Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding and the emergence of large language models capable of processing document-based questions. While various methods have been proposed to tackle the complexities of VsTP, existing solutions often rely on task-specific architectures and objectives for individual tasks. This leads to modal isolation and complex workflows due to the diversified targets and heterogeneous schemas. In this paper, we introduce OmniParser V2, a universal model that unifies VsTP typical tasks, including text spotting, key information extraction, table recognition, and layout analysis, into a unified framework. Central to our approach is the proposed Structured-Points-of-Thought (SPOT) prompting schemas, which improves model performance across diverse scenarios by leveraging a unified encoder-decoder architecture, objective, and input&output representation. SPOT eliminates the need for task-specific architectures and loss functions, significantly simplifying the processing pipeline. Our extensive evaluations across four tasks on eight different datasets show that OmniParser V2 achieves state-of-the-art or competitive results in VsTP. Additionally, we explore the integration of SPOT within a multimodal large language model structure, further enhancing visual text parsing capabilities on four tasks, thereby confirming the generality of SPOT prompting technique.

Visual Information Extraction (VIE), aiming at extracting structured information from visually rich document images , plays a pivotal role in document processing. Considering various layouts, semantic scopes, and languages, VIE encompasses an extensive range of types, potentially numbering in the thousands. However, many of these types suffer from a lack of training data , which poses significant challenges. In this paper, we propose a novel generative model , named Generative Compositor, to address the challenge of few-shot VIE. The Generative Compositor is a hybrid pointer-generator network that emulates the operations of a compositor by retrieving words from the source text and assembling them based on the provided prompts. Furthermore, three pre-training strategies are employed to enhance the model’s perception of spatial context information. Besides, a prompt-aware resampler is specially designed to enable efficient matching by leveraging the entity-semantic prior contained in prompts. The introduction of the prompt-based retrieval mechanism and the pre-training strategies enable the model to acquire more effective spatial and semantic clues with limited training samples . Experiments demonstrate that the proposed method achieves highly competitive results in the full-sample training, while notably outperforms the baseline in the 1-shot, 5-shot, and 10-shot settings.

Dataset distillation techniques have revolutionized the way of utilizing large datasets by compressing them into smaller, yet highly effective subsets that preserve the original datasets' accuracy. However, while these methods have proven effective in reducing data size and training times, the robustness of these distilled datasets against adversarial attacks remains underexplored. This vulnerability poses significant risks, particularly in security-sensitive applications. To address this critical gap, we introduce DD-RobustBench, a novel and comprehensive benchmark specifically designed to evaluate the adversarial robustness of distilled datasets. Our benchmark is the most extensive of its kind and integrates a variety of dataset distillation techniques, including recent advancements such as TESLA, DREAM, SRe2L, and D4M, which have shown promise in enhancing model performance. DD-RobustBench also rigorously tests these datasets against a diverse array of adversarial attack methods to ensure broad applicability. Our evaluations cover a wide spectrum of datasets, including but not limited to, the widely used ImageNet-1K. This allows us to assess the robustness of distilled datasets in scenarios mirroring real-world applications. Furthermore, our detailed quantitative analysis investigates how different components involved in the distillation process, such as data augmentation, downsampling, and clustering, affect dataset robustness. Our findings provide critical insights into which techniques enhance or weaken the resilience of distilled datasets against adversarial threats, offering valuable guidelines for developing more robust distillation methods in the future. Through DD-RobustBench, we aim not only to benchmark but also to push the boundaries of dataset distillation research by highlighting areas for improvement and suggesting pathways for future innovations in creating datasets that are not only compact and efficient but also secure and resilient to adversarial challenges. The implementation details and essential instructions are available on DD-RobustBench.

Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to address the challenging problem of VsTP. However, due to the diversified targets and heterogeneous schemas, previous works usually design task-specific architectures and objectives for individual tasks, which in- advertently leads to modal isolation and complex workflow. In this paper, we propose a unified paradigm for parsing visually-situated text across diverse scenarios. Specifically, we devise a universal model, called OmniParser, which can simultaneously handle three typical visually-situated text parsing tasks: text spotting, key information extraction, and table recognition. In OmniParser, all tasks share the unified encoder-decoder architecture, the unified objective: point- conditioned text generation, and the unified input&output representation: prompt & structured sequences. Extensive experiments demonstrate that the proposed OmniParser achieves state-of-the-art (SOTA) or highly competitive performances on 7 datasets for the three visually-situated text parsing tasks, despite its unified, concise design. The code is available at AdvancedLiterateMachinery.

Automatic Music Transcription (AMT) aims to convert music audio into digital sheet music. Piano transcription is a popular but challenging subtask of AMT. For every piano pitch, the harmonic structure is fixed in the frequency domain, while the Transformer based on self-attention has great potential to extract features in the long sequence. In this paper, we propose piano harmonic attention, a mask self-attention, for better capturing harmonic features. The mask matrix is designed with the harmonic prior to pre-modeling the harmonic structure during calculating attention scores. To verify its effectiveness, we append the harmonic attention-based Transformer after every convolutional neural network block of the High-resolution piano transcription system. The evaluation results on the MAESTRO dataset show that the proposed model achieves comprehensive improvements over the baseline, with a note F1 score of 97.33%, which is comparable to the state-of-the-art system.

Active Speaker Detection (ASD) plays a crucial role in scene understanding tasks by determining whether an on-screen person in a given scene is speaking.In this work, to address the ASD in the context of multi-party roundtable meetings, we propose a novel approach that incorporates the fusion of spatial information of the scenes.To leverage the multiple data sources of the scenes, our method involves generating audio spatial spectrum heatmaps from the multi-channel audio and integrating them with the panoramic images.Additionally, we propose the novel FisheyeMeeting dataset, which combines fisheye panoramic video recordings with muti-channel audio captured from a six-channel circular microphone array.By enabling the multi-modal model to capture audio-visual cues in multi-party meeting scenes, our approach achieves an impressive 89.11% mAP on the FisheyeMeeting dataset.Notably, this outperforms the current SOTA methods by a significant 2.3% mAP improvement.

Text recognition is an inherent integration of vision and language, encompassing the visual texture in stroke patterns and the semantic context among the character sequences. Towards advanced text recognition, there are three key challenges: (1) an encoder capable of representing the visual and semantic distributions; (2) a decoder that ensures the alignment between vision and semantics; and (3) consistency in the framework during pre-training, if it exists, and fine-tuning. Inspired by masked autoencoding, a successful pre-training strategy in both vision and language, we propose an innovative scene text recognition approach, named VL-Reader. The novelty of the VL-Reader lies in the pervasive interplay between vision and language throughout the entire process. Concretely, we first introduce a Masked Visual-Linguistic Reconstruction (MVLR) objective, which aims at simultaneously modeling visual and linguistic information. Then, we design a Masked Visual-Linguistic Decoder (MVLD) to further leverage masked vision-language context and achieve bi-modal feature interaction. The architecture of VL-Reader maintains consistency from pre-training to fine-tuning. In the pre-training stage, VL-Reader reconstructs both masked visual and text tokens, while in the fine-tuning stage, the network degrades to reconstruct all characters from an image without any masked regions. VL-reader achieves an average accuracy of 97.1% on six typical datasets, surpassing the SOTA by 1.1%. The improvement was even more significant on challenging datasets. The results demonstrate that vision and language reconstructor can serve as an effective scene text recognizer.

