Hyperspectral image feature extraction plays a crucial role in reducing the redundancy and correlation among spectral bands while preserving the essential information. Knowledge-driven feature extraction methods, such as spectral indices (SIs), leverage the interaction mechanisms between electromagnetic waves and materials to enhance the characteristic attributes of ground objects through band operations. These methods offer key advantages, including strong physical interpretability, simple construction, and robust scene reusability. However, most of the existing SIs still rely on expert knowledge tailored to specific scenarios, leading to inherent limitations, such as subjectivity, high time consumption, and implementation complexity. In this article, to address these challenges, we propose a hyperspectral image multi-agent deep reinforcement learning feature extraction (HI-MAFE) algorithm, aiming to alleviate the burden of manual SIs design by human experts. HI-MAFE employs a heuristic “generation-selection” strategy to simulate the decision-making process of domain experts, with specifically designed deep reinforcement learning (DRL) models for both the generation and selection steps. To accelerate exploration in a high-dimensional action space, the model incorporates a multi-agent deep reinforcement learning (MADRL) framework. The experimental results demonstrate the effectiveness and superiority of the proposed algorithm for hyperspectral image classification. The proposed HI-MAFE framework leverages DRL to autonomously generate meaningful environmental interpretation from spectral data, thereby reducing the reliance on manually designed SIs. This research can inspire future work in SIs construction and complement the limitations of data-driven approaches.

Soil organic matter (SOM) content is a crucial indicator for assessing soil fertility and serves as a key factor in sustaining a viable agricultural system. With the continuous advancement and improvement of remote sensing technology, hyperspectral imagery has been employed for the monitoring of SOM. While the numerous bands reveal finer details within the spectral features, this also brings information redundancy and noise interference. Currently, the dimensionality reduction methods designed for hyperspectral imagery encounter difficulties in achieving optimal band combinations. As a result, swiftly and accurately capturing the spectral features of SOM becomes a challenging task. In this article, aiming to address the inefficiency and instability in hyperspectral feature selection, we propose a metaheuristic-based algorithm—the improved weighted marine predators algorithm (IWMPA)—for hyperspectral feature selection. Specifically, we simulated the process of hyperspectral feature selection using the foraging strategy of marine predators. We employed prior weight coefficients and reverse learning operations in the initialization phase to accelerate the convergence of the population and introduced mutation operations into the phase of development to prevent the occurrence of local optima traps. We employed the IWMPA feature selection method to establish SOM estimation models within the research area of Yitong Manchu Autonomous County in China. The results demonstrated that the hyperspectral features selected using the IWMPA approach yield favorable outcomes in the SOM estimation models. Specifically, in the best-performing regression model of this study, R2 on the test set was 0.7225. These experimental results suggest that, in comparison to the existing methods, the proposed IWMPA method is more adept at capturing the spectral features of SOM.

Automatic building localization and extraction based on high-resolution remote sensing images is of great importance to city mapping and smart city management. Extracted buildings with high precisions and fine boundaries contribute to the vectorization operation and, thus, the digital line graph (DLG) production. In this regard, a comprehensive framework Poly-BRBLE is proposed, combining a boundary refinement-based individual building localization and extraction model BRBLE as well as a particularly revised regularization method. The BRBLE is mainly composed of a multiscale feature fusion and propagation module and a coarse-to-fine mask optimization module, which allow the model to identify buildings from similar backgrounds and extract them with precise boundaries. Comparisons were made between BRBLE and other classical and state-of-the-art models on the WHU building dataset, the Chinese building instance segmentation dataset, and the Inria Polygon dataset, which demonstrated that BRBLE outperformed the second-best model by 2%, 1.5%, and 1.2%, respectively, in${\text {AP}}_{\text {mask}}$, and the advantage was further enlarged on large objects to 5.2%, 1.8%, and 2%. Moreover, we built the SH building dataset, which contained complex-shaped buildings and mixed-built environments, where it was demonstrated that BRBLE outperformed the second-best model by 3.4% in${\text {AP}}_{\text {mask}}$. We further compared the precisions of the building footprints obtained by the Poly-BRBLE and other different methods, where Poly-BRBLE showed a superior performance, with an${\text {AP}}_{\text {mask}}$score of 60.4%, which demonstrated that it is capable of extracting complicated buildings, such as high-rise buildings and villas, and factories of multiple shapes, even though the images were off-nadir.

Hyperspectral target detection (HTD) relies on prior target spectra to locate the targets of interest within hyperspectral images. Recently, deep learning methods have shown their potential in hyperspectral feature extraction and multi-scale feature fusion. In this article, we propose a background-aware sparse transformer network (BASTNet) for hyperspectral target detection, to solve the problems of target sample imbalance and underutilization of global information. Firstly, the proposed method utilizes random masking and target spectra generation strategies to establish an image-level training paradigm, constructing sufficient and balanced training samples to prompt the network to learn spatial-contextual features between the target and the background. We then introduce a Siamese sparse transformer network (S2TNet) with an encoder-decoder structure to achieve fast inference for large-scene hyperspectral imagery. Specifically, S2TNet consists of pyramid feature extraction, multi-scale feature fusion, and a target detector, with a sparse self-attention mechanism enhancing the focus on target regions and improving the separability between target and background. Furthermore, a background-aware learning mechanism is introduced that uses a foreground and background guidance loss that attenuates the interference of background noise on the target detection. Experiments on five benchmark datasets demonstrate the superiority and applicability of the proposed BASTNet method, showing that it outperforms the current state-of-the-art hyperspectral target detection methods.

