to improve the sensing quality. Furthermore, we propose (a) an in-depth sensing scheme to accelerate sensing decision-making when a change in channel status is suspected, (b) a block-sparse CS reconstruction algorithm to exploit the block sparsity properties of wide spectrum, and (c) a set of schemes to fuse results from the recovered spectrum signals to further improve the overall sensing accuracy. Extensive performance evaluation results show that our proposed schemes can significantly outperform peer schemes under sufficiently low SNR settings.

The introduction of wireless transmissions into the data center has shown to be promising in improving cost effectiveness of data center networks (DCNs). For high transmission flexibility and performance, a fundamental challenge is to increase the wireless availability and enable fully hybrid and seamless transmissions over both wired and wireless DCN components. Rather than limiting the number of wireless radios by the size of top-of-rack switches, we propose a novel DCN architecture, Diamond, which nests the wired DCN with radios equipped on all servers. To harvest the gain allowed by the rich reconfigurable wireless resources, we propose the low-cost deployment of scalable 3-D ring reflection spaces (RRSs) which are interconnected with streamlined wired herringbone to enable large number of concurrent wireless transmissions through high-performance multi-reflection of radio signals over metal. To increase the number of concurrent wireless transmissions within each RRS, we propose a precise reflection method to reduce the wireless interference. We build a 60-GHz-based testbed to demonstrate the function and transmission ability of our proposed architecture. We further perform extensive simulations to show the significant performance gain of diamond, in supporting up to five times higher server-to-server capacity, enabling network-wide load balancing, and ensuring high fault tolerance.

The cognitive radio (CR) technology holds promise to significantly increase spectrum availability and wireless network capacity. With more spectrum bands opened up for CR use, it is critical yet challenging to perform efficient wideband sensing. We propose an integrated sequential wideband sensing scheduling framework that concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower-cost spectrum sensing. First, to ensure more timely detection without incurring high overhead involved in periodic recovery of CS signals, we propose smart scheduling of a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further help users under severe channel conditions identify the occupied sub-channels, we develop two collaborative strategies, namely, joint reconstruction of the signals among neighboring users and wideband sensing-map fusion. Third, to achieve robust wideband sensing, we propose the use of anomaly detection in our framework. Extensive simulations demonstrate that our approach outperforms peer schemes significantly in terms of sensing delay, accuracy and overhead.

Phasor Measurement Units (PMUs) provide high precision data at high sampling rates to support Smart Grid applications. Power Line Outage detection mechanisms can enhance the grid reliability by assisting power operators in taking proper control actions. Despite the potential provided by PMUs, there are very limited efforts on exploiting data available to more effectively detect outages. Conventional outage detection schemes are mostly designed based on simplified power models, and the limited work on detection with data either assume all the measurement samples are available or ignore the missing entries. Their performance suffers in the complex grid conditions in the presence of missing data. In this paper, we design a detection mechanism considering unreliable data, in the form of missing data samples. Detection is performed through the grouping of nodes according to their data availability and their learned detection capabilities. To enable the robust detection of power line outages, we propose learning outage characteristics for each individual node instead of specific single line outage scenarios. Our results show that the outages detected are highly consistent with the evaluated failures under different scenarios, with high accuracy and low false positive rates. Moreover, the detection application is resilient to unreliable data, and can properly differentiate data problems from physical power line failures.

The inference of the network traffic matrix from partial measurement data becomes increasingly critical for various network engineering tasks, such as capacity planning, load balancing, path setup, network provisioning, anomaly detection, and failure recovery. The recent study shows it is promising to more accurately interpolate the missing data with a three-dimensional tensor as compared to interpolation methods based on two-dimensional matrix. Despite the potential, it is difficult to form a tensor with measurements taken at varying rate in a practical network. To address the issues, we propose Reshape-Align scheme to form the regular tensor with data from dynamic measurements, and introduce user-domain and temporal-domain factor matrices which takes full advantage of features from both domains to translate the matrix completion problem to the tensor completion problem based on CP decomposition for more accurate missing data recovery. Our performance results demonstrate that our Reshape-Align scheme can achieve significantly better performance in terms of two metrics: error ratio and mean absolute error (MAE).

Detecting anomalous traffic is a critical task for advanced Internet management. The traditional approaches based on Principal Component Analysis (PCA) are effective only when the corruption is caused by small additive i.i.d. Gaussian noise. The recent Direct Robust Matrix Factorization (DRMF) is proven to be more robust and accurate in anomaly detection, but it incurs a high computation cost due to its need of singular value decomposition (SVD) for low-rank matrix approximation and the iterative use of SVD execution to find the final solution. To enable the anomaly detection for large traffic matrix with the use of DRMF, we formulate the low-rank matrix approximation problem as a problem of searching for the subspace to project the traffic matrix with the minimum error. We propose a novel approach, LSH-subspace, for fast low-rank matrix approximation. To facilitate the matrix partition for the quick search of the subspace, we propose several novel techniques: a multi-layer locality sensitive hashing (LSH) table to reorder the OD pairs based on LSH function, a partition principle to guide the partition to minimize the projection error, and a lightweight algorithm to exploit the sparsity of the outlier matrix to update the LSH table at low overhead. Our extensive simulations based on real trace data demonstrate that our LSH-subspace is 3 times faster than DRMF with high anomaly detection accuracy.

Multi-path TCP has recently shown great potential to take advantage of the rich path diversity in data center networks (DCN) to increase transmission throughput. However, the small flows, which take a large fraction of data center traffic, will easily get a timeout when split onto multiple paths. Moreover, the dynamic congestions and node failures in DCN will exacerbate the reorder problem of parallel multi-path transmissions for large flows. In this paper, we propose DC2-MTCP (Data Center Coded Multi-path TCP), which employs a fast and light-weight coding method to address the above challenges while maintaining the benefit of parallel multi-path transmissions. To meet the high flow performance in DCN, we insert a very low ratio of coded packets with a careful selection of the packets to be coded. We further present a progressive decoding algorithm to decode the packets online with a low time complexity. Extensive ns2-based simulations show that with two orders of magnitude lower coding delay, DC2-MTCP can reduce on average 40% flow completion time for small flows and increase 30% flow throughput for large flows compared to the peer schemes in varying network conditions.

With the constant increase of social-network data published, the privacy preservation becomes more and more important. Although some literature algorithms apply K-anonymity to the relational data to prevent an adversary from significantly perpetrating privacy breaches, the inappropriate choice of K has a big impact on the quality of privacy protection and data utility. We propose a technique named Relationship Privacy Preservation based on Compressive Sensing (RPPCS) in this paper to anonymize the relationship data of social networks. The network links are randomized from the recovery of the random measurements of the sparse relationship matrix to both preserve the privacy and data utility. Two comprehensive sets of real-world relationship data on social networks are applied to evaluate the performance of our anonymization technique. Our performance evaluations based on Collaboration Network and Gnutella Network demonstrate that our scheme can better preserve the utility of the anonymized data compared to peer schemes. Privacy analysis shows that our scheme can resist the background knowledge attack.

Mobile cloud storage services have gained phenomenal success in recent few years. In this paper, we identify, analyze, and address the synchronization (sync) inefficiency problem of modern mobile cloud storage services. Our measurement results demonstrate that existing commercial sync services fail to make full use of available bandwidth, and generate a large amount of unnecessary sync traffic in certain circumstances even though the incremental sync is implemented. For example, a minor document editing process in Dropbox may result in sync traffic 10 times that of the modification. These issues are caused by the inherent limitations of the sync protocol and the distributed architecture. Based on our findings, we propose QuickSync, a system with three novel techniques to improve the sync efficiency for mobile cloud storage services, and build the system on two commercial sync services. Our experimental results using representative workloads show that QuickSync is able to reduce up to 73.1 percent sync time in our experiment settings.

In cellular networks, it is important to conserve energy while at the same time satisfying different user performance requirements. In this paper, we first propose a comprehensive metric to capture the user performance cost due to task delay, deadline violation, different application profiles, and user preferences. We prove that finding the energy-optimal scheduling solution while meeting the requirements on the performance cost is NP-hard. Then, we design an adaptive online scheduling algorithm PerES to minimize the total energy cost on data transmissions subject to user performance constraints. We prove that PerES can make the energy consumption arbitrarily close to that of the optimal scheduling solution. Further, we develop offline algorithms to serve as the evaluation benchmark for PerES. The evaluation results demonstrate that PerES achieves average 2.5 times faster convergence speed compared to state-of-art static methods, and also higher performance than peers under various test conditions. Using 821 million traffic flows collected from a commercial cellular carrier, we verify our scheme could achieve on average 32-56 percent energy savings over the total transmission energy with different levels of user experience.

Affected by hardware and wireless conditions in WSNs, raw sensory data usually have notable data loss and corruption. Existing studies mainly consider the interpolation of random missing data in the absence of the data corruption. There is also no strategy to handle the successive missing data. To address these problems, this paper proposes a novel approach based on matrix completion (MC) to recover the successive missing and corrupted data. By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou, China, we verify that weather data have the features of low-rank, temporal stability, and spatial correlation. Moreover, from simulations on the real weather data, we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way. Motivated by these observations, we propose a novel Principal Component Analysis (PCA)-based scheme to efficiently identify the existence of data corruption. We further propose a two-phase MC-based data recovery scheme, named MC-Two-Phase, which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data matrix due to either data missing or corruption. Finally, the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.

Virtual machine (VM) migration is a key technique for network resource optimization in modern data center networks. Previous work generally focuses on how to place the VMs efficiently in a static network topology by migrating the VMs with large traffic demands to close servers. As the flow demands between VMs change, however, a great cost will be paid for the VM migration. In this paper, we propose a new paradigm for VM migration by dynamically constructing adaptive topologies based on the VM demands to lower the cost of both VM migration and communication. We formulate the traffic-aware VM migration problem in an adaptive topology and show its NP-hardness. For periodic traffic, we develop a novel progressive-decompose-rounding algorithm to schedule VM migration in polynomial time with a proved approximation ratio. For highly dynamic flows, we design an online decision-maker (ODM) algorithm with proved performance bound. Extensive trace-based simulations show that PDR and ODM can achieve about four times flow throughput among VMs with less than a quarter of the migration cost compared to other state-of-art VM migration solutions. We finally implement an OpenvSwitch-based testbed and demonstrate the efficiency of our solutions.

Detecting anomalous traffic is a critical task for advanced Internet management. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix-based traffic data model, existing algorithms often suffer from low accuracy in anomaly detection. To fully utilize the multi-dimensional information hidden in the traffic data, this paper takes the initiative to investigate the potential and methodologies of performing tensor factorization for more accurate Internet anomaly detection. More specifically, we model the traffic data as a three-way tensor and formulate the anomaly detection problem as a robust tensor recovery problem with the constraints on the rank of the tensor and the cardinality of the anomaly set. These constraints, however, make the problem extremely hard to solve. Rather than resorting to the convex relaxation at the cost of low detection performance, we propose TensorDet to solve the problem directly and efficiently. To improve the anomaly detection accuracy and tensor factorization speed, TensorDet exploits the factorization structure with two novel techniques, sequential tensor truncation and two-phase anomaly detection. We have conducted extensive experiments using Internet traffic trace data Abilene and GÈANT. Compared with the state of art algorithms for tensor recovery and matrix-based anomaly detection, TensorDet can achieve significantly lower false positive rate and higher true positive rate. Particularly, benefiting from our well designed algorithm to reduce the computation cost of tensor factorization, the tensor factorization process in TensorDet is 5 (Abilene) and 13 (GÈANT) times faster than that of the traditional Tucker decomposition solution.