Piano transcription is a significant problem in the field of music information retrieval, aiming to obtain symbolic representations of music from captured audio or visual signals. Previous research has mainly focused on single-modal transcription methods using either audio or visual information, yet there is a small number of studies based on audio-visual fusion. To leverage the complementary advantages of both modalities and achieve higher transcription accuracy, we propose a two-stage audio-visual fusion piano transcription model based on the attention mechanism, utilizing both audio and visual information from the piano performance. In the first stage, we propose an audio model and a visual model. The audio model utilizes frequency domain sparse attention to capture harmonic relationships in the frequency domain, while the visual model includes both CNN and Transformer branches to merge local and global features at different resolutions. In the second stage, we employ cross-attention to learn the correlations between different modalities and the temporal relationships of the sequences. Experimental results on the OMAPS2 dataset show that our model achieves an F1-score of 98.60%, demonstrating significant improvement compared with the single-modal transcription models.

Recently, Visual Information Extraction (VIE) has been becoming increasingly important in both the academia and industry, due to the wide range of real-world applications. Previously, numerous works have been proposed to tackle this problem. However, the benchmarks used to assess these methods are relatively plain, i.e., scenarios with real-world complexity are not fully represented in these benchmarks. As the first contribution of this work, we curate and release a new dataset for VIE, in which the document images are much more challenging in that they are taken from real applications, and difficulties such as blur, partial occlusion, and printing shift are quite common. All these factors may lead to failures in information extraction. Therefore, as the second contribution, we explore an alternative approach to precisely and robustly extract key information from document images under such tough conditions. Specifically, in contrast to previous methods, which usually either incorporate visual information into a multi-modal architecture or train text spotting and information extraction in an end-to-end fashion, we explicitly model entities as semantic points, i.e., center points of entities are enriched with semantic information describing the attributes and relationships of different entities, which could largely benefit entity labeling and linking. Extensive experiments on standard benchmarks in this field as well as the proposed dataset demonstrate that the proposed method can achieve significantly enhanced performance on entity labeling and linking, compared with previous state-of-the-art models. Dataset is available at https://www.modelscope.cn/datasets/damo/SIBR/summary.

Automatic singing transcription (AST), which refers to the process of inferring the onset, offset, and pitch from the singing audio, is of great significance in music information retrieval. Most AST models use the convolutional neural network to extract spectral features and predict the onset and offset moments separately. The frame-level probabilities are inferred first, and then the note-level transcription results are obtained through post-processing. In this paper, a new AST framework called MusicYOLO is proposed, which obtains the note-level transcription results directly. The onset/offset detection is based on the object detection model YOLOX, and the pitch labeling is completed by a spectrogram peak search. Compared with previous methods, the MusicYOLO detects note objects rather than isolated onset/offset moments, thus greatly enhancing the transcription performance. On the sight-singing vocal dataset (SSVD) established in this paper, the MusicYOLO achieves an 84.60% transcription F1-score, which is the state-of-the-art method.

Sight-singing exercises are a fundamental part of music education. In this paper, we present an objective and complete automatic evaluation system for sight-singing, which has two critical stages: note transcription and note alignment. In the first stage, we use an onset detector based on the convolutional recurrent neural network (CRNN) for note segmentation and the pitch extractor described in (Kimet al.2018) for note labeling. In the second stage, an alignment algorithm based on relative pitch modeling is proposed. Due to the lack of datasets for sight-singing note alignment and the overall system evaluation, we construct the sight-singing vocal dataset (SSVD). Each module of the system and the entire system are tested on this dataset. The onset detector achieves an F-measure of 90.61%, and the stages of note transcription and note alignment achieve an F-measure of 88.42% and 94.79%, respectively. In addition, we propose an objective criterion for the sight-singing evaluation system. Based on this criterion, our automatic sight-singing system achieves an F-measure of 77.95% on the SSVD dataset.

Recently, vision-language joint representation learning has proven to be highly effective in various scenarios. In this paper, we specifically adapt vision-language joint learning for scene text detection, a task that intrinsically involves cross-modal interaction between the two modalities: vision and language, since text is the written form of language. Concretely, we propose to learn contextualized, joint representations through vision-language pretraining, for the sake of enhancing the performance of scene text detectors. Towards this end, we devise a pre-training architecture with an image encoder, a text encoder and a cross-modal encoder, as well as three pretext tasks: image-text contrastive learning (ITC), masked language modeling (MLM) and word-in-image prediction (WIP). The pretrained model is able to produce more informative representations with richer semantics, which could readily benefit existing scene text detectors (such as EAST and PSENet) in the down-stream text detection task. Extensive experiments on standard benchmarks demonstrate that the proposed paradigm can significantly improve the performance of various representative text detectors, outperforming previous pre-training approaches. The code and pre-trained models will be publicly released.

In this paper, we propose MusicYOLO based on object detection to detect the onset and offset in singing for the first time. The onset of the vocal is not as stable and clear as that of musical instruments, which makes the frame-based onset/offset detection methods often not work well. Compared with the previous onset/offset detection methods, MusicYOLO detects the whole note object in the spectrogram image instead of transient frame features around onset/offset, improving the onset/offset detection performance significantly. The experiment results show that the MusicYOLO framework has obtained a 94.16% F1 score of onset detection and a 91.35% F1 score of offset detection on the ISMIR2014 dataset, which proves that MusicYOLO is the state-of-the-art onset/offset detection framework for singing situation.

This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. We design a progressive generator which comprises a sequence of transfer blocks. Each block performs an intermediate transfer step by modeling the relationship between the condition and the target poses with attention mechanism. Two types of blocks are introduced, namely pose-attentional transfer block (PATB) and aligned pose-attentional transfer block (APATB). Compared with previous works, our model generates more photorealistic person images that retain better appearance consistency and shape consistency compared with input images. We verify the efficacy of the model on the Market-1501 and DeepFashion datasets, using quantitative and qualitative measures. Furthermore, we show that our method can be used for data augmentation for the person re-identification task, alleviating the issue of data insufficiency. Code and pretrained models are available at: https://github.com/tengteng95/Pose-Transfer.git.

In recent years, an increasing amount of new social applications have been emerging and developing with the profound success of deep learning technologies, which have been significantly reshaping our daily life, e.g., interactive games and virtual reality. Deep learning applications are generally driven by a huge amount of training samples collected from the users’ participation, e.g., smartphones and watches. However, the users’ data privacy and security issues have been one of the main restrictions for a broader distribution of these applications. In order to preserve privacy while utilizing deep learning applications, federated learning becomes one of the most promising solutions, which gains growing attention from both academia and industry. It can provide high-quality model training by distributing the training tasks to individual users, relying on on-device local data. To this end, we model the users’ participation in social model training as a training service market. The market consists of model owners (MOs) as consumers (e.g., social applications) who purchase the training service and a large number of mobile device groups (MDGs) as service providers who contribute local data in federated learning. A two-layer hierarchical dynamic game is formulated to analyze the dynamics of this market. The service selection processes of MOs are modeled as a lower level evolutionary game, while the pricing strategies of MDGs are modeled as a higher level differential game. The uniqueness and stability of the equilibrium are analyzed theoretically and verified via extensive numerical evaluations.

Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios. However, they might still fall short when handling text instances of extreme aspect ratios and varying scales. To tackle such difficulties, we propose in this paper a new algorithm for scene text detection, which puts forward a set of strategies to significantly improve the quality of text localization. Specifically, a Text Feature Alignment Module (TFAM) is proposed to dynamically adjust the receptive fields of features based on initial raw detections; a Position-Aware Non-Maximum Suppression (PA-NMS) module is devised to selectively concentrate on reliable raw detections and exclude unreliable ones; besides, we propose an Instance-wise IoU loss for balanced training to deal with text instances of different scales. An extensive ablation study demonstrates the effectiveness and superiority of the proposed strategies. The resulting text detection system, which integrates the proposed strategies with a leading scene text detector EAST, achieves state-of-the-art or competitive performance on various standard benchmarks for text detection while keeping a fast running speed.

Piano transcription is a classic problem in music information retrieval. More and more transcription methods based on deep learning have been proposed in recent years. In 2019, Google Brain published a larger piano transcription dataset, MAESTRO. On this dataset, Onsets and Frames transcription approach proposed by Hawthorne achieved a stunning onset F1 score of 94.73%. Unlike the annotation method of Onsets and Frames, Transition-aware model presented in this paper annotates the attack process of piano signals called atack transition in multiple frames, instead of only marking the onset frame. In this way, the piano signals around onset time are taken into account, enabling the detection of piano onset more stable and robust. Transition-aware achieves a higher transcription F1 score than Onsets and Frames on MAESTRO dataset and MAPS dataset, reducing many extra note detection errors. This indicates that Transition-aware approach has better generalization ability on different datasets.

Piano transcription is a fundamental problem in the field of music information retrieval. At present, a large number of transcriptional studies are mainly based on audio or video, yet there is a small number of discussion based on audio-visual fusion. In this paper, a piano transcription model based on strategy fusion is proposed, in which the transcription results of the video model are used to assist audio transcription. Due to the lack of datasets currently used for audio-visual fusion, the OMAPS data set is proposed in this paper. Meanwhile, our strategy fusion model achieves a 92.07% F1 score on OMAPS dataset. The transcription model based on feature fusion is also compared with the one based on strategy fusion. The experiment results show that the transcription model based on strategy fusion achieves better results than the one based on feature fusion.

In this article, we employ multiple energy harvesting relays to assist information transmission from a multiantenna hybrid access point (HAP) to a receiver. All the relays are wirelessly powered by the HAP in the power-splitting (PS) protocol. We introduce the novel concept of hybrid relay communications, which allows each relay to switch between two radio modes, i.e., the active RF communications and the passive backscatter communications, according to its channel and energy conditions. We aim to jointly optimize the HAP's beamforming, individual relays' radio modes, PS ratios, and the relays' collaborative beamforming strategies to enhance the throughput performance at the receiver. The resulting formulation becomes a combinatorial and nonconvex problem. We first propose a convex approximation to the original problem, which serves as a lower bound of the relay performance. Then, we design an iterative algorithm that decomposes the binary relay mode optimization from the other operating parameters. In the inner loop of the algorithm, we exploit the structural properties to optimize the relay performance with the fixed relay mode by using alternating optimization. In the outer loop, different performance metrics are derived to guide the search for a set of passive relays to further improve the relay performance. The simulation results verify that the hybrid relaying communications can achieve 20% performance improvement compared to the conventional relay communications with all active relays.

In this paper, we consider a backscatter-aided hybrid data offloading scheme for a battery-less wireless sensor network. All sensor devices on the edge are coordinated by a hybrid access point (HAP), while also provides power for them via wireless power transfer. Co-located with the HAP, an edge computing server is set up to provide the computation and caching capabilities for the edge devices with insufficient power and computation resources. Each node is allocated a fixed time- slot for data offloading via either the conventional active communications or the passive backscatter communications. Such a hybrid data offloading scheme can flexibly control the trade- off between power consumption and data rate in offloading. We aim to minimize the total energy consumption by optimizing the offloading strategy of each edge device and the HAP's wireless power allocation over different edge devices. We show that the energy minimization problem exhibits a convex reformulation. For practical consideration, we devise a distributed algorithm to solve the problem. The numerical results demonstrate that the distributed algorithm can achieve a near- optimal performance. With a fixed transmit power at the HAP, our proposed hybrid offloading scheme provides a higher offloading throughput compared to the state-of-the-art data offloading schemes.

In recent decades, unmanned aerial vehicles (UAVs, also known as drones) equipped with multiple sensors have been widely utilized in various applications. Nevertheless, constrained by limited battery capacities, the hovering time of UAVs is quite limited, prohibiting them from serving a wide area. To cater with remote sensing applications, people often employ vehicles to transport, launch, and recycle them. The so-called vehicle-drone cooperation (VDC) benefits from both the far driving distance of vehicles and the high mobility of UAVs. Efficient routing and scheduling can greatly reduce time consumption and financial expenses incurred in VDC. However, previous works in vehicle-drone cooperative sensing considered only one drone, thus unable to simultaneously cover multiple targets distributed in an area. Using multiple drones to sense different targets in parallel can significantly promote efficiency and expand service areas. Therefore, we propose a novel problem, referred to as vehicle-assisted multidrone routing and scheduling problem. To tackle the problem, we contribute an efficient algorithm, referred to as vehicle-assisted multi-UAV routing and scheduling algorithm (VURA). In VURA, we maintain and iteratively update a memory containing candidate UAV routes. VURA works by iteratively deriving solutions based on UAV routes picked from the memory. In every iteration, VURA jointly optimizes anchor point selection, path planning, and tour assignment via nested optimization operations. To the best of our knowledge, we are the first to tackle this novel yet challenging problem. Finally, performance evaluation is presented to demonstrate the effectiveness and efficiency of our algorithm when compared with existing solutions.

In this paper, we consider a wireless powered multi-relay network in which a multi-antenna hybrid access point underlaying a cellular system transmits information to distant receivers. Multiple relays capable of energy harvesting are deployed in the network to assist the information transmission. The hybrid access point can wirelessly supply energy to the relays, achieving multi-user gains from signal and energy cooperation. We propose a joint optimization for signal beamforming of the hybrid access point as well as wireless energy harvesting and collaborative beamforming strategies of the relays. The objective is to maximize the network throughput subject to probabilistic interference constraints at the cellular user equipment. We formulate the throughput maximization with both the time-switching and power-splitting schemes, which impose very different couplings between the operating parameters for wireless power and information transfer. Although the optimization problems are inherently non-convex, they share similar structural properties that can be leveraged for an efficient algorithm design. In particular, by exploiting monotonicity in the throughput, we maximize it iteratively via customized polyblock approximation with reduced complexity. The numerical results show that the proposed algorithms can achieve close to optimal performance in terms of the energy efficiency and throughput.

Social media networks have shown rapid growth in the past, and massive social data are generated which can reveal behavior or emotion propensities of users. Numerous social researchers leverage machine learning technology to build social media analytic models which can detect the abnormal behaviors or mental illnesses from the social media data effectively. Although the researchers only public the prediction interfaces of the machine learning models, in general, these interfaces may leak information about the individual data records on which the models were trained. Knowing a certain user's social media record was used to train a model can breach user privacy. In this paper, we present SocInf and focus on the fundamental problem known as membership inference. The key idea of SocInf is to construct a mimic model which has a similar prediction behavior with the public model, and then we can disclose the prediction differences between the training and testing data set by abusing the mimic model. With elaborated analytics on the predictions of the mimic model, SocInf can thus infer whether a given record is in the victim model's training set or not. We empirically evaluate the attack performance of SocInf on machine learning models trained by Xgboost, logistics, and online cloud platform. Using the realistic data, the experiment results show that SocInf can achieve an inference accuracy and precision of 73% and 84%, respectively, in average, and of 83% and 91% at best.

