Lithium-ion batteries (LIBs) are widely used in industrial applications for energy storage and power supply. The failure of LIBs has attracted significant attention since severe accidents caused by thermal runaway. Electrolyte leakage in lithium-ion batteries (LIBs) represents a significant hidden risk to LIB systems and serves as an early indicator of potential thermal runaway. Current electrolyte leakage detection technologies often lack the sensitivity, cost-effectiveness, and longevity required for modern battery safety systems. To address this challenge, we investigate the phase transitions of leaked electrolytes and changes in particle size of ambient aerosols, and propose a pack-level optical electrolyte leakage sensor for early safety warning of LIBs failures in Industrial Internet of Things (IIoT) applications. Experiments demonstrate that the particle size of ambient aerosol increases rapidly due to phase change condensation of the leaked electrolyte vapor. Correspondingly, the light scattering efficiency of ambient aerosol increases, providing measurable indicators for electrolyte leakage monitoring. Our optical sensor can promptly detect leakage in the overcharging test, slightly surpassing the PPB gas sensor in response time while reducing cost to 1/10 due to mature commercial optical components and increasing lifespan 3 times according to the lifespan of core elements. Utilizing readily available commercial components, this method offers a high-sensitivity, cost-effective, and durable solution for improving safety in LIB systems.

Energy constraints significantly limit the long-term operation of underwater wireless sensor networks (UWSNs) due to their battery-powered sensor nodes (SNs). In this paper, we investigate an autonomous underwater vehicle (AUV)assisted UWSN where the AUV simultaneously collects data and recharges multiple independently located SNs. We employ the value of information (VoI) to evaluate the importance of the sensing data. We propose a novel Lyapunov-guided deep reinforcement learning (LDRL) algorithm to maximize the long-term average VoI while guaranteeing the battery energy constraints of SNs. We first employ a Lyapunov optimization to decompose the multi-stage stochastic VoI maximization problem into a series of single-stage deterministic subproblems. The Lyapunov drift-pluspenalty function is designed to deal with the long-term energy queue equilibrium. Then, we employ a deep neural network (DNN) to optimize the AUV's path and integrate a sequential convex approximation (SCA) optimization module for optimal charging time allocation. Experimental results demonstrate that our algorithm significantly enhances the long-term average VoI while ensuring the SNs' battery energy constraints.

Unmanned surface vessels (USVs) are increasingly utilized for maritime sensing and data collection. With advancements in Sixth-Generation (6G) mobile network technologies, such as UAV-assisted communication, it is now possible to utilize UAVs to provide auxiliary data collection services for USVs in offshore areas. Traditionally, UAV-assisted communication involves UAVs serving as dedicated, long-term relays for USVs. However, within the 6G paradigm, UAVs operate as public communication devices, offering only temporary relay services to USVs. This paper focuses on developing opportunistic routing strategies for this scenario. For ordinary data with low time sensitivity, the objective is to maximize the benefits of opportunistic communication, which is achieved using the proposed Utilization Priority (UP) strategy. For critical data with high time sensitivity, the goal is to ensure the fastest possible delivery, addressed through the proposed Completion Priority (CP) strategy. Simulation results demonstrate that employing UAVs as relays enhances network throughput and reduces latency. The UP strategy generally achieves higher throughput in most scenarios, while the CP strategy is more effective in minimizing network latency.

Utilizing an autonomous underwater vehicle (AUV) for data collection in underwater wireless sensor networks (UWSNs) is a promising approach. However, since underwater sensor nodes are typically battery-powered and difficult to replace, effective energy management is crucial for extending the network lifetime of UWSNs. Additionally, the limited energy of the AUV presents further challenges. To address these issues, this paper proposes an energy-aware AUV-assisted data collection scheme based on dynamic clustering and cluster head selection (DCCHS) to maximize network lifetime. Specifically, we utilize the energy center to define the cluster center and employ a bottom-up hierarchical clustering approach to address the node dynamic clustering problem under the AUV movement distance constraint. Subsequently, we introduce a cluster head (CH) selection algorithm based on iterative optimization, and adds auxiliary CHs near the AUV path to reduce the energy consumption of CHs. Simulation results demonstrate that the proposed DCCHS scheme significantly extends the network lifetime compared to existing schemes, particularly in scenarios with dense node deployment.