Vehicles equipped with various types of sensors can act as mobile sensors to monitor the road conditions. To speed up the information collection process, the monitoring data can be shared among vehicles upon their encounters to facilitate drivers to find a good route. The vehicular network experiences intermittent connectivity as a result of the mobility, which makes the inter-vehicle contact duration a scarce resource for data transmissions and the support of monitoring applications over vehicular networks a challenge. We propose a novel compressive sensing (CS)-based scheme to enable efficient decentralized context sharing in vehicular delay tolerant networks, called CS-Sharing. To greatly reduce the data transmission overhead and speed up the monitoring processing, CS-sharing exploits two techniques: sending an aggregate message in each vehicle encounter, and quick collection of information taking advantage of data sharing and the sparsity of events in vehicle networks to significantly reduce the number of measurements needed for global information recovery. We propose a novel data structure, and an aggregation method that can take advantage of the random and opportunistic vehicle encounters to form the measurement matrix. We prove that the measurement matrix satisfies the Restricted Isometry Property (RIP) property required by the CS technique. Our results from extensive simulations demonstrate that CS-Sharing allows vehicles in a large network to quickly obtain the full context data with the successful recovery ratio larger than 90%.

Transmission in millimeter wave (mmW) band has a big potential to provide orders of higher wireless bandwidth. To combat the high channel loss in high frequency band, beamforming is generally taken to transmit along the direction that provides the maximum transmission gain. This requires the MAC protocol to facilitate the finding of the optimal beamforming direction. Exiting protocol suggests the rotational channel measurement which may introduce high measurement cost, and compromise the transmission capacity. This paper presents a comprehensive design for more efficient directional beam alignment in mmW cellular networks. Instead of exhaustively searching all possible beamforming directions at the transmitter (TX) and the receiver (RX), our proposed scheme selects only a fairly small number of TX and RX beam pairs to facilitate effective beam alignment. To avoid long and resource-consuming exhaustive search, our scheme not only takes advantage of the low rank characteristics of the channel to estimate the full channel information with a small number of measurements, but also further exploits the channel estimation from initial measurements to guide the selection of future beam pairs for more effective measurements later. These strategies help to speed up the process of finding satisfactory beam pairs. We perform extensive simulations to evaluate the performance of our proposed schemes, and our results demonstrate our scheme can significantly outperform other schemes in terms of measurement effectiveness and cost efficiency.

Virtual machine (VM) migration is a key technique for network resource optimization in modern data center networks (DCNs). Previous work generally focuses on how to place the VMs efficiently in a static network topology by migrating the VMs with large traffic demands to close servers. When the VM demands change, however, a great cost will be paid on the VM migration. With the advance of software-defined network (SDN), recent studies have shown great potential to implement an adaptive network topology at a low cost. Taking advantage of the topology adaptability, in this paper, we propose a new paradigm for VM migration by dynamically constructing a topology based on the VM demands to lower the cost of both VM migration and communication. We formulate the traffic-aware VM migration problem in an adaptive topology and show its NP-hardness. Then we develop a novel progressive-decompose-rounding (PDR) algorithm to solve this problem in polynomial time with a proved approximation ratio. Extensive trace-based simulations show that PDR can achieve higher flow throughput among VMs with only a quarter of the migration cost compared to other state-of-art VM migration solutions. We finally implement an OpenvSwitch-based testbed and demonstrate the efficiency of our solution.

The inference of traffic volume of the whole network from partial traffic measurements becomes increasingly critical for various network engineering tasks, such as traffic prediction, network optimization, and anomaly detection. Previous studies indicate that the matrix completion is a possible solution for this problem. However, as a two-dimension matrix cannot sufficiently capture the spatial-temporal features of traffic data, these approaches fail to work when the data missing ratio is high. To fully exploit hidden spatial-temporal structures of the traffic data, this paper models the traffic data as a 3-way traffic tensor and formulates the traffic data recovery problem as a low-rank tensor completion problem. However, the high computation complexity incurred by the conventional tensor completion algorithms prevents its practical application for the traffic data recovery. To reduce the computation cost, we propose a novel Sequential Tensor Completion algorithm (STC) which can efficiently exploit the tensor decomposition result for the previous traffic data to deduce the tensor decomposition for the current data. To the best of our knowledge, we are the first to apply the tensor to model Internet traffic data to well exploit their hidden structures and propose a sequential tensor completion algorithm to significantly speed up the traffic data recovery process. We have done extensive simulations with the real traffic trace as the input. The simulation results demonstrate that our algorithm can achieve significantly better performance compared with the literature tensor and matrix completion algorithms even when the data missing ratio is high.

With the rapid shift from end-to-end communications to content-based data sharing, there are increasing interests in exploiting mobile social Delay-Tolerant Networks (social DTNs) to deliver data, where the forwarding decision is usually made by comparing the social metrics of encountered nodes. Existing studies mostly derive long-term statistical social metrics without considering the temporal impact from node mobility. We exploit the time-varying contact graphs to analyze the dynamics of social DTNs based on two groups of datasets. Based on the analysis, we derive the time-varying characteristics of node contacts, durative and periodicity, and apply them to more accurately predict the corresponding time-varying social metrics (TSMs). We further propose a two-stage opportunistic forwarding strategy to select relays based on TSMs. Our simulation results verify the importance of the two properties we observe and the effectiveness of our algorithm in tracking time-varying social metrics. We also show the potential of our algorithm in finding general time varying metrics to improve the data dissemination performance of other opportunistic forwarding schemes.

There is a big potential to enable more efficient data dissemination in mobile Delay-Tolerant Networks (DTNs) with the concurrent use of multi-copy forwarding and social metrics. However, this also leads to the possibility of severely overloading the relay nodes with high social metrics, and consequent performance degradation. We propose a fair source quota allocation algorithm to effectively alleviate the load while ensuring their dissemination fairness, i.e, Lexicographical order Max-Min Fairness(LMMF). In this paper, A fair source quota allocation algorithm along with an implementation scheme was presented to take advantage of the features of social networks and social forwarding for higher delivery performance. Extensive simulations based on trace data demonstrate that our mechanism greatly reduces the delivery-ratio degradation caused by uneven load while ensuring fairness among the network users.

With the rapid shift from end-to-end communications to content-based data retrieval, there are increasing interests in exploiting Content-centric Networks (CCN) to deliver data. As the special characteristics of CCN, in-network caching and naming-based routing make traditional TCP-like transmission control protocol unsuitable. Although there are some existing efforts on improving the congestion control in CCN, the big issue of redundant transmissions caused by multiple sources has received little attention. To eliminate the redundancy and speed up the transmission, we propose a complete Network Codes-based Multi-Source Transmission Control Protocol (MSTCP), which provides an efficient and controllable multi-source content retrieval service over CCN. MSTCP takes advantage of random network coding to make full use of the coded data responded by different sources to speed up decoding and data receiving at the request side. Moreover, we design a scheduling algorithm based on a simple Expected Reception Deadline (ERD) to efficiently control the number of coded packets to send at each source. This not only effectively eliminates the redundant transmissions in CCN, but also helps to significantly speed up the information retrieval. Extensive simulations show that our mechanism greatly reduces the redundancy while speeding up the content retrievals by the network users.

The popularity of smart phones fosters the growth of Proximity-based Mobile Social Networking (PMSN). Although some profile matching approaches have been proposed to facilitate a user to find another user that shares his/her interest in the proximity, these approaches usually model the matching problem as a Private Set Intersection problem or a Private Set Intersection Cardinality problem and require high complexity of computation. Different from current studies, to facilitate more effective building of PMSNs, we propose a novel similarity metric to evaluate the common interests of mobile users by considering the time-dependent features of their interests. To calculate the metric in a low cost and privacy- protection way, we propose a novel time-dependent bloom filter to encode the time-dependent interest and a novel probabilistic algorithm to estimate the time- dependent similarity metric based on the bloom filter. Based on the proposed BF-based profile matching approach, we further propose InterestMatch, a novel distributed mobile communication system to facilitate more efficient social networking among strangers in the physical proximity. We have done extensive experiments on real-world phones, our experiment results demonstrate that our approach is promising for facilitating mobile social interactions in the physical proximity due to its low complexity and consequently low power consumption.

There are a lot of recent interests on cooperative communication (CC) in wireless networks. Despite the large capacity gain of CC in small wireless networks with its capability of mitigating fading taking advantage of spatial diversity, cooperative communication can result in severe interference in large networks and even degraded throughput. The aim of this work is to concurrently exploit multi-radio and multi-channel (MRMC) technique and cooperative transmission technique to combat co-channel interference and improve the performance of multi-hop wireless network. Our proposed solution concurrently considers cooperative routing, channel assignment, and relay selection and takes advantage of both MRMC technique and spatial diversity in cooperative wireless networks to improve the throughput. We propose two important metrics, contention-aware channel utilization routing metric (CACU) to capture the interference cost from both direct transmission and cooperative transmission, and traffic aware channel condition metric (TACC) to evaluate the channel load condition. Based on these metrics, we propose three algorithms for interference-aware cooperative routing, local channel adjustment, and local path and relay adaptation respectively to ensure high performance communications in dynamic wireless networks. Our algorithms are designed to be fully distributed and can effectively mitigate co-channel interference and achieve cooperative diversity gain. To our best knowledge, this is the first distributed solution that supports cooperative communications in MRMC networks. Our performance studies demonstrate that our proposed algorithms can efficiently support cooperative communications in multi-radio multi-hop networks to significantly increase the aggregate throughput.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as target tracking. In this paper, we study the scenario where sensors take measurements of one or more dynamic targets and send state estimates of the targets to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels reduce the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. In addition, the errors in target-sensor data association can also degrade the estimation performance. To mitigate the effect of imperfect communications on state estimation and fusion, we consider retransmission and retrodiction. The system adopts certain retransmission-based transport protocols so that lost messages can be recovered over time. Moreover, retrodiction/smoothing techniques are applied so that the chances of incurring excess delay due to retransmission are greatly reduced. We analyze the extent to which retransmission and retrodiction can improve the performance of delay-sensitive target tracking tasks under variable communication loss and delay conditions. Simulation results of a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.

Cooperative communication (CC) for wireless networks has gained a lot of recent interests. It has been shown that CC has the potential to significantly increase the capacity of wireless networks, with its ability of mitigating fading by exploiting spatial diversity. However, most of the works on CC are limited to single radio wireless network. To demonstrate the benefits of CC in multiradio multihop wireless network, this paper studies a joint problem of multiradio cooperative routing and relay assignment to maximize the minimum rate among a set of concurrent communication sessions. We first model this problem as a mixed-integer programming (MIP) problem and prove it to be NP-hard. Then, we propose a centralized algorithm and a distributed algorithm to solve the problem. The centralized algorithm is designed within a branch-and-bound framework by using the relaxation of the formulated MIP, which can find a global (1+ε)-optimal solution. Our distributed algorithm includes two subalgorithms: a cooperative route selection subalgorithm and a fairness-aware route adjustment subalgorithm. Our simulation results demonstrate the effectiveness of the proposed algorithms and the significant rate gains that can be achieved by incorporating CC in multiradio multihop networks.