WiFi has become one of the major network access networks due to its simple technical implementation and high‐bandwidth provisioning. In this paper, we studied software defined WiFi networks (SDWN) against traditional WiFi networks to understand the potential benefits, such as the ability of SDWN to effectively hide the handover delay between access points (AP) of the adoption of the SDWN architecture on WiFi networks and identify representative application scenarios where such SDWN approach could bring additional benefits. This study delineated the performance bottlenecks such as the throughput degradation by around 50% compared with the conventional WiFi networks. In addition, our study also shed some insights into performance optimization issues. All of the performance measurements were conducted on a network testbed consisting of a single basic service set (BSS) and an extended service set (ESS) managed by a single SDN controller deployed with various laboratory settings. Our evaluation included the throughput performance under different traffic loads with different number of nodes and packet sizes for both TCP and UDP traffic flows. Handover delays were measured during the roaming phase between different APs against the traditional WiFi networks. Our results have demonstrated the tradeoff between performance and programmability of software defined APs.

Classroom observations have been widely used in education over the past couple of decades to measure effective teaching practice. The traditional observation methods rely on human observers, which are short of scalability and objectivity. In this paper, we implement a kind of automatic behavior measurement system, which utilizes the Microsoft Kinect devices to record the students' performance in classroom. Several Kinect devices are installed under the ceiling of one classroom. The facial images of attended students are collected and recognized. The typical gestures of students (such as sitting, raising hand, standing, sleeping and whispering) are also detected and recorded. A queue-based analysis engine is proposed to distinguish the meaningful learning behaviors from those pointless actions. Experiment results show that this system can be utilized to measure the students' active behaviors in typical learning processes, which will be helpful for the analysis of behavioral engagement in classroom teaching.

Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use. Here, we present a method called iterative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues. icTAIR takes a regulator's previously-identified target gene list and combines it with gene expression data from a context, quantifying that regulator's activity for that context. It then calculates the correlation between each listed target gene's expression and the quantitative score of regulatory activity, removes the uncorrelated genes from the list, and iterates the process until it derives a stable list of refined target genes. To validate and demonstrate icTAIR's power, we use it to refine the MSigDB c3 database of TF, miRNA and unclassified motif target gene lists for breast cancer. We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3,430 patients. We uncover many novel prognostic regulators that were obscured prior to refinement, in particular NFY, and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome. We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts. The icTAIR algorithm can be downloaded from https://github.com/icTAIR.

Higher order sectorization (HOS), which splits macrocells into a larger number of smaller sectors, are receiving significant interest as a cost-effective means of improving network capacity. Potentially, the capacity gain with HOS is proportionally linear to the number of sectors per cell due to spatial reuse, but factors such as non-ideal antenna radiation patterns together with inter-cell interference can significantly reduce this capacity gain. We develop a statistical model to theoretically characterize the performance of HOS deployments in wireless networks using orthogonal frequency division multiple access. Moreover, a fractional frequency reuse scheme is considered, which aids to mitigate inter-cell interference. The model provides a fast and effective tool for studying network performance in terms of user signal quality, site throughput, and outage probability, and it can be used to speed up network planning and optimization. In addition, we consider the impact of user classification methods in the analysis, and propose a new spectrum efficiency-based user classification method that improves resource utilization and allocation fairness. Performance results indicate that the proposed model is accurate, and shows a diminishing performance gain of HOS deployments with the number of sectors. The proposed user classification method improves network performances with respect to the state-of-the-art approaches.

The hybrid architecture of content distribution network (CDN) and peer to peer (P2P) is promising in providing online streaming media services. In this paper, we conducted a comprehensive measurement study on Kankan, one of the leading VoD streaming service providers in China that is based on a hybrid CDN-P2P architecture. Our measurements are multi-fold, as follows. 1) Kankan adopts a loosely-coupled hybrid architecture , in which the user requests are handled by its CDN and P2P network independently. 2) Kankan deploys a small-scale CDN densely in three geographic clusters in China. It adopts specific redirection servers to dispatch the nationwide requests. 3) Kankan adopts a dual-server mechanism to enhance start-up video streaming. It also provides the CDN acceleration in case of inefficient P2P streaming performance. 4) According to our studies on the peer cache lists, the video contents stored in Kankan peers update quite slowly. The average lifetime of cached videos is longer than one week. Our results show that, by utilizing the slow-varying contents cached in peers and deploying various CDN enhancement mechanisms , Kankan provides a large-scale VoD streaming service with a small-scale fixed infrastructure. Insights obtained in this study will be valuable for the development and deployment of future hybrid CDN-P2P VoD streaming systems.

Overlay networks are resilient in transferring data through intermediate nodes. The dynamic stochastic shortest path (DSSP) can be utilized in overlay networks to find the optimized relay paths; however, DSSP depends on the link delay properties/states (i.e., delay distribution and delay average range). Nevertheless, it is difficult to acquire accurate link delay states to approximate the link characteristics due to possible measurement errors. In this paper, we first proposed a convenient DSSP estimation approach to approximate the link stochastic delay considering the tradeoff between the immediate delay and historical delay samples in stochastic overlay networks. Then, in order to evaluate the performance of DSSP, we conducted a comprehensive simulation study to compare DSSP with the shortest path computed using classic routing algorithms with the average delay values and delay errors due to the variation of delay distributions and updating intervals. The experiment results show that the proposed DSSP delay estimation method is more reliable and outperforms the conventional shortest path routing with the delay estimation using average delay and delay errors. In addition, we also proposed a refined heuristic K-shortest stochastic path routing algorithm using the proposed delay estimation method. In two typical overlay relay network scenarios, the simulation results show that the proposed stochastic routing algorithm outperforms the classic routing algorithms in reducing the average delay by 20% – 40% and the packet loss for nearly 50%.

Recently, the variable channel-width (VW) scheme was proposed to improve the performance of WLANs. Cooperative channel allocation has been studied in some existing literature under the assumption that the traffic demands of cooperative access points (APs) are constant. In fact, the traffic demands may vary when the corresponding stations change their AP association decisions. Hence, this work jointly considers the channel allocation and AP association, aims to maximize the system performance in terms of throughput and fairness. The problem is formulated as a constrained Integer Non-Linear Programming (INLP) problem, which is NP-hard. Two penalty functions are introduced to relax the constraints, and a discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the problem. The simulation results show that our algorithm can improve the performance by about 20% compared to the fixed traffic scheme.

Due to the limited resources and dynamically varying nature of wireless links, guaranteeing high quality demands for a large number of heterogeneous users is very challenging in video streaming over wireless networks. In this paper, we introduce a layered multiple description coding with an embedded forward error correction scheme (LMDC-FEC). The combination of layered MDC and FEC aims at coping with not only the diverse bandwidth and unreliability of wireless links but also the heterogeneous user devices. We further propose a joint replication density and rate allocation (RD-RA) optimization problem in the context of video on-demand systems (VoDs) over wireless mesh networks (WMNs), and employ a genetic algorithms based approach to solve the optimization problem. Our objective is to elaborately distribute proper replication density for each video segment and allocate optimal bit rate to each layer of each segment in order to gain high user-perceived quality (UPQ) with small consumption of storage resource. By this method, both optimal replication densities and rate allocations are found in accordance with the access rate of segments, the diverse loss characteristics of descriptions in each segment, and the rate-distortion relationship of segments, so as to thoroughly optimize UPQ and storage resource consumption. Simulation results demonstrate that our proposed method significantly enhances the streaming performance of VoDs over WMNs.