The rapid proliferation of Internet of Things (IoT) devices has increased the variety of devices and data traffic, making data management and analysis more complex. This complexity has raised the demand for efficient device identification methods to ensure the smooth operation of the network. Conventional identification methods rely on Machine Learning (ML) and Deep Learning (DL), which either suffer from unstable feature engineering or rely on large labeled datasets with confined representation. To overcome these shortcomings, generic hybrid representations of raw traffic are essential for precise device identification. Additionally, existing work mainly investigated device identification in clouds, incurring high network latency and computation costs. A few studies have identified IoT devices in edge, but such methods used simple neural networks, resulting in incomplete representation and redundant operations. Comprehensive representations typically require complex models, but the limited resources at the edge are insufficient to execute these models. Therefore, this paper proposes a lightweight hybrid device identification (LHDI) approach, which achieves efficient device identification in resource-constrained edge nodes. First, we adopt the unsupervised pre-training to enhance the characterization of network packets. Second, we devise LHDI by integrating bidirectional long short-term memory (Bi-LSTM) and Transformerbased blocks in a parallel configuration. Third, a pruning framework is introduced to automatically reduce Transformer parameters using structured sparsity methods without retraining. By reducing redundant neural network parameters, the proposed lightweight model facilitates effective device identification in edge, without losing representation capabilities. Experimental results demonstrate that our methods deliver high accuracy with low cost compared to others.

Volumetric videos offer an incredibly immersive viewing experience but encounters challenges in maintaining quality of experience (QoE) due to its ultra-high bandwidth requirements. One significant challenge stems from user’s spatial interactions, potentially leading to discrepancies between transmission bitrates and the actual quality of rendered viewports. In this study, we conduct comprehensive measurement experiments to investigate the impact of six degrees of freedom information on received video quality. Our results indicate that the correlation between spatial quality and transmission bitrates is influenced by the user’s viewing distance, exhibiting variability among users. To address this, we propose a spatial quality oriented rate control system, namely sparkle, that aims to satisfy spatial quality requirements while maximizing long-term QoE for volumetric video streaming services. Leveraging richer user interaction information, we devise a tailored learning-based algorithm to enhance long-term QoE. To address the complexity brought by richer state input and precise allocation, we integrate pre-constraints derived from three-dimensional displays to intervene action selection, efficiently reducing the action space and speeding up convergence. Extensive experimental results illustrate that sparkle significantly enhances the averaged QoE by up to 29% under practical network and user tracking scenarios.

Recently, the machine unlearning has emerged as a popular method for efficiently erasing the impact of personal data in machine learning (ML) models upon the data owner's removal request. However, few studies take into consideration the security concerns that may exist in the unlearning process. In this article, we propose the first unlearning attack dubbed unlearning attack for regression learning (UnAR) to deliberately influence the predictive behavior of the target sample against regression learning models. The central concept of UnAR revolves around misleading the regression model into erasing the information associated with the influential samples for the target sample. Observing that the influential samples for target data are generally located far away from the regression plane, we thus propose two novel methods, known as influential sample selection (ISS) and influential sample unlearning (ISU), to identify and subsequently eliminate the lineage of the influential samples. By doing so, we can substantially introduce bias into the prediction pertaining to the target sample, yielding the deliberate manipulation for the user adversely. We extensively evaluate UnAR on five public datasets, and the experimental results indicate our attacks can achieve prediction deviations over 35% by unlearning only 0.5% data as the influential samples.

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://github.com/CGCL-codes/UnlearnablePC.

In recent years, deep neural networks (DNNs) have witnessed extensive applications, and protecting their intellectual property (IP) is thus crucial. As a non-invasive way for model IP protection, model fingerprinting has become popular. However, existing single-point based fingerprinting methods are highly sensitive to the changes in the decision boundary, and may suffer from the misjudgment of the resemblance of sparse fingerprinting, yielding high false positives of innocent models. In this paper, we propose ADV-TRA, a more robust fingerprinting scheme that utilizes adversarial trajectories to verify the ownership of DNN models. Benefited from the intrinsic progressively adversarial level, the trajectory is capable of tolerating greater degree of alteration in decision boundaries. We further design novel schemes to generate a surface trajectory that involves a series of fixed-length trajectories with dynamically adjusted step sizes. Such a design enables a more unique and reliable fingerprinting with relatively low querying costs. Experiments on three datasets against four types of removal attacks show that ADV-TRA exhibits superior performance in distinguishing between infringing and innocent models, outperforming the state-of-the-art comparisons.

As one of the promising self-powered sensors on Internet of Things (IoT) platforms, unmanned aerial vehicles (UAVs) have attracted much attention for parcel delivery. Their high flexibility and low cost facilitate last-one-mile delivery. However, the limitations of battery capacity and payloads prevent drones from delivering independently over large scales. In this case, it is available to employ vehicles to assist the drones. The vehicles can be private-own trucks and vehicles in public transportation systems (PTSs). Compared to trucks, PTSs, such as buses and trains, do not require extra operating and fuel costs. Given these advantages, this article adopts PTSs to assist UAVs in parcel delivery. Nevertheless, the fixed routes and schedules of public vehicles pose new challenges to the routing and scheduling problem for PTS-assisted multidrone parcel delivery (RSPMD). To tackle the problem, we propose a novel routing and scheduling algorithm, referred to as the PTS-assisted multidrone parcel delivery (PDD) algorithm. Considering the schedules of the public vehicles, the algorithm jointly optimizes the distance and time cost of drones by iteratively combining parts of existing routes. To the best of our knowledge, we are the first to address RSPMD in which UAVs ride public vehicles to deliver parcels in a wide area. Simulation results are finally presented to demonstrate that PDD outperforms existing solutions in terms of effectiveness and efficiency.