The lack of pre-existing infrastructure or dynamic topology makes it impossible to establish end-to-end connections in opportunistic networks (OppNets). Instead, a store-and-forward strategy can be employed. However, such loosely knit routing paths depend heavily on the cooperation among participating nodes. Selfish or malicious behaviors of nodes impact greatly on the network performance. In this paper, we design and validate a dynamic trust management model for secure routing optimization. We propose the concept of incorporating social trust into the routing decision process and design a trust routing based on social similarity (TRSS) scheme. TRSS is based on the observation that nodes move around and contact each other according to their common interests or social similarities. A node sharing more social features in social history record with the destination is more likely to travel close to the latter in the near future and should be chosen as the next-hop forwarder. Furthermore, social trust can be established based on an observed node's trustworthiness and its encounter history. Based on direct and recommended trust, those untrustworthy nodes will be detected and purged from the trusted list. Since only trusted nodes' packets will be forwarded, the selfish nodes have the incentives to behave well again. Simulation evaluation demonstrates that TRSS is very effective in detecting selfish or even malicious nodes and achieving better performance.

End-to-end network monitoring is essential to ensure transmission quality for Internet applications. However, in large-scale networks, full-mesh measurement of network performance between all transmission pairs is infeasible. As a newly emerging sparsity representation technique, matrix completion allows the recovery of a low-rank matrix using only a small number of random samples. Existing schemes often fix the number of samples assuming the rank of the matrix is known, while the data features thus the matrix rank vary over time. In this paper, we propose to exploit the matrix completion techniques to derive the end-to-end network performance among all node pairs by only measuring a small subset of end-to-end paths. To address the challenge of rank change in the practical system, we propose a sequential and information-based adaptive sampling scheme, along with a novel sampling stopping condition. Our scheme is based only on the data observed without relying on the reconstruction method or the knowledge on the sparsity of unknown data. We have performed extensive simulations based on real-world trace data, and the results demonstrate that our scheme can significantly reduce the measurement cost while ensuring high accuracy in obtaining the whole network performance data.

Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow 46% less number of samples for accurate signal reconstruction and achieve up to 57% smaller signal reconstruction error under the same noise condition.

Multi-robot systems (MRS) have many applications and the efficient operation of MRS relies on coordination of robots. However, it is difficult to build network connections among randomly distributed robots in the presence of robot movements and weak wireless channels. In this work, we propose to jointly exploit communications and motion control to efficiently establish robot connections. To achieve this goal, we concurrently use MUSIC and particle filter to more accurately and efficiently estimate robot signal directions, built on which signal strength-based potential field is formed to control robot motion to establish and maintain communication links. Our studies based on test bed and simulations demonstrate the effectiveness of our algorithm in networking robots, with much higher number of robots connected compared to peer algorithms.

Mobile cloud storage services have gained phenomenal success in recent few years. In this paper, we identify, analyze and address the synchronization (sync) inefficiency problem of modern mobile cloud storage services. Our measurement results demonstrate that existing commercial sync services fail to make full use of available bandwidth, and generate a large amount of unnecessary sync traffic in certain circumstance even though the incremental sync is implemented. These issues are caused by the inherent limitations of the sync protocol and the distributed architecture. Based on our findings, we propose QuickSync, a system with three novel techniques to improve the sync efficiency for mobile cloud storage services, and build the system on two commercial sync services. Our experimental results using representative workloads show that QuickSync is able to reduce up to 52.9% sync time in our experiment settings.

Cognitive radio (CR) techniques promise to significantly increase the available spectrum thus wireless bandwidth. With the increase of spectrum allowed for CR, it is critical and challenging to perform efficient wideband sensing. We propose an integrated sequential wideband sensing framework which concurrently exploits sequential detection and compressed sensing (CS) techniques for more accurate and lower cost spectrum sensing. First, to ensure more timely spectrum detection while avoiding the high overhead involved in periodic recovery of CS signals, we design a CS-based sequential wideband detection scheme to effectively detect the PU activities in the wideband of interest. Second, to further identify the sub-channels occupied, we exploit joint sparsity of the signals among neighboring users to achieve efficient cooperative wideband sensing. Our performance evaluations demonstrate that our proposed scheme can outperform other peer schemes significantly in terms of the detection delay, detection accuracy, sensing overhead and sensing accuracy.

Ideally, the throughput of a Multipath TCP (MPTCP) connection should be as high as that of multiple disjoint single-path TCP flows. In reality, the throughput of MPTCP is far lower than expected. In this paper, we conduct an extensive simulation-based study on this phenomenon, and the results indicate that a subflow experiencing high delay and loss severely affects the performance of other subflows, thus becoming the bottleneck of the MPTCP connection and significantly degrading the aggregate goodput. To tackle this problem, we propose Fountain code-based Multipath TCP (FMTCP), which effectively mitigates the negative impact of the heterogeneity of different paths. FMTCP takes advantage of the random nature of the fountain code to flexibly transmit encoded symbols from the same or different data blocks over different subflows. Moreover, we design a data allocation algorithm based on the expected packet arriving time and decoding demand to coordinate the transmissions of different subflows. Quantitative analyses are provided to show the benefit of FMTCP. We also evaluate the performance of FMTCP through ns-2 simulations and demonstrate that FMTCP outperforms IETF-MPTCP, a typical MPTCP approach, when the paths have diverse loss and delay in terms of higher total goodput, lower delay, and jitter. In addition, FMTCP achieves high stability under abrupt changes of path quality.

In long-haul sensor networks, remote sensors are deployed to cover a large geographical area, such as a continent or the entire globe. Related applications can be found in military surveillance, air traffic control, greenhouse gas emission monitoring, and global cyber attack detection, among others. In this paper, we consider target monitoring and tracking using a long-haul sensor network, wherein the state and covariance estimates are sent from the sensors to a fusion center that generates a fused state estimate. Long-haul communications over submarine fibers and satellite links are subject to long latencies and/or high loss rates, which lead to lost or out-of-order messages. These in turn may significantly degrade the fusion performance: Fusing fewer state estimates may compromise the accuracy of the fused state, whereas waiting for all estimates to arrive may compromise its timeliness. We propose an online selective linear fusion method to fuse the state estimates based on projected information contribution from the pending data. Using both prediction and retrodiction techniques, our scheme enables the fusion center to opportunistically make decisions on when to fuse the estimates, thereby achieving a balance between accuracy and timeliness of the fused state. Simulation results of a target tracking application show that our scheme yields accurate and timely fused estimates under variable communications delay and loss conditions.

Multicast is a crucial routine operation for vehicular networks, which underpins important functions such as message dissemination and group coordination. As vehicles may distribute over a vast area, the number of vehicles in a given region can be limited which results in sparse node distribution in part of the vehicular network. This poses several great challenges for efficient multicast, such as network disconnection, scarce communication opportunities and mobility uncertainty. Existing multicast schemes proposed for vehicular networks typically maintain a forwarding structure assuming the vehicles have a high density and move at low speed while these assumptions are often invalid in a practical vehicular network. As more and more vehicles are equipped with GPS enabled navigation systems, the trajectories of vehicles are becoming increasingly available. In this work, we propose an approach called TMC to exploit vehicle trajectories for efficient multicast in vehicular networks. The novelty of TMC includes a message forwarding metric that characterizes the capability of a vehicle to forward a given message to destination nodes, and a method of predicting the chance of inter-vehicle encounter between two vehicles based only on their trajectories without accurate timing information. TMC is designed to be a distributed approach. Vehicles make message forwarding decisions based on vehicle trajectories shared through inter-vehicle exchanges without the need of central information management. We have performed extensive simulations based on real vehicular GPS traces and compared our proposed TMC scheme with other existing approaches. The performance results demonstrate that our approach can achieve a delivery ratio close to that of the flooding-based approach while the cost is reduced by over 80 percent.

Matrix completion has emerged very recently and provides a new venue for low cost data gathering in WSNs. Existing schemes often assume that the data matrix has a known and fixed low-rank, which is unlikely to hold in a practical monitoring system such as weather data gathering. Weather data varies in temporal and spatial domain with time. By analyzing a large set of weather data collected from 196 sensors in ZhuZhou, China, we reveal that weather data have the features of low-rank, temporal stability, and relative rank stability. Taking advantage of these features, we propose an on-line data gathering scheme based on matrix completion theory, named MC-Weather, to adaptively sample different locations according to environmental and weather conditions. To better schedule sampling process while satisfying the required reconstruction accuracy, we propose several novel techniques, including three sample learning principles, an adaptive sampling algorithm based on matrix completion, and a uniform time slot and cross sample model. With these techniques, our MC-Weather scheme can collect the sensory data at required accuracy while largely reduce the cost for sensing, communication and computation. We perform extensive simulations based on the real weather data sets and the simulation results validate the efficiency and efficacy of the proposed scheme.

In cellular networks, it is important to conserve energy while at the same time ensuring users to have good transmission experiences. The energy cost can result from tail energy due to the radio resource control strategies designed in cellular networks and data transmission. Existing efforts generally consider one of the energy issues, and also ignore the adverse impact on user transmission performance due to energy conservation. In addition, many existing algorithms are based on prediction and knowledge on future traffic, which are hard to apply in a practical wireless system with dynamic user traffic and channel condition. The goal of this work is to design an efficient online scheduling algorithm to minimize energy consumption both due to tail energy and transmissions while meeting user performance expectation. We prove the problem to be NP-hard, and design a practical online scheduling algorithm PerES to minimize the total energy cost of multiple mobile applications subject to user performance constraints. We propose a comprehensive performance cost metric to capture the impacts due to task delay, deadline violation, different application profiles and user preferences. We prove that our proposed scheduling algorithm can make the energy consumption arbitrarily close to that of the optimal scheduling solution. The evaluation results demonstrate the effectiveness of our scheme and its higher performance than peers. Moreover, by supporting dynamic performance requirement by mobile users, PerES can achieve 2 times faster convergence to both the performance degradation bound and optimal energy conversation bound than those of traditional static methods. Using 821 million traffic flows collected from a commercial cellular carrier, we verify our scheme could achieve on average 32%-56% energy savings with different levels of user experience.

In recent years, cooperative communication has attracted researchers' attention as it showed a good capability to increase network performance. On the other hand, a cooperative transmission may cause more interference. This can cause difficulty in achieving cooperative diversity gain in multi-flow and multi-hop networks. In this paper, we propose a novel interference aware-cooperative-routing metric which will lead to creating a new scheme for cooperative routing and channel assignment in multi-flow and multi-hop networks. Then, we show through preliminary simulation results, by investigating the impact of the node density and impact of the flow number, that the proposed scheme minimizes interference while achieving maximum cooperative diversity gain.

Efficient power line monitoring is essential for reliable operation of the Smart Grid. A Power Line Monitoring Network (PLMN) based on wireless sensor nodes can provide the necessary infrastructure to deliver data from the extension of the power grid to one or several control centers. However, the restricted physical topology of the power lines constrains the data paths, and has a great impact on the reporting performance. We discuss the features of power-lines and their impact on the performance of monitoring and transmissions. We present a comprehensive design to guide efficient and flexible relay selection in PLMNs to ensure reliable and energy efficient transmissions while taking into account the restricted topology of power-lines. Specifically, our design applies probabilistic power control along with flexible transmission scheduling to combat the poor channel conditions around power line while maintaining the energy level of transmission nodes. We evaluate the impact of different channel conditions, non-uniform topologies for a power line corridor and the effect of reporting events. Our performance results demonstrate that our data forwarding scheme can well control the energy consumption and delay while ensuring reliability and extended lifetime.