Channel allocation is a crucial concern in variable-width wireless local area networks. This work aims to obtain the stable and fair nonoverlapped variable-width channel allocation for selfish access points (APs). In the scenario of single collision domain, the channel allocation problem reduces to a channel-width allocation problem, which can be formulated as a noncooperative game. The Nash equilibrium (NE) of the game corresponds to a desired channel-width allocation. A distributed algorithm is developed to achieve the NE channel-width allocation that globally maximizes the network utility. A punishment-based cooperation self-enforcement mechanism is further proposed to ensure that the APs obey the proposed scheme. In the scenario of multiple collision domains, the channel allocation problem is formulated as a constrained game. Penalty functions are introduced to relax the constraints and the game is converted into a generalized ordinal potential game. Based on the best response and randomized escape, a distributed iterative algorithm is designed to achieve a desired NE channel allocation. Finally, computer simulations are conducted to validate the effectiveness and practicality of the proposed schemes.

Massive multi-player online games (MMOGs) have been attracting thousands of millions of participants on the Internet in the past decades. The newly emerging smart phones and game consoles together with PCs have enlarged the player base to an even larger scale. The increasing game traffic may generate significant real-time traffic across different ISP networks. In this paper, we conducted a measurement study of the traffic locality property of a popular online game, Call-of-Duty (CoD), which is a hybrid peer-to-peer (P2P) client/server massive multi-player online first person shooter (MMOFPS) game in the play station network (PSN) over the Internet. To facility our measurement, we designed and implemented a peer crawler over the PSN. Our instrumented crawler applies the principle of the ARP poisoning attack in our justified scenario so that our crawler is able to penetrate the PSN to harvest player's information successfully. We analyzed the measurement results for finer granularity at the autonomous system (AS) level compared with previous measurement studies. Our results show that the sessions in this CoD game are constructed with players' locality in mind. Nevertheless, optimized locality-aware game sessions are yet to be found. Insights obtained from this study may be valuable for the development and deployment of future P2P online gaming systems.

Many Internet applications are designed and deployed as overlay applications. The potential mismatch between the application overlay and the network underlay has driven the demand for designing locality-aware applications in order to reduce emerging huge inter-domain traffic load. In this paper, we study a fundamental problem of measuring AS paths between two Internet hosts using three methods including traceroute-based direct measurement, BGP-based indirect inference and graph-based shortest AS path. We conducted a measurement study of AS paths to evaluated the accuracy and complexity of the above three AS path measurement methods and the corresponding tools. Inspired from our experiment results, we proposed a hybrid progressive method to combine the traceroute probes and the BGP tables to enhance the IP-to-AS mapping process to achieve a more accurate estimation of AS paths. We also found that the missing IP addresses in the traceroute measurement decrease the accuracy of the traceroute-based method; however, this performance degradation can be compensated using BGP tables. Our study leads a more accurate IP-to-AS mapping tool and it can provide a solid support for locality-aware Internet applications.

KAD is the largest DHT system with several million simultaneous users. The dynamics of peer participation which is called churn affects the performance of lookup operations in P2P systems, since some individual peers in the routing tables might be missing or stale. In this paper, we performed a simple way to improve lookup performance in KAD, by taking highly available contacts as lookup entries instead of stale ones. We track highly available peers in KAD by a special designed crawler. When a stale contact is encountered in the lookup process, the closest XOR-distance highly available peer of the target will be found to replace the stale contact. The measurement study, compared with the normal lookup process, shows that it is much effective.

In cognitive radio networks (CRNs), cooperative spectrum sensing (CSS) is usually performed periodically due to the uncertain activity of primary users (PUs). Considering the overhead in performing CSS, a selfish secondary user (SU) may not always participate in CSS. Instead, it elaborately selects a frequency (or number of times) for CSS participation to maximize its interest. A fusion center then schedules it to conduct CSS in appropriate periods. This paper investigates the interactive decision on the CSS participation frequency under sensing performance and quality of service (QoS) requirements. The problem is formulated as a noncooperative game, where Nash Equilibrium (NE) corresponds to the desired frequency selection outcome. Since the strategy sets of SUs are coupled, obtaining directly the NE requires explicit coordination among SUs, which is unrealistic in practice. Alternatively, we decompose the game into a lower-level uncoupled game and a higher-level optimization problem. A distributed hierarchical iterative algorithm (DHIA) is then proposed to obtain the desired frequency selection outcome without requiring explicit coordination. Furthermore, the uncertain sensing performance of SUs and the fairness issue are also considered. Finally, numerical results validate the effectiveness of the proposed scheme.

In this paper, we propose a cross-layer optimization approach for replication of video streaming over multihop wireless network by jointly taking into account application, network, MAC, and physical layers. Specifically, we design a middleware replication strategy layer with a stack profile, in which parameters across the hops and all considered layers are exchanged for optimal video replication. The simulation results demonstrate that our method improves the replication strategy performance in terms of user-perceived video quality. In turn, it satisfies the heterogeneous receive bandwidth constraint of users for efficiently using the network bandwidth compared to other optimal replication strategies without cross-layer approach.

In this paper, we consider the replication strategy for the applications of video streaming in wireless mesh networks (WMNs). In particular, we propose a closed-form of optimal replication densities for a set of frames of a video streaming based on exploiting not only the skewed access probability of each frame but also the skewed loss probability and skewed encoding rate-distortion information. The simulation results demonstrate that our method improves the replication performance in terms of user-perceived quality (UPQ) including: 1) minimum average maximum reconstructed distortion for high peak signal-to-noise ratio (PSNR), 2) small reconstructed distortion fluctuation among frames for smooth playback, and 3) reasonable average maximum transmission distance for continuous playback. Furthermore, the proposed strategy consumes small storage capacity requirement for saving the resource of network compared to other existing optimal replication strategies.

This paper investigates downlink frequency resource coordination for LTE network that comprises of macrocell and femtocell. Due to the requirement of high data rate for indoor coverage, femtocells have been considered as a potential solution recently. However, owing to their random and uncoordinated deployment, cross-tier interference exists and affects the system throughput seriously. A hybrid resource allocation (HRA) technique based on measurement reports both from macro users (MUEs) and femto users (FUEs) is proposed. Simulation results show that proposed scheme can enhance overall throughput and mitigate major interferences in different scenarios.

Spectrum sensing is an essential functionality of cognitive radio networks (CRN). Among existing spectrum sensing methods, cooperative spectrum sensing is the best one which can achieve superior sensing performance by introducing spatial diversity of sensing data sources. Such cooperation also introduces additional information exchanging which leads to extra power consumption and reporting delay. In this paper, the optimal sensing performance problem is formulated as a nonlinear binary integer programming problem to find suitable cooperative nodes minimizing the average detection Bayesian risk. The binary particle swarm optimization (BPSO) algorithm is adopted to obtain suboptimal solutions to cooperative nodes. Computer simulations show that the proposed scheme can significantly improve the sensing performance compared with the case that all neighboring nodes participate in sensing without discrimination under different scenarios.