Currently, leading methods for spectral super-resolution (SSR) depend heavily on constructing diverse network architectures in a heuristic manner, in order to learn a full mapping from the RGB image to its corresponding hyperspectral image (HSI). Despite promising results in reconstruction performance, significant challenges remain with respect to model interpretation and the capture of long-range dependencies. In response to these issues, based on a comprehensive exploration of the physical imaging mechanism between spectral response curve (SRC) and HSI, we have developed a novel model-driven degradation-aware unfolding network (DAUNet) in an iterative way. Besides, the learning process is explicitly integrated with the intrinsic generation mechanism of the SSR task. To be specific, we unfold each step into a degradation-aware gradient decent (DAGD) module and a proximal mapping module (PMM), using the framework of maximum a posteriori (MAP) theory. Additionally, to introduce more discriminative learning capabilities to our network, we have further enhanced the PMM architecture by incorporating a fine-grained multihead spectral-wise transformer (FMST) block, which improves global feature representation compared to the channel-wise transformer block. Extensive experiments over several spectral datasets finely demonstrate the superior performance of our method beyond the current representative state-of-the-art (SOTA) SSR methods.

Deep-learning-based fault diagnosis models perform well when the training and test sets have the same label set. However, these models are invalid in practical applications because they misclassify any unknown faults into existing known classes. An effective diagnosis model for practical industrial applications requires the ability to detect unknown faults as well as maintain high classification accuracy on known faults. To address this challenge, this article proposes a generic open-set classification method for vibration signals. We propose a variational encoder-classifier structure to extract the robust latent features that have different specific distributions with respect to their classes. According to the distances between the latent feature distributions, the samples from unknown faults are rejected using extreme value theory (EVT) and empirical threshold. In addition, we devised an EVT-based instance-level regularization weight function to allow the model to enhance the regularization on the samples that around the known and unknown decision boundaries, which can reduce the risk of bias in the empirical threshold setting caused by the hard training samples. Experimental results on five public rotating machinery vibration datasets reveal that the proposed method achieves the best performance for each dataset. This demonstrates the effectiveness and superiority of the proposed method for practical application scenarios.

Hyperspectral (HS) reconstruction from RGB images denotes the recovery of whole-scene HS information, which has attracted much attention recently. State-of-the-art approaches often adopt convolutional neural networks to learn the mapping for HS reconstruction from RGB images. However, they often do not achieve high HS reconstruction performance across different scenes consistently. In addition, their performance in recovering HS images from clean and real-world noisy RGB images is not consistent. To improve the HS reconstruction accuracy and robustness across different scenes and from different input images, we present an effective HSGAN framework with a two-stage adversarial training strategy. The generator is a four-level top-down architecture that extracts and combines features on multiple scales. To generalize well to real-world noisy images, we further propose a spatial-spectral attention block (SSAB) to learn both spatial-wise and channel-wise relations. We conduct the HS reconstruction experiments from both clean and real-world noisy RGB images on five well-known HS datasets. The results demonstrate that HSGAN achieves superior performance to existing methods. Please visit https://github.com/zhaoyuzhi/HSGAN to try our codes.

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modifications for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause the performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data.

Volumetric video offers a highly immersive viewing experience, but poses challenges in ensuring quality of experience (QoE) due to its high bandwidth requirements. In this paper, we explore the effect of viewing distance introduced by six degrees of freedom (6DoF) spatial navigation on user's perceived quality. By considering human visual resolution limitations, we propose a visual acuity model that describes the relationship between the virtual viewing distance and the tolerable boundary point cloud density. The proposed model satisfies spatial visual requirements during 6DoF exploration. Additionally, it dynamically adjusts quality levels to balance perceptual quality and bandwidth consumption. Furthermore, we present a QoE model to represent user's perceived quality at different viewing distances precisely. Extensive experimental results demonstrate that, the proposed scheme can effectively improve the overall average QoE by up to 26% over real networks and user traces, compared to existing baselines.