Compressed sensing (CS) technique has attracted a lot of recent research interests in mathematics and signal processing fields. Literature studies often exploit CS at the receiver side to sub-sample the receiving signals to reduce the sampling rate and processing overhead. It would be of great benefit if it is possible to exploit CS at the transmitter side to reduce the redundancy of the data before transmission to conserve precious wireless bandwidth. Different from receiver-side sub-sampling, the sub-sampled transmitting data may be perturbed by the dynamics of wireless channels and experience higher overall noise. In this paper, we propose a set of mechanisms to enable compressive wireless data transmissions. Specifically, we investigate the impacts of imperfect channel equalization on the data reconstruction, and propose a comprehensive signal recovery algorithm to cope with the perturbations introduced by wireless channels. Simulation results demonstrate that our proposed schemes can effectively reduce the effects of dynamic wireless channels on the data reconstruction and maintain the performance comparable to that of traditional communication scheme which does not apply CS to compress data. This indicates that it is promising to exploit CS to reduce the communication data thus bandwidth requirement. Transmission data reduction can complement existing efforts of improving wireless channel capacity to support the quick growth of wireless applications.

Spectrum sensing is a crucial element of dynamic spectrum access (DSA) as it enables cognitive radios (CRs) to opportunistically access the under-utilized spectrum. Existing efforts on sensing have not adequately addressed sensing scheduling over time for better detection performance. In this work, we consider sequential periodic sensing of an in-band channel. We focus primarily on finding the appropriate sensing frequency during an SU's active data transmission on a licensed channel. Detection schemes addressing channel state change and anomalous data are designed specifically to facilitate short-term sensing adaptation to the variations in sensed data. In addition, long-term adaptation is also considered so that the evolving sensing environment can be reflected in the sensing schedule as well. Simulation results demonstrate that our design guarantees better conformity to the spectrum access policies by significantly reducing the delay in change detection while ensuring better sensing accuracy.

OS-level virtualization generates a minimal start-up and run-time overhead on the host OS and thus suits applications that require both good isolation and high efficiency. However, multiple-member applications required for forming a system may need to occasionally communicate across this isolation barrier to cooperate with each other while they are separated in different VMs to isolate intrusion or fault. Such application scenarios are often critical to enterprise-class servers, HPC clusters and intrusion/fault-tolerant systems, etc. We make the first effort to support the inter-application interactions in an OS-level virtualization system without causing a significant compromise on VM isolation. We identify all interactive operations that impact inter-application interactions, including inter-process communications, application invocations, resource name transfers, and application dependencies. We propose Shuttle, a novel approach for facilitating inter-application interactions within and across OS-level virtual machines. Our results demonstrate that Shuttle can correctly address all necessary inter-application interactions while providing good isolation capability for all sample applications on different versions of Windows OS.

Behavior-based detection is promising to resolve the pressing security problem of malware. However, the great challenge lies in how to detect malware in a both accurate and light-weight manner. In this paper, we propose a novel behavior-based detection method, named growing grapes, aiming to enable accurate online detection. It consists of a clustering engine and detection engine. The clustering engine groups the objects, e.g., processes and files, of a suspicious program together into a cluster, just like growing grapes. The detection engine recognizes the cluster as malicious if the behaviors of the cluster match a predefined behavior template formed by a set of discrete behaviors. The approach is accurate since it identifies a malware based on multiple behaviors and the source of the processes requesting the behaviors. The approach is also light-weight as it uses OS-level information flows instead of data flows that generally impose significant performance impact on the system. To further improve the performance, a novel method of organizing the behavior template and template database is proposed, which not only makes the template matching process very quick, but also makes the storage space small and fixed. Furthermore, the detection accuracy and performance are optimized to the best degree using a combinatorial optimization algorithm, which properly selects and combines multiple behaviors to form a template for malware detection. Finally, the approach novelly identifies malicious OS objects in a cluster fashion rather than one by one as done in traditional methods, which help users to thoroughly eliminate the changes of a malware without malware family knowledge. Compared with commercial antimalware tools, extensive experiments show that our approach can detect new malware samples with higher detection rate and lower false positive rate while imposing low overhead on the system.

The demands for data rate and transmission reliability constantly increase with the explosive use of wireless devices and the advancement of mobile computing techniques. Multiple-input and multiple-output (MIMO) technique is considered as one of the most promising wireless technologies that can significantly improve transmission capacity and reliability. Many emerging mobile wireless applications require peer-to-peer transmissions over an ad hoc network, where the nodes often have a different number of antennas, and the channel condition and network topology vary over time. It is important and challenging to develop efficient schemes to coordinate transmission resource sharing among a heterogeneous group of nodes over an infrastructure-free mobile ad hoc network. In this work, we propose a holistic scheduling algorithm that can adaptively select different transmission strategies based on the node types and channel conditions to effectively relieve the bottleneck effect caused by nodes with smaller antenna arrays, and avoid the transmission failure due to the violation of lower degree of freedom constraint resulted from the channel dependency. The algorithm also takes advantage of channel information to opportunistically schedule cooperative spatial multiplexed transmissions between nodes and provide special transmission support for higher priority nodes with weak channels, so that the data rate of the network can be maximized while user transmission quality requirement is supported. The performance of our algorithm is studied through extensive simulations and the results demonstrate that our algorithm is very effective in handling node heterogeneity and channel constraint, and can significantly increase the throughput while reducing the transmission delay.

Two fast growing technologies, MIMO and cognitive radio (CR), can both effectively combat the transmission interference among links and thus increase the network throughput. MIMO exploits spatial degree of freedom (DoF) through spatial multiplexing and interference cancellation within the same frequency channel, while CR exploits all available frequency channels for transmissions. We consider an ad hoc network where each node is equipped with an array of cognitive radios. A radio can tune to a different channel and transmit independently, or transmit together with other radios on the same channel using MIMO mode. Additionally, different frequency and spatial channels could have different conditions. There is a big challenge for nodes to distributively coordinate in selecting a transmission channel and/or a spatial DoF taking advantage of this unprecedented flexibility and diversity of channels for a higher network performance. In this work, we mathematically model the opportunities and constraints for such a network with the objective of maximizing the weighted network throughput. We propose a centralized algorithm as our comparison benchmark, and a distributed algorithm to flexibly assign spectrum channel or spatial DoF exploiting the multiuser diversity, channel diversity and spatial diversity for a higher performance in a practical network. The algorithm further supports different transmission priorities, reduces transmission delay and ensures fair transmissions among nodes by providing all nodes with certain transmission probability. The performance of our algorithms are studied through extensive simulations and the results demonstrate that our algorithm is very effective and can significantly increase the network throughput while reducing the delay.

Spectrum sensing enables cognitive radios (CRs) to opportunistically access the under-utilized spectrum. Existing efforts on sensing have not adequately addressed sensing scheduling over time for better detection performance. In this work, we consider sequential periodic sensing of an in-band channel. We focus primarily on finding the appropriate sensing frequency during an SU's active data transmission on a licensed channel. Change and outlier detection schemes are designed specifically to facilitate short-term sensing adaptation to the variations in sensed data. Simulation results demonstrate that our design guarantees better conformity to the spectrum access policies by significantly reducing the delay in change detection while ensuring better sensing accuracy.

With the popularity of wireless devices and the increase of computing and storage resources, there are increasing interests in supporting mobile computing techniques. Particularly, ad hoc networks can potentially connect different wireless devices to enable more powerful wireless applications and mobile computing capabilities. To meet the ever increasing communication need, it is important to improve the network throughput while guaranteeing transmission reliability. Multiple-input-multiple-output (MIMO) technology can provide significantly higher data rate in ad hoc networks where nodes are equipped with multiantenna arrays. Although MIMO technique itself can support diversity transmission when channel condition degrades, the use of diversity transmission often compromises the multiplexing gain and is also not enough to deal with extremely weak channel. Instead, in this work, we exploit the use of cooperative relay transmission (which is often used in a single antenna environment to improve reliability) in a MIMO-based ad hoc network to cope with harsh channel condition. We design both centralized and distributed scheduling algorithms to support adaptive use of cooperative relay transmission when the direct transmission cannot be successfully performed. Our algorithm effectively exploits the cooperative multiplexing gain and cooperative diversity gain to achieve higher data rate and higher reliability under various channel conditions. Our scheduling scheme can efficiently invoke relay transmission without introducing significant signaling overhead as conventional relay schemes, and seamlessly integrate relay transmission with multiplexed MIMO transmission. We also design a MAC protocol to implement the distributed algorithm. Our performance results demonstrate that the use of cooperative relay in a MIMO framework could bring in a significant throughput improvement in all the scenarios studied, with the variation of node density, link failure ratio, packet arrival rate, and retransmission threshold.

A virtual machine(VM) can be simply created upon use and disposed upon the completion of the tasks or the detection of error. The disadvantage of this approach is that if there is no malicious activity, the user has to redo all of the work in her actual workspace since there is no easy way to commit (i.e., merge) only the benign updates within the VM back to the host environment. In this work, we develop a VM commitment system called Secom to automatically eliminate malicious state changes when merging the contents of an OS-level VM to the host. Secom consists of three steps: grouping state changes into clusters, distinguishing between benign and malicious clusters, and committing benign clusters. Secom has three novel features. First, instead of relying on a huge volume of log data, it leverages OS-level information flow and malware behavior information to recognize malicious changes. As a result, the approach imposes a smaller performance overhead. Second, different from existing intrusion detection and recovery systems that detect compromised OS objects one by one, Secom classifies objects into clusters and then identifies malicious objects on a cluster by cluster basis. Third, to reduce the false-positive rate when identifying malicious clusters, it simultaneously considers two malware behaviors that are of different types and the origin of the processes that exhibit these behaviors, rather than considers a single behavior alone as done by existing malware detection methods. We have successfully implemented Secom on the feather-weight virtual machine system, a Windows-based OS-level virtualization system. Experiments show that the prototype can effectively eliminate malicious state changes while committing a VM with small performance degradation. Moreover, compared with the commercial antimalware tools, the Secom prototype has a smaller number of false negatives and thus can more thoroughly clean up malware side effects. In addition, the number of false positives of the Secom prototype is also lower than that achieved by the online behavior-based approach of the commercial tools.

In recent years, we have witnessed a surge of interest in enabling communications over meshed wireless networks. Particularly, supporting peer-to-peer communications over a multihop wireless network has great potential in enabling ubiquitous computing. However, many wireless nodes have limited capabilities, for example, sensor nodes or small handheld devices. Also, the end-to-end capacity and delay degrade significantly as the path length increases with the number of network nodes. In these scenarios, the deployment of a backbone network could potentially facilitate higher performance network communications. In this paper, we study the novel reinforced backbone network (RBN) deployment problem considering the practical limitation in the number of available backbone nodes and enforcing backbone network connectivity. We propose an iterative and adaptive (ITA) algorithm for efficient backbone network deployment. In addition, in order to provide the performance bound, we redefine and solve the problem by implementing the genetic algorithm. Finally, we present our simulation results under various settings and compare the performance of the proposed ITA algorithm and the genetic algorithm. Our study indicates that the proposed ITA algorithm is promising for deploying a connected RBN with a limited number of available backbone nodes.