This paper investigates non-overlapping variable-width channel allocation for cooperative access points (APs) in multiple collision domains with the goal of maximizing the total capacity of wireless local network (WLAN). Due to the complexity of finding an optimal allocation, this paper considers it from a game-theoretic perspective. First, the problem of variable-width channel allocation is formulated as an identical interest game and the existence of pure Nash Equilibrium (NE) is investigated. Then a decentralized learning-based total capacity maximization algorithm (LTCMA) is designed for APs to achieve an optimal allocation. To analyze the fairness property of the optimal allocation, a game-theoretic fairness analysis model is developed. With this model, this paper shows that the fairness is usually acceptable for a WLAN in which every client is rational and free to associate itself with any APs. Finally, the numerical results verify the effectiveness of LTCMA and the fairness of the optimal allocation.

The fixed channelization structure used by IEEE 802.11-based WLANs constrains the total capacity and leads to unfairness. The concept of variable-width channels is recently proposed to overcome these drawbacks. To investigate the problem of the non-overlapping variable-width channel allocation for selfish access points (APs) in a WLAN, we model it as a non- cooperative game, we aim to investigate two fundamental issues on it in this paper: 1) Are there some fair and system-optimal Nash equilibrium (NE) allocations? 2) How to achieve one of these desirable allocations if they exist? At first, the existence of fair and system-optimal Nash equilibria in this game is proved. Then, a simple protocol to achieve one of these desirable NE allocations is proposed. Considering the implementation issues, a punishment-based method and a transfer-based self-enforcing truth-telling method are proposed for single-stage and multistage game scenarios respectively. The numerical results show the effectiveness of our approaches.

Spectrum sensing is important for cognitive radios to utilize the idle spectrum opportunities, and recently cooperation schemes have been introduced to enhance spectrum sensing in specific areas. However, when a mobile cognitive node roams among heterogenous wireless network, it will be difficult to catch the changes of primary user's behavior, or to setup the cooperation relationship with local network nodes in a short time. In this paper, an self-learning spectrum sensing framework is proposed, which can enable the single mobile cognitive node to work in unknown wireless environment. When the wireless environment changes, the main sensing parameters (such as decision threshold, sampling frequency) could be adapted to optimum in the self- earning process. One adaptive algorithm is proposed to find the optimal decision threshold in energy detection sensing method. Simulation results show that, the proposed scheme could converge to optimal sensing parameters in spatial and temporal varying environment.

Cooperation can improve the performance of spectrum sensing. However, the sensing overhead is generally increasing with the number of cooperating users as more data needs to be reported to the fusion center. Most existing works assume a general time frame structure in which spectrum observing and sensing results reporting are conducted sequentially. We argue that this frame structure is inefficient, since the time consumed by reporting contributes little to the performance of spectrum sensing. In this paper, we propose a pipelined spectrum sensing framework, in which spectrum observing is conducted concurrently with results reporting in a pipelined way. By making use of the reporting time for sensing, the new framework provides a much wider observing window for spectrum measurement, which results in a performance improvement of spectrum sensing. Besides, we also present a multi-threaded sequential probability ratio test method (MTSPRT) which is very suitable for the pipelined framework as the data fusion technique. The MTSPRT method can improve the sensing speed significantly. Numerical results indicate that our pipelined sensing scheme incorporating with MTSPRT shows a better performance than the cooperative sensing based on the general frame structure.

Cognitive radio technology is used to improve spectrum efficiency by having the cognitive radios act as secondary users to access primary frequency bands when they are not currently being used. In general conditions, cognitive secondary users are mobile nodes powered by battery and consuming power is one of the most important problem that facing cognitive networks; therefore, the power consumption is considered as a main constraint. In this paper, we study the performance of cognitive radio networks considering the sensing parameters as well as power constraint. The power constraint is integrated into the objective function named power efficiency which is a combination of the main system parameters of the cognitive network. We prove the existence of optimal combination of parameters such that the power efficiency is maximized. Then we reformulate the objective function to incorporate the throughput. According to different constraints or degree of significance, we may put proper weight to each term so that we could obtain more preferable combination of parameters. Computer simulations have given the optimal solution curve for different weights. We can draw the conclusion that if we put more emphasis on power efficiency, the transmit power is a more critical parameter, however if throughput is more important, the effect of sensing time is significant.

Cooperative transmission technique is now widely considered as a promising approach to combat fading and achieve energy efficiency in wireless networks. In this paper we focus on the routing problem in energy-constrained wireless sensor networks (WSNs), of which a cooperative MISO-based routing strategy is adopted. We first analyze the physical layer energy consumption model of cooperative transmission in the scenarios of one hop and hop-to-hop for energy-efficient routing in order to prolong the network lifetime. Based on this analysis, we disclose how the energy-efficient network routing problem is tightly related to the inter-cluster MISO node and hop-to-hop relay node selection. As we noticed, the problem of energy-efficient cooperative routing is NP-hard innately which is difficult to implement in a totally distributive approach. Due to these analysis, a feasible algorithm with minimum cost is thus proposed. In the simulation part, we prove that our protocol can prolong the network lifetime tremendously when choose appropriate transmission parameters. Moreover, as an example, we simulate a typical network scenario which indicates our protocol is more energy efficient when comparing with vMIMO scheme in our previous work.

In cognitive radio networks, secondary users (SUs) opportunistically exploit the spectrum unutilized by primary users (PUs). In this paper, we study the secondary communication where secondary transmitters and receivers have different available spectrum. Considering the spectrum diversity and the space distance between different PUs, we introduce cognitive relay node into the secondary communication and propose a novel Cooperative Relay Scheme (CRS) to increase the SINR at secondary receivers. A novel Opportunistic Sharing Scheme (OSS) is also proposed for the secondary transmitters to share the spectrum of relay nodes. We model it with a non-cooperative game, and study the performance of competition of SUs. The Nash equilibrium and Pareto efficiency of this game is presented. Simulations show that CRS can increase SINR at secondary receivers under proper configurations.

In cognitive radio networks, power control is necessary to not only decrease the interference among the secondary users (SUs), but also avoid negative impact to the primary users (PUs). Prevalent research works on power control are mainly focus on maximizing SINR as the QoS requirement of SUs under the interference power constraint for PUs. We note that besides achieving a high SINR to guarantee reliable data transmissions, SUs also require to support heterogenous services with different transmission rates. In order to provide flexible transmission rates to each SU, efficient use of networks radio resource requires transmission rate control in addition to transmit power control. In this paper, we consider the problem of joint power and rate control for SUs in cognitive radio network by using non-cooperative game theory. We study how to jointly allocate optimal transmit power and transmission rate given certain QoS requirement of SUs. We analysis of existence, uniqueness and Pareto efficiency of Nash equilibrium for our game. The performance of our proposed joint power and rate control algorithm is investigated by numeral results.

This paper presents a novel backoff algorithm in CSMA/CA-based Medium Access Control (MAC) protocols for clustered sensor networks. We first show that every node should have the same value of Contention Window (CW) in a cluster by formulating resource allocation as a utility maximization optimal problem, then assume all nodes have the same CW and gain the relation between the optimal value of CW and the number of nodes by maximizing the total network utility with constrains of minimizing collision probability. The result is a new retransmission algorithm that uses an optimal shared CW that is easy to implement and results in fewer collisions than binary exponential backoff algorithm. The proposed scheme can decrease delay and improve throughput, moreover, it is also energy-efficiency for clustered sensor networks, simulation results validate our conclusion.