The appearances of children are inherited from their parents, which makes it feasible to predict them. Predicting realistic children's faces may help settle many social problems, such as age-invariant face recognition, kinship verification, and missing child identification. It can be regarded as an image-to-image translation task. Existing approaches usually assume domain information in the image-to-image translation can be interpreted by “style”, i.e., the separation of image content and style. However, such separation is improper for the child face prediction, because the facial contours between children and parents are not the same. To address this issue, we propose a new disentangled learning strategy for children's face prediction. We assume that children's faces are determined by genetic factors (compact family features, e.g., face contour), external factors (facial attributes irrelevant to prediction, such as moustaches and glasses), and variety factors (individual properties for each child). On this basis, we formulate predictions as a mapping from parents’ genetic factors to children's genetic factors, and disentangle them from external and variety factors. In order to obtain accurate genetic factors and perform the mapping, we propose a ChildPredictor framework. It transfers human faces to genetic factors by encoders and back by generators. Then, it learns the relationship between the genetic factors of parents and children through a mapping function. To ensure the generated faces are realistic, we collect a large Family Face Database to train ChildPredictor and evaluate it on the FF-Database validation set. Experimental results demonstrate that ChildPredictor is superior to other well-known image-to-image translation methods in predicting realistic and diverse child faces. Implementation codes can be found athttps://github.com/zhaoyuzhi/ChildPredictor.

The physical unclonable function (PUF) is a widely used hardware security primitive. Many strong PUF designs have been proposed to resist non-invasive attacks leveraging acquired CRPs. In this work, we propose a general framework for efficient attacks on strong PUFs by investigating from two perspectives, namely, statistical covariances in the challenge space and the design dependency among PUF compositions. The framework consists of two novel attack methods against a wide range of PUF families, including XOR APUFs, interpose PUFs, and bistable ring (BR)-PUFs. We evaluate our proposed attacks through extensive experiments, running both software-based simulation and hardware implementations on FPGAs to compare with corresponding SOTA works. Much effort have been expended to maintain the same software/ hardware conditions for fair comparison. The results demonstrate that our framework significantly outperforms SOTA results. Moreover, we show that our framework can efficiently attack multiple PUF families built from entirely different types, while almost all existing works solely focused on attacking one or very limited number of PUF designs.

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.

Hybrid memory systems composed of DRAM and Non-Volatile Memory (NVM) can offer very large memory capacity for data-intensive applications such as graph processing. The performance of parallel graph processing is often affected by straggler tasks due to asymmetrical graph partitioning and sub-graph processing. In hybrid memory systems, the significant difference of performance between DRAM and NVM may exacerbate the load imbalance of parallel graph processing. Traditional load balancing schemes that only aim to balance computing loads among multiple processors are no longer effective in hybrid memory systems.In this paper, we first explore how hybrid memory systems affect the efficiency of existing graph processing systems. We make two observations: 1) Traditional work-stealing schemes may be not efficient enough in hybrid memory systems. Data migration combining with work-stealing can further improve the efficiency of parallel graph processing; 2) Interleaving accesses to different graph partitions due to data dependency have a significant impact on the effectiveness of data migration. To address these problems, we propose NVMGraph, a parallel graph processing scheme to mitigate memory access imbalance among partitions in hybrid memory systems. We first recognize data dependency among all partitions according to graph algorithms and then coalesce the randomly-accessed data required by a working thread in a single partition to mitigate data dependency. To reduce migration cost while still speeding up the straggler task, we only migrate randomly-accessed data blocks in the most frequently-accessed partition from NVM to DRAM. We implement NVMGraph based on Ligra and evaluate it in a hybrid memory system using real NVM devices. Experimental results demonstrate that NVMGraph can improve the performance of graph processing by up to 40.3% compared with the state-of-the-art Ligra and Polymer.

Hybrid memories exacerbate the asymmetry of memory access latencies in Non-Uniform Memory Access (NUMA) systems due to the vast performance gap between DRAM and Nonvolatile Memory (NVM). Since most graph processing systems have not considered the memory heterogeneity of NUMA nodes, they have sub-optimal performance due to improper data placement and access strategies. This paper proposes HNGraph, a graph processing framework for hybrid memory based NUMA systems. It mainly focuses on performance improvement by reducing random accesses to both local and remote NVM nodes. First, HNGraph assembles most random memory accesses in DRAM by exploiting a degree-aware partitioning strategy, which distributes high-degree and low-degree vertices to DRAM and NVM nodes, respectively. Second, we propose an adaptive graph processing model, which uses a hybrid inter-node communication mechanism to adapt to the asymmetric access latency between NVM and DRAM nodes. In DRAM nodes, we exploit a message passing communication model for remote random NVM updates. In NVM nodes, we use shared memory primitives to access remote DRAM directly. We evaluate the performance of HNGraph using different graph algorithms on typical datasets. Experimental results show that HNGraph can improve the application performance by 43.8% and 30.6% on average compared with the state-of-the-art graph processing systems GBBS and Polymer, respectively.