Multiple-Input-Multiple-Output (MIMO) has great potential for enhancing the throughput of multihop wireless networks via spatial multiplexing or spatial reuse. Spatial reuse with Stream Control (SC) provides a considerable improvement of the network throughput over spatial multiplexing. The gain of spatial reuse, however, is still not fully exploited. There exist large numbers of additional data streams, which could be transmitted concurrently with those data streams scheduled by stream control at certain time slots and vicinities. In this paper, we address the issue of MIMO link scheduling to maximize the gain of spatial reuse and thus network throughput. We propose a Receiver-Oriented Interference Suppression model (ROIS), based on which we design both centralized and distributed link scheduling algorithms to fully exploit the gain of spatial reuse in multihop MIMO networks. Further, we address the traffic-aware link scheduling problem by injecting nonuniform traffic load into the network. Through theoretical analysis and comprehensive performance evaluation, we achieve the following results: 1) link scheduling based on ROIS achieves significant higher network throughput than that based on stream control, with any interference range, number of antennas, and average hop length of data flows. 2) The traffic-aware scheduling is enticingly complementary to the link scheduling based on ROIS model. Accordingly, the two scheduling schemes can be combined to further enhance the network throughput.

Cooperative communication for wireless networks has gained a lot of recent interests due to its ability to mitigate fading with exploration of spatial diversity. The objective of this paper is to design an efficient algorithm to minimize the total consumed power of the network while guaranteeing transmission reliability of multiple active transmission pairs through cooperative wireless communications. This problem has not been studied and is much more challenging than relay assignment considered in literature work which simply targets to reduce the transmission power for a single transmission pair. We achieve the objective by jointly considering transmission mode selection, relay assignment and power allocation. This requires us to solve a combinatorial optimization problem, namely Reliable and Energy Efficient Cooperative Communication problem (REECC), which is a hard problem as its complexity increases exponentially with the number of relay nodes. We propose an iterative solution framework by testing different power levels to find the optimal solution. To reduce the computational cost, we design several novel techniques in the solution framework. The simulation results demonstrate that our solution can run very efficiently to obtain the minimum total consumed power while satisfying the reliable transmission requirement.

The built-in caching capability of future Named Data Networking (NDN) promises to enable effective content distribution at a global scale without requiring special infrastructure. The aim of this work is to design efficient caching schemes in NDN to achieve better performance at both the network layer and application layer. With the specific objective of minimizing the inter-ISP (Internet Service Provider) traffic and average access latency, we first formulate the optimization problems for different objectives and then solve them to obtain the optimal replica placement. Then we develop popularity-driven caching schemes which dynamically place the replicas in the caches on the en-route path in a coordination fashion. Simulation results show that the performances of our caching algorithms are much closer to the optimum and outperform the widely used schemes in terms of the inter-ISP traffic and the average number of access hops. Finally, we thoroughly evaluate the impact of several important design issues such as network topology, cache size, access pattern and content popularity on the caching performance and demonstrate that the proposed schemes are effective, stable, scalable and with reasonably light overhead.

In this paper, we propose the application of low-rank matrix completion techniques for array signal processing. Specifically, under the assumption that the number of targets is generally much smaller than the number of antennas, the received signals can form a low-rank matrix with noise. According to the recently proposed matrix completion theory, only a subset of the entries are enough to recover the whole matrix as long as certain conditions are met, thus the implementation cost of obtaining a matrix could be reduced. We prove that the matrix formed by the received signals satisfies the condition for matrix recovery. Moreover, a uniform spatial sampling (USS) method is proposed, which is easy for hardware implementation and also could take advantage of the available number of front-end elements to achieve a better performance. We analytically prove that the probability of matrix recovery failure under the USS model is asymptotically equal to that under the Bernoulli model. Simulation results demonstrate that the matrix recovery performance under the USS model is very close to that using the uniform model.

In multi-hop wireless networks where links are prone to be broken or degraded, it is important to guarantee the network connectivity as well as satisfy the performance requirements. Observing the promising features of Multiple-Input Multiple-Output (MIMO) techniques for improving the transmission capacity and reliability, in this paper, we make the very first attempt to deploy MIMO nodes as relays to assist weak links in wireless networks, with the aim of reducing the number of relay nodes and providing performance provisioning. We identify the specific constraints of MIMO relay nodes for assisting weak links, and take advantage of the MIMO ability to flexibly select among different transmission strategies. The constrains and flexibility, however, make the MIMO deployment problem different from conventional single-antenna deployment schemes and much more challenging. Based on the constraints, we formulate the MIMO relay deployment problem, and provide a polynomial-time approximation scheme (PTAS) algorithm, as well as a distributed heuristic algorithm. The performance of the proposed algorithms is evaluated through simulations and demonstrated to be very effective.

Ideally, the throughput of a Multipath TCP (MPTCP) connection should be as high as that of multiple disjoint single-path TCP flows. In reality, the throughput of MPTCP is far lower than expected. This is fundamentally caused by the fact that a sub flow with high delay and loss affects the performance of other sub flows, and thus becomes the bottleneck of the MPTCP connection and significantly degrades the aggregate good put. To tackle this problem, we propose Fountain code-based Multipath TCP (FMTCP), which effectively mitigates the negative impact of the heterogeneity of different paths. FMTCP takes advantage of the random nature of the fountain code to flexibly transmit encoded symbols from the same or different data blocks over different sub flows. Moreover, we design a data allocation algorithm based on the expected packet arriving time and decoding demand to coordinate the transmissions of different sub flows. Quantitative analyses are provided to show the benefit of FMTCP. We also evaluate the performance of FMTCP through ns-2 simulations and demonstrate that FMTCP can outperform IETF-MPTCP, a typical MPTCP approach, when the paths have diverse loss and delay in terms of higher total good put, lower delay and jitter. In addition, FMTCP achieves much more stable performance under abrupt changes of path quality.

Long-haul sensor networks are deployed in a wide range of applications from national security to environmental monitoring. We consider target tracking over a long-haul sensor network, wherein state and covariance estimates are sent from sensors to a fusion center that generates a fused state. Fusion serves as a viable means to improve the estimation performance to meet the system requirement on accuracy and delay. Communications over the long-haul links, such as submarine fibers and satellite links, is subject to long latencies and high loss rates that lead to many lost or out-of-order messages and may significantly degrade the fusion performance. We propose an online selective fuser to combine the received state estimates based on estimated information contribution from the pending data. By concurrently using prediction and retrodiction, the fuser opportunistically makes timely decisions to achieve a balance between accuracy and timeliness of the fused estimate. Simulation results show that our method effectively maintains high levels of fusion performance under various communication delay and loss conditions.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks. We consider a class of such networks where sensors take measurements of one or more dynamic targets and send state estimates of the target(s) to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels render insufficient the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. The system can adopt certain retransmission-based transport protocols so that lost messages can be recovered over time. However, excess delay may be incurred that can potentially violate the deadline for reporting the estimate. For many applications, though, retrodiction/smoothing techniques can be applied so that the chances of incurring such excess delay are greatly reduced. In this work, we analyze the extent to which retrodiction, along with message retransmission, can improve the performance of delay-sensitive state estimation tasks. Results of numerical and simulation studies of an illustrative example and a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.

OS-level virtualization generates a minimal start-up and run-time overhead on the host OS and thus suits applications that require both good isolation and high efficiency. However, multiple-member applications required for forming a system may need to occasionally communicate across this isolation barrier to cooperate with each other while they are separated in different VMs to isolate intrusion or fault. Such application scenarios are often critical to enterprise-class servers, HPC clusters and intrusion/fault-tolerant systems, etc. We make the first effort to support the inter-application interactions in an OS-level virtualization system without causing a significant compromise on VM isolation. We identify all interactive operations that impact inter-application interactions, including inter-process communications, application invocations, resource name transfers and application dependencies. We propose Shuttle, a novel approach for facilitating inter-application interactions within and across OS-level virtual machines. Our results demonstrate that Shuttle can correctly address all necessary inter-application interactions while providing good isolation capability to all sample applications on different versions of Windows OS.

With the popularity of wireless devices and the increasing demand of network applications, it is emergent to develop more effective communications paradigm to enable new and powerful pervasive applications, and to allow services to be accessed anywhere, at anytime. However, it is extremely challenging to construct efficient and reliable networks to connect wireless devices due to the increasing communications need and the dynamic nature of wireless communications. In order to improve transmission throughput, many efforts have been made in recent years to reduce traffic and hence transmission collisions by constructing backbone networks with the minimum size. However, many other important issues need to be considered. Instead of simply minimizing the number of backbone nodes or supporting some isolated network features, in this work, we exploit the use of algebraic connectivity to control backbone network topology design for concurrent improvement of backbone network robustness, capacity, stability and routing efficiency. In order to capture other network features, we provide a general cost function and introduce a new metric, connectivity efficiency, to trade off algebraic connectivity and cost for backbone construction. We formally prove the problem of formulating a backbone network with the maximum connectivity efficiency that is NP-hard, and design both centralized and distributed algorithms to build more robust and efficient backbone infrastructure to better support the application needs. We have made extensive simulations to evaluate the performance of our work. Compared to literature studies on constructing wireless backbone networks, the incorporation of algebraic connectivity into the network performance metric could achieve much higher throughput and delivery ratio, and much lower end-to-end delay and routing distances under all test scenarios. We hope our work could stimulate more future research in designing more reliable and efficient networks. Our performance studies demonstrate that, compared to peer work, the incorporation of algebraic connectivity into network performance metric could achieve much higher throughput and delivery ratio, and much lower end-to-end delay and routing distances under all test scenarios. We hope our work could stimulate more future research in designing more reliable and efficient networks.

Enforcing a practical Mandatory Access Control (MAC) in a commercial operating system to tackle malware problem is a grand challenge but also a promising approach. The firmest barriers to apply MAC to defeat malware programs are the incompatible and unusable problems in existing MAC systems. To address these issues, we manually analyze 2,600 malware samples one by one and two types of MAC enforced operating systems, and then design a novel MAC enforcement approach, named Tracer, which incorporates intrusion detection and tracing in a commercial operating system. The approach conceptually consists of three actions: detecting, tracing, and restricting suspected intruders. One novelty is that it leverages light-weight intrusion detection and tracing techniques to automate security label configuration that is widely acknowledged as a tough issue when applying a MAC system in practice. The other is that, rather than restricting information flow as a traditional MAC does, it traces intruders and restricts only their critical malware behaviors, where intruders represent processes and executables that are potential agents of a remote attacker. Our prototyping and experiments on Windows show that Tracer can effectively defeat all malware samples tested via blocking malware behaviors while not causing a significant compatibility problem.