Multicast support is critical and a desirable feature of wireless mesh networks (WMNs). In this paper, we propose an approach to joint optimizing rate allocation of flows and power consumption of links for forwarding data flows for overlay multicast in WMNs. We develop a distributed algorithm based on pricing scheme by using dual decomposition technique. The "price" is associated with each individual network link, which reflects the traffic load on this link. The receiver in turn collects the prices of all links on its multicast path and calculates the overall network price. Then, it adjusts the streaming rate such that its "net benefit," the utility minus the network cost, is maximized. The validity and effectiveness of our approach are demonstrated in simulations.

Cooperative MIMO transmission technique is considered as one of the effective solutions to reduce the energy consumption in wireless sensor networks. However, the existing cooperative MIMO based protocols only focused on how to reduce the energy consumption, but for some applications such as audio/video surveillance sensor network, a large amount of gathered data (formulated as network utility function) and long network lifetime are both required. In those cases, the two performance parameters should be considered and jointly optimized during the protocol design. In this paper, we first model and analyze the energy consumption and channel capacity of Multi-hop cooperative MIMO transmission. Then, we propose a joint network lifetime and utility optimization model based on NUM (network utility maximization) approach. Finally, we use the dual decomposition technique to solve the primal optimization problem and get a distributed algorithm. Simulation results show that, by using our distributed algorithm, the lifetime and utility of cooperative MIMO sensor network can converge to Pareto optimal trade-off.

Wireless sensor networks have limited energy resource and therefore energy-efficient protocols are required to minimize the energy consumption. The virtual MIMO technique is considered as one of the new solutions to solve this problem. In this paper, in order to maximize the network lifetime, we propose a virtual MIMO transmission scheme coupled with multi-hop transmission. In our cross-layer design, the transmission rate, the number of clusters and the number of virtual antenna nodes are jointly optimized. Unlike existing work that is mostly based on Alamouti scheme, the proposed transmission scheme does not require transmitter-side cooperation (joint STBC encoding/decoding), making it more suitable for application in real wireless sensor networks. Simulation results show significant energy savings and lifetime maximization of the network.

Next Generation wireless networks are evolving towards all-data system which are expected to support a variety of application services with diverse transmission rates. Meanwhile, since most of the mobile terminals in wireless networks are battery-powered, to use energy efficiently, each terminal needs to transmit just enough power to achieve the desired transmission rate without causing excessive interference in the network. In this paper, a game-theoretic framework is used to study the joint power and rate control problem on the energy efficiency of wireless data network. A energy-efficient non-cooperative joint power and rate control game is thus introduced in which each user seeks to choose its possible transmit power and transmission rate in order to maximize its own utility while satisfying its target SINR as quality-of service (QoS) requirement. The utility function here we adopt is especially suitable for energy-constrained networks. We introduce pricing of transmit power into the utility function which not only improves the overall system performance, but also obtains Pareto Improvement when compared to the game with no pricing. The existence, uniqueness, best-response strategies and Pareto efficiency of Nash Equilibrium for the proposed game are proved. Based on these analysis, we present a distributive joint power and rate control algorithm. In the simulation part, we investigate the best pricing factor and compare our proposed algorithm with alternative algorithms developed by using game theory.

Wireless sensor networks (WSNs) are energy- constrained in nature, moreover, sensor nodes play the dual role of data gathering and data relaying. In this paper, we consider how to allocate the power of sensor nodes for forwarding traffic of other nodes. After forwarding power ratios of nodes are decided, we consider pricing as a mean to stimulate cooperation between a node and other nodes along its routing path to a sink node. By formulating the problem of data sensing and transport in WSNs as a network utility maximization (NUM) problem, we propose an iterative price and power adaption algorithm by using dual decomposition techniques. Numerical results show that by using our price mechanism, we can improve system performance while reducing power consumption.

In cognitive radio networks, nodes can work on different frequency bands. Existing routing proposals help nodes select frequency bands without considering the effect of band switching and intra-band backoff. In this paper, We propose a joint interaction between on-demand routing and spectrum scheduling. A node analytical model is proposed to describe the scheduling-based channel assignment progress, which relief the inter-flow interference and frequent switching delay. We also use an on-demand interaction to derive a cumulative delay based routing protocol. Simulation results show that, comparing to other approaches, our protocol provides better adaptability to the multi- flow environment and derives paths with much lower cumulative delay.

Wireless sensor networks (WSNs) are energy- constrained in nature, in this paper, we formulate the problem of data transport in sensor networks as a network utility maximization (NUM) problem, but we argue that each source utility not only depends on its source rate, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of source rates and consumed energy. Differentiating from the classical NUM framework which usually takes the consumed energy as constraints. Our utility model regards consumed energy as one of the components of measure of the utility values, which indicates the tradeoff of source rates and consumed energy, it is a more accurate utility model for abstracting the energy characteristics for data gathering and transmission in WSNs. Due to the coupled energy utility, our optimization problem is not separable. Despite the difficulty, we present a systematic approach to decouple our NUM problem with coupled utilities by introducing into the slack variables and using dual decomposition techniques, and obtain a distributed algorithm for solving our problem. The proposed algorithm can converge to the Pareto optimal tradeoff between rates and energy for all users.

One of the wireless mesh network's important features is each node can support more than one transmission rate. However, few previous literatures on the broadcast tree construction take this into account. Some researchers proposed to reduce the network wide broadcast transmission latency by taking advantage of the multi-rate nature. However, it suffers from a long construction time as analyzed in this paper, which brings in a long start-up delay. This paper proposes a fast broadcast tree construction algorithm (called rate first) by exploiting the relationship between the transmission rate and its range. Simulation results show that it does not only keep the broadcast transmission latency at the same level with the state- of-the-art work, but also accomplishes in a significantly short time.

One of the main objectives in broadcast is to minimize the overall latency, in which the minimal connected dominating set (MCDS) has been shown as an effective technique in single-rate wireless mesh networks. However, this can not be directly applied in a multi-rate wireless mesh network In this paper we present a formal minimized latency broadcast formulation for multi-rate wireless mesh networks and we propose a novel distributed Rate First broadcast algorithm. Extensive results demonstrate that the proposed algorithm can achieve up to 50% reduction in latency comparing to the existing distributed algorithms.

Evaluating position uncertainties is a fundamental problem of wireless sensor network localization. A constraint set, including both positive and negative constraints, is constructed to bound sensor position. By projecting the feasible region of this constraint set onto a 2D plane, the feasible scope of sensor position is computed to evaluate node position uncertainty. The projection result, called feasible geographic region (FGR), is approximated by its inner and outer polygon. The polygon approximation will converge to the actual FGR if we incrementally add more polygon vertices. A distributed algorithm is proposed to compute FGR. Finally, we study the impact of node position uncertainty upon a typical network application, target event detection. The feasible scope of target event position is computed even though the sensor position is not certain.