Summary With the increasing cloud‐trend of high‐performance computing (HPC), more users submit their applications simultaneously to the platform and wish they could finish before the deadline. Moreover, due to the severe holistic performance degradation caused by I/O contention, a deadline‐sensitive I/O scheduler is needed to allocate storage resources according to the requirements of applications and resultantly guarantee the quality of service (QoS) of concurrently running applications. In this paper, we first explore the bandwidth allocation phenomenon caused by interference in applications through the modeling of historical data, and then we quote a metric called random percentage that can represent the random degree of the applications and be used to guide I/O scheduling in the later stage. We design a dynamic I/O scheduler named DDL‐QoS that uses solid state drives(SSDs) as QoS guarantee to minimize interference and ensure applications meet their deadline. The potential of our design is that the greater the I/O interference, the greater the performance improvement, but this performance improvement will be limited by the physical properties of the storage hardware.

Currently, HPC storage systems still use hard disk drive (HDD) as their dominant storage device. Solid state drive (SSD) is widely deployed as the buffer to HDDs. Burst buffer has also been proposed to manage the SSD buffering of bursty write requests. Although burst buffer can improve I/O performance in many cases, we find that it has some limitations such as requiring large SSD capacity and harmonious overlapping between computation phase and data flushing phase. In this article, we propose a scheme, called SSDUP+. 1 SSDUP+ aims to improve the burst buffer by addressing the above limitations. First, to reduce the demand for the SSD capacity, we develop a novel method to detect and quantify the data randomness in the write traffic. Further, an adaptive algorithm is proposed to classify the random writes dynamically. By doing so, much less SSD capacity is required to achieve the similar performance as other burst buffer schemes. Next, to overcome the difficulty of perfectly overlapping the computation phase and the flushing phase, we propose a pipeline mechanism for the SSD buffer, in which data buffering and flushing are performed in pipeline. In addition, to improve the I/O throughput, we adopt a traffic-aware flushing strategy to reduce the I/O interference in HDD. Finally, to further improve the performance of buffering random writes in SSD, SSDUP+ transforms the random writes to sequential writes in SSD by storing the data with a log structure. Further, SSDUP+ uses the AVL tree structure to store the sequence information of the data. We have implemented a prototype of SSDUP+ based on OrangeFS and conducted extensive experiments. The experimental results show that our proposed SSDUP+ can save an average of 50% SSD space while delivering almost the same performance as other common burst buffer schemes. In addition, SSDUP+ can save about 20% SSD space compared with the previous version of this work, SSDUP, while achieving 20–30% higher I/O throughput than SSDUP.

In this paper, we exploit the radio diversity gain in a multi-user hybrid radio network wirelessly powered by a power beacon station (PBS). Each user has a dual-mode radio that can switch between the passive and active modes, according to the channel and energy conditions. This provides extra degree of freedom to improve the overall network performance. As such, we propose a throughput maximization problem by jointly optimizing the PBS' energy beamforming and the radios' transmission scheduling strategies in two modes. We show that the throughput maximization is easily tractable by solving a semi-definite program. However, it becomes non-convex and intractable when we allow radios' cooperation in data transmissions. To this end, we propose a set of heuristic algorithms with different complexities for cooperative relay transmissions, which are shown to significantly improve the sum throughput compared to the non-cooperative case. The simulation results show that a simple adaptive scheme can achieve the maximum throughput according to the PBS' power supply.

The integration of wireless power transfer (WPT) with the backscatter communications provides a promising way to sustain batteryless wireless networks. In this paper, we consider a backscatter communication network, in which the passive radio uses the harvested energy from a power beacon station (PBS) to supply its data transmissions, while some other radios can help as the wireless relays. To improve the throughput performance of a distant transceiver pair, we propose a two-hop backscatter relay model and formulate a throughput maximization problem to jointly optimize WPT and the relay strategies. Noting that the proposed problem is non-convex, an iterative algorithm with reduced complexity is proposed to decompose the original problem into a power allocation subproblem in the outer loop and an optimization of the relay strategy in the inner loop. Numerical results reveal that the power allocation converges to the optimum and the relay strategy significantly improves the throughput when the radios' power demand is low.