It has been a big challenge to develop a routing protocol that can meet different application needs and optimize routing paths according to the topology changes in mobile ad hoc networks. Basing their forwarding decisions only on local topology, geographic routing protocols have drawn a lot of attentions in recent years. However, there is a lack of holistic design for geographic routing to be more efficient and robust in a dynamic environment. Inaccurate local and destination position information can lead to inefficient geographic forwarding and even routing failure. The use of proactive fixed-interval beaconing to distribute local positions introduces high overhead when there is no traffic and cannot capture the topology changes under high mobility. It is also difficult to preset protocol parameters correctly to fit in different environments. In this work, we propose two self-adaptive on-demand geographic routing schemes which build efficient paths based on the need of user applications and adapt to various scenarios to provide efficient and reliable routing. To alleviate the impact due to inaccurate local topology knowledge, the topology information is updated at a node in a timely manner according to network dynamics and traffic demand. On-demand routing mechanism in both protocols reduces control overhead compared to the proactive schemes which are normally adopted in current geographic routing protocols. Additionally, our route optimization scheme adapts the routing path according to both topology changes and actual data traffic requirements. Furthermore, adaptive parameter setting scheme is introduced to allow each node to determine and adjust the protocol parameter values independently according to different network environments, data traffic conditions, and node's own conditions. Our simulation studies demonstrate that the proposed routing protocols are more robust and outperform the existing geographic routing protocol and conventional on-demand routing protocols under various conditions including different mobilities, node densities, traffic loads, and destination position inaccuracies. Specifically, the proposed protocols could reduce the packet delivery latency up to 80 percent as compared to GPSR at high mobility. Both routing protocols could achieve about 98 percent delivery ratios, avoid incurring unnecessary control overhead, have very low forwarding overhead and transmission delay in all test scenarios.

Enforcing a practical Mandatory Access Control (MAC) in a commercial operating system to tackle malware problem is a grand challenge but also a promising approach. The firmest barriers to apply MAC to defeat malware programs are the incompatible and unusable problems in existing MAC systems. To address these issues, we start our work by analyzing the technical details of 2,600 malware samples one by one and performing experiments over two types of MAC enforced operating systems. Based on the preliminary studies, we design a novel MAC model incorporating intrusion detection and tracing in a commercial operating system, named Tracer, in order to disable malware on hosts while offering good compatibility to existing software and good usability to common users who are not system experts. The model conceptually consists of three actions: detecting, tracing and restricting suspected intruders. One novelty is that it leverages light-weight intrusion detection and tracing techniques to automate security label configuration that is widely acknowledged as a tough issue when applying a MAC system in practice. The other is that, rather than restricting information flow as a traditional MAC does, it traces intruders and restricts only their critical malware behaviors, where intruders represent processes and executables that are potential agents of a remote attacker. Our prototyping and experiments on Windows show that Tracer can effectively defeat all malware samples tested via blocking malware behaviors while not causing a significant compatibility problem.

Multicast benefits data center group communications in saving network bandwidth and increasing application throughput. However, it is challenging to scale Multicast to support tens of thousands of concurrent group communications due to limited forwarding table memory space in the switches, particularly the low-end ones commonly used in modern data centers. Bloom Filter is an efficient tool to compress the Multicast forwarding table, but significant traffic leakage may occur when group membership testing is false positive. To reduce the Multicast traffic leakage, in this paper we bring forward a novel multi-class Bloom Filter (MBF), which extends the standard Bloom Filter by embracing element uncertainty. Specifically, MBF sets the number of hash functions in a per-element level, based on the probability for each Multicast group to be inserted into the Bloom Filter. We design a simple yet effective algorithm to calculate the number of hash functions for each Multicast group. We have prototyped a software based MBF forwarding engine on the Linux platform. Simulation and prototype evaluation results demonstrate that MBF can significantly reduce Multicast traffic leakage compared to the standard Bloom Filter, while causing little system overhead.

Internet structure possesses many properties of complex networks. However, existing studies are often constrained to deriving web connections based on partial data collected, and the actual Internet traffic and user behaviors are far from being understood. With detailed traffic flow records collected through powerful hardware-based monitors, we study from the perspective of complex network the characteristics of four types of traffic: P2Pdownload, HTTP, Instant Messaging and overall traffic. Based on the data analysis and comparison of different applications, we confirm that both the distributions of node degree and strength of nodes/edges follow power law but they have significant different exponents. Specifically, taking advantage of the strict timing of the records, we study the dynamics of flow graphs. The growth of edges upon nodes is nonlinear. Edges formed between existing nodes, instead of the ones arriving with new nodes dominate the growth. We also observe linear preferential attachment behaviors in the flow graphs.

In recent years, cognitive radio technology (CR) has been proposed to allow unlicensed secondary users (SUs) to opportunistically access the channels unused by primary users. As a result, there is a lot of recent interests on studying cognitive radio cellular networks (CogCells) that can support both PUs and SUs. Due to the limited transmission range of SUs, in this work we consider supporting Multi-hop infrastructure-based secondary systems (SSs), where SUs can communicate with the BS over multiple hops. The use of SSs improves the reliability and coverage compared to its single-hop counterpart. In addition, SUs are allowed to access multiple channels, which helps to increase transmission reliability and coverage and relieve interference at PUs. To enable multi-hop secondary transmissions, it is also important to support efficient routing. In CogCells, efficient admission control, channel assignment and routing is crucial for the coverage optimization of SSs and to ensure the QoS requirements in CogCells. In this paper, we mathematically formulate the problem of joint admission control, channel assignment and QoS routing to maximize the coverage of SUs in a CogCell system that supports multi-hop secondary transmissions, taking into account the interference constraints and QoS requirements from the PUs and admitted SUs. To our best knowledge, this is the first study that attempts to optimize the coverage of SUs in multi-hop CogCells with the concurrent support of the above three important procedures. We show that the problem is NP-hard and propose three different algorithms to solve the coverage optimization problem and give the theoretical analyses of its performances in terms of approximation ratio to the optimum. Our solutions include a greedy heuristic approximation scheme, an algorithm that can provide exact solution, and a new approximation solution with a poly-logarithmic approximation ratio guarantee, e.g., the performance of our algorithm is within a poly-logarithmic factor of that of any optimal algorithm for the problem. Our preliminary simulation results indicate that our new approximation algorithms can effectively exploit the increased number of SUs and channels, and performs much better than the theoretical worst case bound.

Group communications are important in Mobile Ad hoc Networks (MANETs). Multicast is an efficient method for implementing group communications. However, it is challenging to implement efficient and scalable multicast in MANET due to the difficulty in group membership management and multicast packet forwarding over a dynamic topology. We propose a novel Efficient Geographic Multicast Protocol (EGMP). EGMP uses a virtual-zone-based structure to implement scalable and efficient group membership management. A networkwide zone-based bidirectional tree is constructed to achieve more efficient membership management and multicast delivery. The position information is used to guide the zone structure building, multicast tree construction, and multicast packet forwarding, which efficiently reduces the overhead for route searching and tree structure maintenance. Several strategies have been proposed to further improve the efficiency of the protocol, for example, introducing the concept of zone depth for building an optimal tree structure and integrating the location search of group members with the hierarchical group membership management. Finally, we design a scheme to handle empty zone problem faced by most routing protocols using a zone structure. The scalability and the efficiency of EGMP are evaluated through simulations and quantitative analysis. Our simulation results demonstrate that EGMP has high packet delivery ratio, and low control overhead and multicast group joining delay under all test scenarios, and is scalable to both group size and network size. Compared to Scalable Position-Based Multicast (SPBM) [CHECK END OF SENTENCE], EGMP has significantly lower control overhead, data transmission overhead, and multicast group joining delay.

Many minimum energy (energy-efficient) routing protocols have been proposed in recent years. However, very limited effort has been made in studying routing overhead, route setup time, and route maintenance issues associated with these protocols. Without a careful design, an energy-efficient routing protocol can perform much worse than a normal routing protocol. In this paper, we first show that the minimum energy routing schemes in the literature could fail without considering the routing overhead involved and node mobility. We then propose a more accurate analytical model to track the energy consumptions due to various factors, and a simple energy-efficient routing scheme PEER to improve the performance during path discovery and in mobility scenarios. Our simulation results indicate that compared to a conventional energy-efficient routing protocol, PEER protocol can reduce up to 2/3 path discovery overhead and delay, and 50 percent transmission energy consumption.

In recent years, we have seen a surge of interest in enabling communications over meshed wireless networks. Particularly, supporting peer-to-peer communications over a multi-hop wireless network has a big potential in enabling ubiquitous computing. However, many wireless nodes have limited capabilities, for example, sensor nodes or small handheld devices. Also, the end-to-end capacity and delay degrade significantly as the path length increases with the number of network nodes. In these scenarios, the deployment of a backbone network could potentially facilitate higher performance network communications. In this paper, we study the novel Reinforcement Back-bone Network (RBN) deployment problem considering the practical limitation in the number of available backbone nodes and enforcing backbone network connectivity. We propose an iterative and adaptive (ITA) algorithm for efficient backbone network deployment. In addition, in order to provide the performance bound, we redefine and solve the problem by implementing the Generic Algorithm. Finally, we present our simulation results under various settings and compare the performance of the proposed ITA algorithm and the generic algorithm. Our study indicates that the proposed ITA algorithm is promising for deploying a connected RBN with a limited number of available backbone nodes.

Multiple-input and multiple-output (MIMO) technique is considered as one of the most promising emerging wireless technologies that can significantly improve transmission capacity and reliability in wireless mesh networks. While MIMO has been widely studied for single link transmission scenarios in physical layer as well as from MAC perspective, its impact on network layer, especially its interaction with routing has not drawn enough research attention. In this paper, we investigate the problem of routing in MIMO-based wireless mesh networks. We mathematically formulate the MIMO-enabled multi-source multi-destination multi-hop routing problem into a multi-commodity flow problem by identifying the specific opportunities and constraints brought by MIMO transmissions, in order to provide the fundamental basis for MIMO-aware routing design. We then use this formulation to develop a polynomial time approximation solution that maximizes the scaling factor for the concurrent flows in the network. Moreover, we also consider a more practical case where controllers are distributed, and propose a distributed algorithm to minimize the congestion in the network links based on steepest descent framework, which is proved to provide a fixed approximation ratio. The performance of the algorithms is evaluated through simulations and demonstrated to outperform the counterpart strategies without considering MIMO features.

In order to improve transmission throughput of a multi-hop wireless network, many efforts have been made in recent years to reduce traffic and hence transmission collisions by constructing backbone networks with minimum size. However, many other important issues need to be considered. Instead of simply minimizing the number of backbone nodes or supporting some isolated network features, in this work, we exploit the use of algebraic connectivity to control backbone network topology design for concurrent improvement of backbone network robustness, capacity, stability and routing efficiency. In order to capture other network features, we also provide a general cost function and introduce a new metric, connectivity efficiency, to tradeoff algebraic connectivity and cost for backbone construction. We have designed both centralized and distributed algorithms to build more robust and efficient backbone infrastructure to better support the application needs. Our performance studies demonstrate that, compared to peer work, our algorithms could achieve much higher throughput and delivery ratio, and much lower end-to-end delay and routing distances under all test scenarios.