Semi-supervised change detection(SSCD) in remote sensing faces two critical challenges: cyclical error propagation from noisy pseudo-labels and the loss of fine-grained boundary details. To address these, we propose a novel SSCD framework, the Semi-supervised Connectivity-Aware Decoupling Network (SCAD-Net). SCAD-Net breaks the cycle of error amplification with a strategy called Pixels-Regions via Curriculum-guided Consistency Learning (P2R-CL). This strategy progresses from initial, high-confidence pixel-level supervision to more flexible, region-based semantic correction as training matures. To tackle boundary ambiguity, SCAD-Net employs a two-stage decoupling architecture: a Channel Information Decoupling Module (CIDM) separates categorical and directional features, followed by a Multi-scale Attention and Feature Integration (MAFI) module that reintegrates this information for precise boundary localization. Experiments on four benchmark datasets demonstrate our method’s superiority. On the SHD, using only 10% labeled data, SCAD-Net achieves an F1-score of 90.1%, representing a 3.88% improvement over state-of-the-art methods. Similarly, strong performance is observed on the LEVIR-CD (90.31% F1), WHU-CD (89.35% F1) and CDD (87.05% F1) datasets. This feature decoupling paradigm offers a robust approach for semi-supervised remote sensing in environments.

Urban change detection (CD) is crucial for informed decision-making but faces various challenges, including complex features, rapid changes, and extensive human interventions. These challenges underscore the urgent need for innovative multiclass CD (MCD) techniques that extensively incorporate deep learning (DL). Despite several successes achieved with the DL-based MCD methods, still certain shortcomings persist, including the disregard for spatial principles, which significantly hinders the seamless integration of geoscience-knowledge and artificial-intelligence. In this article, a novel DL model known as the position-aware graph-convolutional neural network (CNN) fusion network (PGCFN) is introduced, integrating spatial position encoding to effectively detect urban changes. The model’s first part encodes geospatial positions following Tobler’s first law (TFL) of geography. It then integrates encoded positions into an MCD model, combining a graph attention network (GAT) with a CNN to enhance performance. The model was tested on 0.5-m resolution remote sensing (RS) images, achieving an impressive minimum mean intersection over union (MIoU) score of 91.20%. Additionally, the model’s position-aware graph attention module exhibited a strong emphasis on geographic proximity when evaluating connections between superpixels. Overall, these findings affirm that our model could effectively addresses urban CD challenges and significantly enhances the integration of geoscience knowledge and artificial intelligence (AI).

Convolutional neural networks (CNNs) have achieved success in HSI classification, but the performance is constrained by the limited reception field. In this regard, vision transformer is introduced recently, which is of powerful capabilities in long-range feature extraction for HSI classification. However, transformers are computation intensive and poor for local feature extraction. The motivation for this study is to build a lightweight hybrid model, which ensembles the respective inductive bias from CNNs and global receptive field from transformers. In this work, we propose a concise and efficient framework—the spatial-spectral feature extraction network with patch attention module (PASSNet), to simultaneously extract both local and global features. Specifically, we design an innovative plugin called patch attention module (PAM), which can be easily integrated into both CNNs and transformers blocks to extract spatial-spectral features from multiple spatial perspectives. Besides, a novel partial convolution operation is introduced, with a reduced computational cost than vanilla convolution operation. Through coupling the local attention from the CNNs with the global receptive fields in the transformers, the proposed PASSNet exhibits a superior classification performance on three well-known datasets with a small training sample size.

The issues of spectral redundancy and limited training samples hinder the widespread application and development of hyperspectral images. In this letter, a novel active deep feature extraction scheme is proposed by incorporating both representative and informative measurement. Firstly, an adversarial autoencoder is modified to suit the classification task with deep feature extraction. Dictionary learning and a multi-variance and distributional distance (MVDD) measure are then introduced to choose the most valuable candidate training samples, where we use the limited labeled samples to obtain a high classification accuracy. Comparative experiments with the proposed querying strategy were carried out with two hyperspectral datasets. The experimental results obtained with the two datasets demonstrate that the proposed scheme is superior to the others. With this method, the unstable increase in accuracy is eliminated by incorporating both informative and representative measurement.

With satellite platforms gazing at a target territory, the captured satellite videos exhibit local misalignment and local intensity variation on some stationary objects that can be mistakenly extracted as moving objects and increase false alarm rates. Typical approaches for mitigating the effect of moving cameras in moving object detection (MOD) follow domain transformation technique, where the misalignment between consecutive frames is restricted to the image planar. However, such technique cannot properly handle satellite videos, as the local misalignment on them is caused by the varying projections from the 3D objects on the Earth's surface to 2D image planar. In order to suppress the effect of moving satellite platform in MOD, we propose a Moving-Confidence-Assisted Matrix Decomposition (MCMD) model, where foreground regularization is designed to promote real moving objects and ignore system movements with the assistance of a moving-confidence score estimated from dense optical flows. For solving the convex optimization problem in MCMD, both batch processing and online solutions are developed in this study, by adopting the alternating direction method and the stochastic optimization strategy, respectively. Experimental results on the videos captured by SkySat and Jilin-1 show that MCMD outperforms the state-of-the-art techniques with improved precision by suppressing effect of nonstationary satellite platforms.