The integration of wireless power transfer (WPT) with the low-power backscatter communications provides a promising way to sustain battery-less wireless networks. In this paper, we consider a backscatter communication network wirelessly powered by a power beacon station (PBS). Each backscatter radio uses the harvested energy to power its data transmissions, in which some other radios can help as the wireless relays with an aim to improve throughput performance by cooperative transmission. Under this setting, we formulate a throughput maximization problem to jointly optimize WPT and the relay strategy of the backscatter radios. An iterative algorithm with reduced complexity and communication overhead is proposed to decompose the original problem into two sub-problems distributed at the PBS and the backscatter receiver. Moreover, we take uncertain channel information into consideration and formulate robust counter-parts of the throughput maximization problem when either the backscatter or relay channel is subject to estimation errors. The difficulty of the robust counter-part lies in the coupling of the PBS' power allocation and relay strategy in matrix inequalities, which is addressed by alternating optimization with guaranteed convergence. Numerical results reveal that the cooperative relay strategy of the backscatter radios significantly improves the throughput performance.

Multiple-choice (MC) question is an important form of test to assess the students' academic achievement, especially in the e-learning applications. However, the classical evaluation metrics on MC questions (such as the correctness ratio) only consider the correctness of the final selection but ignore the solving progress of the testee. In the existing literature, the eye-tracking based visual attention was studied to infer the testee's cognitive progress towards a specific MC question. However, there is little work on the visual attention based evaluation of one complete MC test. In this paper, we measure the eye movement data of a group of students in an online test, which consists of forty more MC questions. We divide the screen area into five AOIs (area of interests), including one for the question and four for the candidate options. The fixation duration as well as the gaze sequence on these AOIs are recorded and studied. In the case study on the most difficult question, we observe the great differences among the eye movement of the testees in different academic levels. A new metric, namely Visual-Attention-assisted Score (VAS), is proposed to assess the student's performance with the bias of his fixations on the correct options. Experiment results show that, this metric can reflect the difference of gaze movement of testees, and thus it is helpful for the teachers to infer the real level of the students' academic achievement.

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.

Many high performance computing (HPC) applications are highly data intensive. Current HPC storage systems still use hard disk drives (HDDs) as their dominant storage devices, which suffer from disk head thrashing when accessing random data. New storage devices such as solid state drives (SSDs), which can handle random data access much more efficiently, have been widely deployed as the buffer to HDDs in many production HPC systems. Burst buffer has also been proposed to manage the SSD buffering of bursty write requests. Although burst buffer can improve I/O performance in many cases, we find that it has some limitations such as requiring large SSD capacity and harmonious overlapping between computation phase and data flushing stage.

High performance computing (HPC) applications are becoming more data-intensive and produce increasingly large I/O demands on storage systems. New storage devices such as SSD which has nearly no seek latency and high throughput have been widely used together with HDD to serve as a hybrid storage system. To solve the I/O bottleneck problem, existing hybrid storage solutions such as Burst Buffer have been proposed as intermediate layer between clients and disks to absorb burst I/O requests and improve write performance. However Burst Buffer needs sufficient SSD space to meet the maximum burst I/O requests which is still a costly solution. In this paper, we propose a hybrid architecture called SSDUP (an SSD write buffer Using Pipeline) for HPC storage systems, which uses NAND flash based SSD as a write-back buffer for HDD. With our efforts, SSDUP can achieve a good performance by using limited SSD space.

Data capture is important for some critical network applications, such as network diagnosis and criminal investigation. In multi-channel wireless networks, the fundamental challenge for data capture is how to assign operation channels to wireless sniffers. The existing approaches make some impractical assumptions, such as the prior knowledge on network traffic and the perfect conditions of data capture. In this paper, we relax these assumptions and investigate the sniffer-channel assignment problem in multi-hop scenarios. Especially, sniffer redundancy deployment is discussed, which enables multiple sniffers to monitor one traffic. This problem is formulated as a combinatorial multi-arm bandit (MAB) problem, and a cooperative distribute learning policy is proposed. We analyze the regret of our policy in theory, and validate its effectiveness through numerical simulations.

Sniffer channel assignment (SCA) is a fundamental building block for wireless data capture, which is essential for traffic monitoring and network forensics. Most of the existing SCA approaches for cognitive radio networks (CRNs) adopt optimization-based methods and rely on the prior knowledge of the secondary user (SU) activities. To relax this constraint, learning-based methods have been recently developed; however, there is still insufficient theoretical understanding within the learning framework for SCA. In this paper, we aim to maximize the total amount of the captured SU traffic, and we formulate the SCA problem as a nonstochastic/adversarial multiarmed bandit problem. Moreover, the inherent error in wireless capturing, i.e., imperfect monitoring, is considered in our model. We propose two online learning algorithms for the SCA scenarios with and without channel switching costs, respectively, and their regret performances are proved uniformly sublinear in time and polynomial in the number of channels. The numerical evaluation shows, in addition to their robust regret performances, the proposed algorithms greatly outperform the existing SCA approaches in the amount of effectively captured SU traffic.