With the popularity of wireless devices and the increase of computing and storage resources, there are increasing interests in supporting mobile computing techniques. Particularly, ad hoc networks can potentially connect different wireless devices to enable more powerful wireless applications and mobile computing capabilities. To meet the ever increasing communication need, it is important to improve the network throughput while guaranteeing transmission reliability. Multiple-input-multiple-output (MIMO) technology can provide significantly higher data rate in ad hoc networks where nodes are equipped with multi-antenna arrays. Although MIMO technique itself can support diversity transmission when channel condition degrades, the use of diversity transmission often compromises the multiplexing gain and is also not enough to deal with extremely weak channel. Instead, in this work, we exploit the use of cooperative relay transmission (which is often used in a single antenna environment to improve reliability) in a MIMO-based ad hoc network to cope with harsh channel condition. We design both centralized and distributed scheduling algorithms to support adaptive use of cooperative relay transmission. Our algorithm effectively exploits the cooperative multiplexing gain and cooperative diversity gain to achieve higher data rate and higher reliability under various channel conditions. Our scheduling scheme can efficiently invoke relay transmission without introducing significant signaling overhead as conventional relay schemes, and seamlessly integrate relay transmission with multiplexed MIMO transmission. We also design a MAC protocol to implement the distributed algorithm. Our performance results demonstrate that the use of cooperative relay in a MIMO framework could bring in a significant throughput improvement in all the scenarios studied, with the variation of node density, link failure ratio, packet arrival rate and retransmission threshold.

There are increasing interest and big challenges in designing a scalable and robust multicast routing protocol in a mobile ad hoc network (MANET) due to the difficulty in group membership management, multicast packet forwarding, and the maintenance of multicast structure over the dynamic network topology for a large group size or network size. In this paper, we propose a novel Robust and Scalable Geographic Multicast Protocol (RSGM). Several virtual architectures are used in the protocol without need of maintaining state information for more robust and scalable membership management and packet forwarding in the presence of high network dynamics due to unstable wireless channels and node movements. Specifically, scalable and efficient group membership management is performed through a virtual-zone-based structure, and the location service for group members is integrated with the membership management. Both the control messages and data packets are forwarded along efficient tree-like paths, but there is no need to explicitly create and actively maintain a tree structure. The stateless virtual-tree-based structures significantly reduce the tree management overhead, support more efficient transmissions, and make the transmissions much more robust to dynamics. Geographic forwarding is used to achieve further scalability and robustness. To avoid periodic flooding of the source information throughout the network, an efficient source tracking mechanism is designed. Furthermore, we handle the empty-zone problem faced by most zone-based routing protocols. We have studied the protocol performance by performing both quantitative analysis and extensive simulations. Our results demonstrate that RSGM can scale to a large group size and a large network size, and can more efficiently support multiple multicast groups in the network. Compared to existing protocols ODMRP and SPBM, RSGM achieves a significantly higher delivery ratio under all circumstances, with different moving speeds, node densities, group sizes, number of groups, and network sizes. RSGM also has the minimum control overhead and joining delay.

With the fast progress of multiple-input-multiple-output (MIMO) technology and its growing applications in networks, it is important to develop techniques to enable more efficient MIMO network communications. However, it is very challenging to coordinate node transmissions in a MIMO-based ad hoc network. In this work, we propose schemes that take advantage of the meshed topology of ad hoc networks to fully exploit the multiuser diversity and spatial diversity in order to maximize the data rate of the network while supporting different transmission priorities, reducing transmission delay, and ensuring fair transmissions among nodes. We formulate a concrete physical model and present cross-layer centralized and distributed scheduling algorithms that exploit physical-layer channel information to opportunistically schedule cooperative spatial multiplexed transmissions between nodes. We also propose a new MAC scheme to support our distributed algorithm. The performance of our algorithms are studied through extensive simulations, and the results demonstrate that our algorithms are very effective and can significantly increase the network throughput while reducing the transmission delay.

Multiple-input and multiple-output (MIMO) technique is considered as one of the most promising wireless technologies that can significantly improve transmission capacity and reliability. Many emerging mobile wireless applications require peer-to-peer transmissions over an ad hoc network, where the nodes often have different number of antennas, and the channel condition and network topology vary over time. It is important and challenging to develop efficient schemes to distributively coordinate transmission resource sharing among a heterogeneous group of nodes over an infrastructure-free mobile ad hoc network. In this work, we propose a holistic distributed scheduling algorithm that can adaptively select different transmission strategies based on the node types and channel conditions to effectively relieve the bottleneck effect caused by nodes with smaller antenna arrays, and avoid transmission failure due to violation of channel constraint. The algorithm also takes advantage of channel information to opportunistically schedule cooperative spatial multiplexed transmissions between nodes and provide special transmission support for higher priority nodes with weak channels, so that the data rate of the network can be maximized while user transmission quality requirement is supported. The performance of our algorithm is studied through extensive simulations and the results demonstrate that our algorithm is very effective in handling node heterogeneity and channel constraint, and can significantly increase the throughput while reducing the transmission delay.

It is important and challenging to develop efficient schemes to coordinate node transmissions in a MIMO-based ad hoc network. In this work, we propose a scheme to fully exploit the multiuser diversity and spatial diversity by taking advantage of the meshed topology, while also supporting user transmission quality requirement. We formulate a concrete physical model, and present cross-layer algorithms which take advantage of physical layer channel information to opportunistically schedule cooperative spatial multiplexed transmissions between nodes, so that the data rate of the network can be maximized. The performance of our algorithm is studied by extensive simulations and the results demonstrate that our algorithm is very effective and can significantly increase the throughput while reducing the transmission delay.

Group communications are important in mobile ad hoc networks (MANET). Multicast is an efficient method to implement the group communications. However, it is challenging to implement scalable, robust and efficient multicast in MANET due to the difficulty in group membership management, multicast packet forwarding and the maintenance of a tree-or mesh-based multicast structure over the dynamic topology for a large group size or network size. We propose a novel robust and scalable geographic multicast protocol (RSGM). Scalable and efficient group membership management has been performed through zone-based structure, and the location service for group members is combined with membership management. Both the control messages and data packets are forwarded along efficient tree-shape paths, but there is no need to actively maintain a tree structure, which efficiently reduces the maintenance overhead and makes the transmissions more robust to dynamics. Geographic forwarding is used to achieve further scalability and robustness. To avoid periodic flooding-based sources' announcements, an efficient source tracking mechanism is designed. Furthermore, we handle the empty zone problem faced by most zone-based routing protocols. Our simulation studies show that RSGM can scale to large group size and large network size, and a high delivery ratio is achieved by RSGM even under high dynamics.

Supporting scalable and efficient routing and service provision in mobile ad hoc networks (MANET) has been a big research challenge. Conventional topology-based unicast and multicast protocols are normally hard to scale due to the big overhead in their routing schemes. Supported by these routing protocols, conventional service discovery schemes also have limited scalability and efficiency. Basing their forwarding decisions only on the local topology, geographic-based unicast routing protocols have drawn a lot of attentions in recent years. However, current geographic unicast routing can not adapt to different traffic conditions in a service provision network and current geographic multicast protocols can hardly scale to a large network size and group size. We propose a geographic routing and service provision framework for MANET which possesses the features of scalability, efficiency, robustness and adaptability. In the framework, an efficient hierarchical structure is built and maintained, based on which a scalable membership management is deployed to efficiently track the resource and service states. An adaptive and reactive geographic unicast routing protocol and a scalable and robust multicast protocol are designed to meet the different routing requirements in a service provision network. With the support of all of these components, the service provision functions, such as service discovery, delivery and coordination, can be deployed in the framework

With the improvement of the traffic complexity extent, the solution of traffic problems becomes more and more difficult in the reality, so traffic simulation is an effective method for analyzing the traffic states and problems. As the simulation area becomes larger and more complicate, which requires large computing and storage resources, it can't be resolved by the traditional computing technology. Due to grid technology's advantages on such aspects, a new simulation architecture named GHA, which implements the HLA 's component as grid service and combined with the agent technology is presented in this paper. Thus the simulation architecture can offer high-capable computing resources to solve the complex traffic issues with great expansibility and flexibility; moreover, it also makes solid foundation to the authenticity of traffic simulation by adopting agent model the character of traffic entity. At last, a traffic simulation platform is implemented based on GHA and the performance tests are given

Wireless broadband networks, especially WiMAX networks, have emerged in the industry recently and many challenging research issues arise. In this paper, we proposed a heuristic clustering algorithm for minimum cost wireless broadband overlay network deployment Moreover, we also modified and implemented two heuristic algorithms based on classic linear programming based capacitated facility location algorithms. We analyzed the theoretical worst-case performance ratio of our algorithm and our numerical results showed that our algorithm performs much better in practical network settings

Group communications is important in supporting multimedia applications. Multicast is an efficient method in implementing the group communications. However, it is challenging to implement efficient and scalable multicast in mobile ad hoc networks (MANET) due to the difficulty in group membership management and multicast packet forwarding over the dynamic topology. We propose a novel efficient geographic multicast protocol (EGMP). EGMP uses a zone-based structure to implement scalable and efficient group membership management and a network-range zone-based bi-directional tree is constructed to achieve a more efficient multicast delivery. The position information is used to guide the zone structure building, multicast tree construction and multicast packet forwarding, which efficiently reduces the overhead for route searching and tree structure maintenance. EGMP does not depend on any specific geographic unicast routing protocol. Several methods are assumed to further make the protocol efficient, for example, introducing the concept of zone depth for building an optimal tree structure and combining the location search of group members with the hierarchical group membership management. Finally, we design a scheme to handle empty zone problem faced by most routing protocols using a zone structure.

As more and more wireless subscribers access the Internet through cellular networks, Internet data traffic, which is known to be long range dependent (LRD), will soon dominate the conventional voice traffic. In this paper, we study the impact of such LRD data traffic on the statistical characteristics of multi-access interference (MAI) and signal to interference-plus-noise ratio (SINR) in a code division multiple access (CDMA) network. Through analysis and simulation, we show that the time-scaled MAI and SINR have slow decaying tail distributions due to the LRD data traffic. As a result, the outage probability is larger for data users than that for voice users. To improve the performance of the CDMA network in the presence of LRD data traffic, we propose a variable period prediction scheme to predict MAI or the equivalent number of active users. We show that the proposed variable period prediction is not only more accurate for data users but also less memory-consuming than existing fixed period prediction. In addition, rate control based on variable period prediction can achieve lower outage probability and higher throughput for data users than that based on fixed period prediction.

A typical data-dissemination sensor network consists of a large number of sensor nodes, which are normally energy-constrained and unlikely to be recharged in field. Redundant sensor nodes are often deployed to increase network robustness, and to extend network lifetime. Target and inquirer mobilities further bring more challenges to large-scale sensor networks. Frequent location updates for multiple inquirers and targets can drain the limited onboard energy excessively. We present EEDD, an energy-efficient data-dissemination protocol to address both the target and inquirer mobility problem and the energy conservation problem. We extend network lifetime by adopting a virtual-grid-based two-level architecture to schedule the activities of sensor nodes. Furthermore, we propose an adaptive scheduling scheme and a data dissemination scheme to reduce sensing delay and packet forwarding delay. The simulation results show that our protocol saves up to twelve times energy without much increased delay when compared with protocol not considering energy efficiency.