Wireless sensor networks (WSNs) are energy- constrained in nature, in this paper, we formulate the problem of data transport in sensor networks as a network utility maximization (NUM) problem, but we argue that each source utility not only depends on its source rate, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of source rates and consumed energy. Differentiating from the classical NUM framework which usually takes the consumed energy as constraints. Our utility model regards consumed energy as one of the components of measure of the utility values, which indicates the tradeoff of source rates and consumed energy, it is a more accurate utility model for abstracting the energy characteristics for data gathering and transmission in WSNs. Due to the coupled energy utility, our optimization problem is not separable. Despite the difficulty, we present a systematic approach to decouple our NUM problem with coupled utilities by introducing into the slack variables and using dual decomposition techniques, and obtain a distributed algorithm for solving this problem. The proposed algorithm can converge to the Pareto optimal tradeoff between rates and energy for all users.

Most traditional localization approaches, providing single position estimate for each sensor node, cannot evaluate localization accuracy and position uncertainty. In this study, we construct a constraint set using proximity information ,and then, compute feasible geographic region (FGR) for each sensor node by projecting high dimensional feasible region of the constraint set to a specific 2D plane. The FGR approximated by simple and expressive polygon could bound exact sensor node position and clearly reflect position uncertainty. A distributed algorithm is also proposed to compute FGR effectively. In addition, considerable improvements of localization accuracy can be made if we use polygon with more number of vertices to approximate FGR, or utilize non-convex range constraints to locate infeasible holes within the polygon.

In this paper, we focus on the intra-cluster information exchange problem and propose some novel solutions. We only concern about how to exchange information inside cluster of sensor networks efficiently and do not consider cluster forming process and MAC layer scheme. Firstly, the intra-cluster information exchange problem is introduced. And secondly, the circular and random cluster models are presented, based on which some algorithms are proposed, such as routing, flooding, relaying and network coding. After theoretical analysis and packet-level simulation comparison, we find that network coding algorithm allows to realize significant energy and time savings.

As Wireless Sensor Networks find more and more application in daily life, energy efficiency of these networks, which require unattended operations, is of paramount concern. In this paper, we propose an energy-efficient virtual MIMO transmission scheme based on V-BLAST technique. The proposed scheme does not require transmitter-side cooperation (joint STBC encoding), which is more suitable for the energy-constrained wireless sensor networks compared with the Alamouti-based virtual MIMO scheme. Simulation results show significant energy savings and network lifetime extension can be achieved by using our scheme.

Node localization is very important for wireless sensor networks. Traditional localization methods usually cannot perform well in practical sensor networks which are affected by several well-known factors, such as low node density, anisotropic deployment terrain, imprecise GPS node position and noisy range measurements. In this paper, we propose a robust region based localization approach which is able to naturally address these factors. An iterative and distributed implementation based on clustering is also given to provide scalability and energy efficiency.

Traditional backoff algorithms in WLAN adopt contention window scheme for collision resolution. Collided stations are redistributed in extended contention window ranges to avoid further collisions. However, due to the existence of intersection among these ranges, collision can still occur. This paper proposes collision sequential resolution (CSR) algorithm to address the problem, which is compatible with IEEE 802.11. CSR allocates discrete contention windows for active stations; therefore the stations can be deployed in a series of separated distribution windows sequentially to eliminate collisions. The simulation results demonstrate that CSR algorithm provides significant comprehensive improvement to IEEE 802.11 protocol.

Delay requirement becomes a key problem in sensor networks in the time-critical applications. This paper proposes a real-time routing protocol in sensor networks. A novel concept of Effective Transmission (ET) is put forward to ensure each forwarding node is farther from the source node and nearer to the destination node with respect to its sender. The end-to-end delay requirement is separated into the sum of point-to-point Constrained Equivalent Delay (CED). Each intermediate node can independently decide its next forwarding node according to each CED, so it will greatly simplify the route discovery process. The simulation results show the routing protocol is effective on the performance of energy consumption and end-to-end delay compared with some other routing protocols.

A fault-tolerant conference key distribution scheme based on mechanism of identity-based cryptography and (t,n) threshold secret sharing is proposed in this paper This scheme is much different to traditional ones, its secret shadows are not brought from the sponsor of conference, but from each server's private key signature. By getting together these n secret shadows, the sponsor can construct polynomial. Any of conferees invited by the sponsor can request these secret shadows from t of these n servers, and then restitute conference key by them. In all courses of conference key distribution and reconstruction, every member's identity can be easily validated, so it can be prevented from all kinds of cheat

Learning resource standards are very important in a courseware authoring system. Some learning resource standards, like learning object metadata (LOM), content packaging (CP) and question and test interoperability (QTI) standards, provide a very effective way to process learning resource content. LOM provides a circumspect element set for describing content. CP defines a graceful structure for packaging content and makes it easy to analyze and interchange content packages. QTI standards describes a basic structure for the representation of question and test data. LOM, CP and QTI can be well applied in courseware authoring. Based on them, a standardized visual Web-based courseware authoring system named SmartEye system is developed to help courseware developers developing high-quality Web-based courseware. This paper instructs the application of LOM, CP and QTl in content authoring and deployment, and also instructs the design and implementation of the SmartEye system.

Recently, lots of RTS/CTS-similar MAC protocols, based on multi-channel and reservation for mobile wireless ad hoc networks, have been proposed, which can improve the performance of the network to some extent. However, the throughput improvement is still limited because there is only one wireless transceiver per node. In this paper, we propose a multi-transceiver multiple access (MTMA) protocol for ad hoc networks in which each node has multiple sub-nodes equipped with independent wireless transceivers and the whole wireless channel is divided into multiple sub-channels. Every sub-node dynamically reserves an idle traffic channel by RTS/CTS dialogue on the common channel that enables a node to perform parallel communications with other nodes. Fast packet switching is enabled between sub-nodes within the same node. The MTMA protocol outperforms the single-transceiver RTS/CTS protocols with single channel or multi-channel in the case of the same total wireless bandwidth.

The fault restoration technique based on n:m scheme can improve system availability with small cost increases for WDM networks, where it is possible that n faults occur synchronously in a protection domain or a protection entity, but it has rarely been implemented or standardized. In this paper, an analytical model for mean restoration time of restoration mechanism based on n:m scheme is developed. As a general model, it aims at the WDM networks with the restoration paths created in advance of fault occurrence without wavelength reserved. In particular, the proposed protocol for processing faults has been improved to make the restoration based on n:m scheme as fast as that based on 1:1 or n:1 scheme.

We propose an identity-based proxy ring signature scheme from bilinear pairings which combines the advantages of proxy signature and of ring signature. Furthermore, our scheme can prevent the original signer form generating the proxy ring signature, thus the profits of the proxy signer are guaranteed. We introduce bilinear pairings to minimize the computation overhead and to improve the related performance of our scheme. As compared with Zhang's scheme, our scheme is a computational efficiency improvement for signature verification because the computational cost of bilinear pairings required is reduced form O(n) to O(1). In addition, the proxy ring signature presented in This work is signer ambiguous, nonforgeable, verifiable, nonrepudiable and identifiable.

We propose a new micropayment scheme in which possible overspending is radically reduced by the adoption of probabilistic polling in a transaction. On the other hand, the losses caused by a dishonest overspending consumer must be shared by banks and merchants, furthermore, an iterative interactive payment protocol is performed which protects the profits of both consumers and merchants. Compared with PayWord, the fairness of our scheme is much higher and restricted anonymity of a customer is provided.

Priority queuing is often used as a way to provide differential services for users with different delay sensitivities. An auction-based admission control and pricing mechanism is proposed for priority services, where higher priority services are allocated to the users who are more sensitive to delay, and each user pays a congestion fee for the external effect caused by their participation. The mechanism is proved to be strategy-proof and efficient.