Passive network monitoring is important for the critical applications of network diagnosis and criminal investigation. As in multi-channel wireless networks, the sniffer-channel assignment problem faces a tradeoff between exploitation and exploration. In this paper, we investigate this problem in a practical scenario. Different from the existing literature, we assume that the knowledge of the users' activities is not known a priori, and there exists capture uncertainty due to unreliable monitoring conditions. Furthermore, we consider the case of sniffer redundancy deployment, which enables multiple sniffers to monitor one channel to enhance capture reliability. Our problem is then formulated as a combinatorial multi-arm bandit problem. We propose an online learning policy, in which sniffer-channel assignment is dynamically decided based on the learning results of the users' activities. We further develop a greedy algorithm to achieve the channel assignment decision in polynomial time. Our solution is evaluated by both theoretical analysis and numerical simulations. Simulation results show that our policy achieves logarithmic regret in time and outperforms the learning policy without consideration of sniffer redundancy deployment.

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.

The prediction of traffic matrices (TM) is critical for many IP network management tasks. With the recent development of high-speed traffic measurement technologies, complete TM could be collected from operational IP networks. In this paper, we report our efforts in predicting the TM measured from a real IP backbone network in China. The new problem here is how to deal with the rich but noisy TM data and predict various traffics, ranging from the original-destination (OD) flow traffic, the node traffic to the total network traffic. After examining the traffic characteristics, we choose the node traffic as the principal data for prediction, and propose three prediction and correction methods: Independent Node Prediction (INP), Total Matrix Prediction with Key Element Correction (TMP-KEC) and Principle Component Prediction with Fluctuation Component Correction (PCP-FCC). TMP-KEC and PCP-FCC are designed with different purposes, i.e., for smaller prediction errors of the total network and the OD flows, respectively. The results show that, INP performs worst; TMP-KEC efficiently reduces the prediction errors of the large matrix elements; while, PCP-FCC achieves smaller average prediction errors for the elements as well as the complete matrix.

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%.

The harmonic coexistence of secondary users (SUs) and primary users (PUs) in cognitive radio networks requires SUs to identify the idle spectrum bands. One common approach to achieve spectrum awareness is through spectrum sensing, which usually assumes known distributions of the received signals. However, due to the nature of wireless channels, such an assumption is often too strong to be realistic, and leads to unreliable detection performance in practical networks. In this paper, we study the sensing performance under distribution uncertainty, i.e., the actual distribution functions of the received signals are subject to ambiguity and not fully known. Firstly, we define a series of uncertainty models based on signals' moment statistics in different spectrum conditions. Then we present mathematical formulations to study the detection performance corresponding to these uncertainty models. Moreover, in order to make use of the distribution information embedded in historical data, we extract a reference distribution from past channel observations, and define a new uncertainty model in terms of it. With this uncertainty model, we propose two iterative procedures to study the false alarm probability and detection probability, respectively. Numerical results show that the detection performance with a reference distribution is less conservative compared with that of the uncertainty models merely based on signal statistics.

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.

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.

The successful coexistence of cognitive radio systems with licensed system requires the secondary users the capability of interference-awareness, i.e., knowing which spectrum bands are occupied by primary users, i.e., the legacy users. Spectrum sensing thus is a key enabling module, which usually models the sensing process as a binary hypothesis testing assuming known signal distribution. However, an unrealistic assumption regarding the signal distribution easily leads to unreliable detection probability. In this paper, we study the sensing performance considering the distribution uncertainty in hypothesis testing, i.e., the actual distribution function of the received signal strength is not known. According to different signal characteristics, we define appropriate uncertainty sets respectively for different hypotheses. Then we present an approximate approach to determine the robust decision threshold, and investigate the performance bounds for the detection probability under distribution uncertainty. Moreover, we provide an analytical expression for the lower bound of detection probability. Numerical results are given to validate our conclusions.

In a cognitive radio network, the full-spectrum is usually divided into multiple channels. However, due to the hardware and energy constraints, a cognitive user (also called secondary user) may not be able to sense two or more channels simultaneously. As different channels may have different primary user activities and time-varying channel qualities, an important task is to select which channels to sense and access for a given time period so that the available spectrum left by the primary users can be fully utilized by the secondary user. In this paper, we propose an optimal sensing channel selection policy based on partially observable Markov decision process (POMDP). The proposed policy takes the time-varying channel state into consideration and intends to optimally exploit spectrum resources for the secondary user. In addition to selecting optimal channel to sense, we also derive the optimal sensing time which leads to maximized throughput of the secondary user.

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.

Positioning of real world objects (e.g., people) in indoor environment will facilitate location dependent or context-aware applications. Due to severe multi-path fading effect in indoor wireless environment, received signal strength indicator (RSSI) based indoor positioning systems usually require a great amount of human intervention for data measurement during the system initiation. This paper proposes a novel two-phase positioning technique that has been implemented and tested in real environment. Experiment results show that our method can significantly cut down the requirements on data acquisition and achieve satisfactory performance in terms of error distance.