Energy conservation is important for ad hoc networks. However, little effort has been made to carefully study the energy cost metrics upon which the design of various energy efficient algorithms is based. More specifically, most existing energy consumption models only considered energy cost of exchanging data packets, although common wireless protocols also need control packets (e.g., ACK) for reliable data transmissions. Without considering the energy cost of exchanging control packets, these existing models tend to underestimate the actual energy consumption, and thus leading to suboptimal energy efficient designs. In this paper, we develop energy consumption models that take into account energy consumption due to data packets, control packets and retransmission. We verify by simulations that our models match the actual energy consumption much better than existing models. In addition, we show that a minimum energy routing protocol based on an accurate model of ours performs much better than those based on existing models

Many minimum energy (energy efficient) routing protocols have been proposed so far. However, a few effort has been spent on the routing overhead, route setup time, and route maintenance issues associated with such protocols. This paper first shows that the minimum energy routing schemes in the literature could fail without considering the routing overhead involved and the node mobility. It then proposes a more accurate analytical model to track the energy consumption and the impact of packets errors, and a simple energy-efficient routing scheme to improve the performance in mobility scenarios. The simulation results indicate that the PEER-based energy efficient routing has significantly higher performance than that of a normal energy-based routing scheme.

Guard channels have been proposed to minimize handoff call dropping when mobile hosts move from one cell to another. Code-division multiple-access (CDMA) systems are power- and interference-limited. Therefore, guard capacity in CDMA networks is soft, that is, a given capacity corresponds to variable number of connections. Thus, it is essential to adjust the guard capacity in response to changes in traffic conditions and user mobility. We propose two schemes for managing downlink CDMA radio resources: guard capacity adaptation based on dropping (GAD), and guard capacity adaptation based on prediction and dropping (GAPD). In both schemes, the guard capacity of a cell is dynamically adjusted so as to maintain the handoff dropping rate at a target level. In the second scheme, there is an additional, frequent adjustment component where guard capacity is adjusted based on soft handoff prediction. We show through extensive simulations that GAD and GAPD control the handoff dropping rate effectively under varying traffic conditions and system parameters. We also find that GAPD is more robust than GAD to temporal traffic variations and changes in control parameters.

The rapid deployment of new applications and the interconnection of networks with increasing diversity of technologies and capacity make it more challenging to provide end-to-end quality assurance to the value-added services, such as the transmission of real-time multimedia and mission critical data. In a network with enhancements for QoS support, pricing of network services based on the level of service, usage, and congestion provides a natural and equitable incentive for multimedia applications to adapt their sending rates according to network conditions. We have developed an intelligent service architecture that integrates resource reservation, negotiation, pricing and adaptation in a flexible and scalable way. In this paper, we present a generic pricing structure that characterizes the pricing schemes widely used in the current Internet, and introduce a dynamic, congestion-sensitive pricing algorithm that can be used with the proposed service framework. We also develop the demand behavior of adaptive users based on a physically reasonable user utility function. We introduce our multimedia testbed and describe how the proposed intelligent framework can be implemented to manage a video conference system. We develop a simulation framework to compare the performance of a network supporting congestion-sensitive pricing and adaptive reservation to that of a network with a static pricing policy. We study the stability of the dynamic pricing and reservation mechanisms, and the impact of various network control parameters. The results show that the congestion-sensitive pricing system takes advantage of application adaptivity to achieve significant gains in network availability, revenue, and user-perceived benefit relative to the fixed-price policy. Congestion-based pricing is stable and effective in limiting utilization to a targeted level. Users with different demand elasticity are seen to share bandwidth fairly, with each user having a bandwidth share proportional to its relative willingness to pay for bandwidth. The results also show that even a small proportion of adaptive users may result in a significant performance benefit and better service for the entire user population-both adaptive and nonadaptive users. The performance improvement given by the congestion-based adaptive policy further improves as the network scales and more connections share the resources. Finally, we complement the simulation with experimental results demonstrating important features of the adaptation process.

As CDMA-based cellular networks mature, the current point-to-point links used in connecting base stations to network controllers evolve to an IP-based radio access network (RAN) for reasons of lower cost due to statistical multiplexing gains, better scalability and reliability, and the projected growth in data applications. In this paper, we study the impact of congestion in a best-effort IP RAN on CDMA cellular voice networks. We propose and evaluate three congestion control mechanisms, admission control, diversity control, and router control, to maximize network capacity while maintaining good voice quality. We first propose two new enhancements to CDMA call admission control that consider a unified view of both IP RAN and air interface resources. Next, we introduce a novel technique called diversity control that exploits the soft-handoff feature of CDMA networks and drops selected frames belonging to multiple soft-handoff legs to gracefully degrade-voice quality during congestion. Finally, we study the impact of router control where an active queue management technique is used to reduce delay and minimize correlated losses. Using simulations of a large mobile network, we show that the three different control mechanisms can help gracefully manage 10-40 percent congestion overload in the IP RAN.

Guard channels have been proposed to minimize handoff call dropping when mobile hosts move from one cell to another. CDMA systems are power- and interference-limited. Therefore, guard capacity in CDMA networks is soft, that is, a given capacity corresponds to variable number of connections. Thus, it is essential to adjust the guard capacity in response to changes in traffic conditions and user mobility. We propose two schemes for managing downlink CDMA radio resources: guard capacity adaptation based on dropping (GAD), and guard capacity adaptation based on prediction and dropping (GAPD). In both schemes, the guard capacity of a cell is dynamically adjusted so as to maintain the handoff dropping rate at a target level. In the second scheme, there is an additional, frequent adjustment component where guard capacity is adjusted based on soft handoff prediction. We show through extensive simulations that GAD and GAPD control the handoff dropping rate effectively under varying traffic conditions and system parameters. We also find that GAPD is more robust than GAD to temporal traffic variations and changes in control parameters.

Current minimum energy routing schemes in wireless networks only consider energy consumption for transmitting data packets. However most wireless devices also transmit some control packets (such as RTS and CTS in 802.11) besides data packets. Without considering the energy consumption for control packets, the existing minimum energy routing schemes tend to use more intermediate nodes, which results in more energy consumption and less throughput. We first propose more comprehensive energy consumption models that consider the energy consumption for data packets as well as control packets. Based on these models, we propose our minimum energy routing scheme. The simulation results verify that our scheme performs better than the existing minimum energy routing schemes in terms of energy consumption as well as throughput.

Energy conservation is a primary concern in sensor networks. Several MAC protocols have been proposed to address this concern. However, the tradeoff between power consumption and latency has not been thoroughly studied. In this paper, we propose a sensor medium access control protocol with dynamic duty cycle, DSMAC, which achieves a good tradeoff between the two performance metrics without incurring much overhead. Moreover, DSMAC is able to adjust its duty cycle with varying traffic conditions without assuming any prior knowledge of application requirements. Both analytical and simulation results have been presented in this paper.

As CDMA-based cellular networks mature, the current point-to-point links used in connecting base stations to network controllers will evolve to an IP-based radio access network (RAN) for reasons of lower cost due to statistical multiplexing gains, better scalability and reliability, and the projected growth in data applications. In this paper, we study the impact of congestion in a best-effort IP RAN on CDMA cellular voice networks. We propose and evaluate three congestion control mechanisms, admission control, diversity control, and router control, to maximize network capacity while maintaining good voice quality. We first propose two new enhancements to CDMA call admission control that consider a unified view of both IP RAN and air interface resources. Next, we introduce a novel technique called diversity control that exploits the soft-handoff feature of CDMA networks and drops selected frames belonging to multiple soft-handoff legs to gracefully degrade voice quality during congestion. Finally, we study the impact of router control where an active queue management technique is used to reduce delay and minimize correlated losses. Using simulations of a large mobile network, we show that the three different control mechanisms can help gracefully manage 10-40% congestion overload in the IP RAN.

In earlier work, we had proposed a pricing model in which service prices are based on QoS (resources consumed) and long-term user demand, and also have a congestion-sensitive component to motivate rate and service adaptation by applications with elastic demand. The network is provisioned to provide multiple services, with short-term, dynamic configuration of network resources. Congestion pricing schemes in the network literature fall into two basic categories: tatonnement and bandwidth auctions. As far as we know, there has been no work comparing these two schemes in the same environment. The goal of this paper is to develop pricing schemes based on the above two approaches, in an environment with short-term resource allocation and demand adaptation. We address some important practical issues related to making the schemes work in such an environment. We compare the tatonnement, and auction-based schemes with respect to network utilization, connection blocking rate, user satisfaction and network revenue, and draw some general conclusions about the relative benefits of the two approaches.

The differentiated services framework (DiffServ) has been proposed to provide multiple quality of service (QoS) classes over IP networks. A network supporting multiple classes of service also requires a differentiated pricing structure. We propose a pricing scheme in a DiffServ environment based on the cost of providing different levels of quality of service to different classes, and on long-term demand. Pricing of network services dynamically based on the level of service, usage, and congestion allows a more competitive price to be offered, allows the network to be used more efficiently, and provides a natural and equitable incentive for applications to adapt their service contract according to network conditions. We develop a DiffServ simulation framework to compare the performance of a network supporting congestion-sensitive pricing and adaptive service negotiation to that of a network with a static pricing policy. Adaptive users adapt to price changes by adjusting their sending rate or selecting a different service class. We also develop the demand behavior of adaptive users based on a perceptually reasonable user utility function. Simulation results show that a congestion-sensitive pricing policy coupled with user rate adaptation is able to control congestion and allow a service class to meet its performance assurances under large or bursty offered loads, even without explicit admission control. Users are able to maintain a stable expenditure. Allowing users to migrate between service classes in response to price increases further stabilizes the individual service prices. When admission control is enforced, congestion-sensitive pricing still provides an advantage in terms of a much lower connection blocking rate at high loads.

Little attention has been given to understanding the fault recovery characteristics and performance tuning of native IP multicast networks. This paper focuses on the interactions of the component protocols to understand their behavior in network failure and recovery scenarios. We consider a multicast environment based on the Protocol Independent Multicast (PIM) routing protocol, the Internet Group Management Protocol (IGMP) and the Open Shortest Path First (OSPF) protocol. Analytical models are presented to describe the interplay of all of these protocols in various multicast channel recovery scenarios. Quantitative results for the recovery time of IP multicast channels are given as references for network configurations, and protocol development. Simulation models are developed using the OPNET simulation tool to measure the fault recovery time and the associated protocol control overhead, and study the influence of important protocol parameters. A testbed with five Cisco routers is configured with PIM, OSPF, and IGMP to measure the multicast channel failure and recovery times for a variety of different link and router failures. In general, the failure recovery is found to be lightweight in term of control overhead and recovery time. Failure recovery time in a WAN is found to be dominated by the unicast protocol recovery process. Failure recovery in a LAN is more complex, and strongly influenced by protocol interaction and implementation specifics. Suggestion for improvement of the failure recovery time via protocol enhancements, parameter tuning, and network configuration are provided.

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No abstract available.

We study a dynamic, usage- and congestion-dependent pricing system in conjunction with price-sensitive user adaptation of network usage. We first present a resource negotiation and pricing (RNAP) protocol and architecture to enable users to select and dynamically renegotiate network services. We develop mechanisms within the RNAP architecture for the network to dynamically formulate prices and communicate pricing and charging information to the users. We then outline a general pricing strategy in this context. We discuss candidate algorithms by which applications (singly, or as part of a multi-application system) can adapt their rate and QoS requests, based on the user-perceived value of a given combination of transmission parameters. Finally, we present experimental results to show that usage- and congestion-dependent pricing can effectively reduce the blocking probability, and allow bandwidth to be shared fairly among applications, depending on the elasticity of their respective bandwidth requirements.