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.

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.

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.

In this paper, a new initial radius (IR) selection method of Sphere Decoding (SD), called IR-ZF-OSUC, for MIMO system is proposed. An significant merit is that this method utilizes the result ofQR decomposition which is inherent in SD to obtain a suboptimal solution, and selects the distance between the received signal and the lattice point mapped by the suboptimal solution as the IR, which makes the procedure can be embedded in the body of SD. Unlike other approaches, it needs no extra processing, so lower computational complexity is achieved. Additionally, this method includes an ordering step, which not only makes the IR be closer to the optimal value but also increases the efficiency ofSD itself. The simulation results show that the computational complexity ofSD with IR-ZF-OSUC is lower than the ones using other approaches over a wide range ofSNRs.

IEEE 802.11 MAC standard and model are well researched. Although it supports multiple channels in physical layer, how to provide multi-channel support in MAC layer is still challenging. Most of current work on multi-channel MAC only evaluate by simulations. In this paper, we propose a simple but general scheme to describe channel switching process. With its help, we extend the IEEE 802.11 MAC into a multi-channel MAC, and present a three-dimensional Markov chain to model it. This analytical model supports both access methods: basic and RTS/CTS. Simulation results show that our model can predict saturation throughput well. We also investigate the effect of two major parameters, namely the number of retransmission and the number of switching channel, on the performance of multichannel MAC.

We present a cluster-based virtual multiple-input single-output (vMISO) multihop cooperative transmission protocol in wireless sensor networks. In order to construct the cooperative transmission, we arrange each hop into two consecutive time slots: the intra-cluster slot, that accounts for data sharing within the cluster, and the inter-cluster slot for transmission between clusters. In this protocol, we investigate a novel optimal cluster formation method and best cooperative transmission routing selection base on the network energy consumption status. Also, an optimization model is developed to find the optimum number of cooperative nodes, clusters and transmission rates. Simulation results show that the protocol can save energy significantly.

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.

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.

Caching video objects at the edge of the back-bone network has become a hot spot in the video delivery research. Some researchers proposed segmentation-based caching algorithms to improve the disk utility in the homogeneous environment. Other researchers proposed caching algorithms with video layering or transcoding to improve the perceptual quality in the heterogeneous environment. However, for a low cost proxy, these systems are suffering from either inflexible structures or high computation. In this paper, the overall quality hit ratio(QHR) is proposed to measure how well the clients' requests on the quality are satisfied. Aiming to achieve the highest QHR, an intelligent caching algorithm is designed to determine the appropriate perceptual quality of the segments cached in the proxy. This innovative approach takes not only the access bandwidth distribution into account, but also the segment popularity distribution. Simulation results demonstrate that our variable rate caching(VRC) algorithm not only achieves the highest QHR but also keeps its complexity at an acceptable level.

The DiffServ Assured Forwarding (AF) service provides a scalable solution to QoS guarantee. Currently, AF only provides qualitative differentiation of delay or loss between classes of service, but not quantitative guarantee. By studying the quantitative behaviour of the steady state operating point for RIO, we propose two adaptive RIO algorithms for per-hop QoS provisioning in AF service. These two algorithms, ARIO-D and ARIO-L, work by dynamically adjusting the RIO parameters so that the packet delay and the packet loss are, respectively, kept at their target level. Simulation results show that they can provide both high link utilization as well as stable and differentiated delay or loss for different AF classes on a single router.

RIO is the primary queue management mechanism proposed for assured forwarding in the DiffServ framework. Although RIO can generally provide bandwidth guarantee, its performance in terms of both delay and loss is sensitive to traffic level. In this paper, we demonstrate this sensitivity problem by simulation and present a qualitative explanation for its origin. We propose two adaptive algorithms to overcome this problem. Simulation results show that they can effectively improve the robustness of RIO under different and dynamic traffic, and provide stable and quantitative performance of delay or loss.

Most of the current proxy caching algorithms for streaming video media assume that users favor the beginning of the media object. However, this assumption is questionable in highly interactive scenarios, such as e-learning, where some parts of the video other than the prefix can also be popular. A new segment-based proxy caching algorithm, named popularity-wise caching, is proposed for highly interactive streaming. It is designed to deal with arbitrary popularity distribution of media content. Simulations are performed using synthetic traces with different kinds and levels of user interactivity. The results show that the performance of current segment-based caching (such as exponential caching and soccer caching) degrade with increasing user interactivity, while popularity-wise caching can provide the lowest user startup latency for interactive requests and highest bandwidth saving for the backbone network.