The ZY1-02D satellite, which was launched in 2019, is China’s first civil hyperspectral satellite. However, the laboratory calibration and vicarious calibration methods could not provide accurate radiometric calibration coefficients after the satellite had been launched. In this article, we describe how a cross-calibration method was utilized to calibrate the ZY1-02D hyperspectral imager using the well-calibrated Gaofen-5 Advanced Hyperspectral Imager (GF-5 AHSI). The 6S radiative transfer model was selected to simulate the apparent reflectance of the two hyperspectral sensors under corresponding imaging conditions, and the calibration coefficients were calculated by spectral channel matching. The reflectance-based vicarious calibration was carried out for comparison. Through the validation experiments, it is shown that the reflectance data obtained by cross-calibration and vicarious calibration are basically consistent, showing a stable radiation performance. At the Dunhuang calibration site, the ratio of measured surface reflectance to the cross-calibrated image reflectance is between 0.9 and 1.1, the$R^{2}$values are more than 0.96, and the spectral angles are less than 3°. The validation results for different ground features also show the applicability of the corrected coefficients. When compared with different sensors, the maximum difference between the ZY1-02D reflectance results after cross-calibration and Landsat-8/Sentinel-2 is less than 0.04 and the mean difference is less than 0.02, which further proves that the ZY1-02D hyperspectral imager has a high radiation accuracy after cross-calibration. The proposed cross-calibration method could be used as an effective supplement to the on-orbit calibration method and could also be extended to other satellite hyperspectral imagers.

The highly correlated spectral features and the limited training samples pose challenges in hyperspectral image classification. In this article, to tackle the issues of end-to-end feature learning and transfer learning with limited labeled samples, we propose a unified multiscale learning (UML) framework, which is based on a fully convolutional network. A multiscale spatial-channel attention mechanism and a multiscale shuffle block are proposed in the UML framework to improve the problem of land-cover map distortion. The contextual information and the spectral feature are enhanced before the last classification layer based on three strategies in this work: 1) the channel shuffle operation, which was employed to learn the more effective spectral characteristics by disordering the channels of the feature map; 2) multiscale block, which considered the contextual information in multiple ranges; and 3) spatiospectral attention, which enhanced the expression of the important characteristic among all pixels. Three hyperspectral datasets, including two airborne hyperspectral images and one spaceborne hyperspectral image, were used to demonstrate the performance of the UML framework in both classification and transfer learning. The experimental results confirmed that the proposed method outperforms most of the state-of-the-art hyperspectral image classification methods. The source code is released athttps://github.com/Hyper-NN/UML.

The visible-shortwave infrared Advanced Hyperspectral Imager (AHSI) is a payload onboard the Gaofen-5 satellite, which is China’s first hyperspectral satellite and is part of the Chinese High-Resolution Earth Observation System. As a supplement to the onboard radiometric calibration of the AHSI instrument, vicarious calibration is also required, which is independent of the instrument-based calibration. In this article, a reflectance-based vicarious calibration approach is presented, which takes surface reflectance data, aerosol data, and atmospheric water vapor data into account. The Dunhuang test site, which is one of the China Radiometric Calibration Sites (CRCS) for the vicarious calibration of spaceborne sensors, possesses stable, uniform, and measurable surface objects, so it was chosen as the radiation source to replace the laboratory and onboard calibrators. A Spectra Vista Corporation (SVC) spectral radiometer and a CE318 sun photometer were utilized for the measurement of the surface reflectance and the condition of the aerosol, respectively. The radiance at the entrance pupil at the top of atmosphere was then obtained through the MODerate resolution atmospheric TRANsmission (MODTRAN) atmospheric transmission model. The surface reflectance was obtained using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric model for validation. The results show that, with regard to the calibration coefficients, the calibrated AHSI instrument presents a stable radiometric performance among different land-cover types. The ratios on all the bands are between 0.8 and 1.2 and are consistent with the reflectance data from the Dunhuang test site. The${R} ^{{2}}$values are all greater than 0.95 and the spectral angle is all less than 2°. The standard deviations of the ratios are less than 3% for each chosen band, which proves that the calibrated data have a high consistency with thein situmeasurements. When compared with Landsat 8 and Sentinel-2, the mean errors of the surface reflectance are all under 0.06, which further demonstrates that the calibrated reflectance has a high accuracy.

Heavy metals affect the soil and food adversely, as well as threaten the human health and ecosystem by biologically cumulative effect. Yitong county, an important grain production base with abundance of black soil, has been threatened by a gold mine in fields of the security of food production and environment. For the exploration of the pollution situation, the heavy metal contents in agricultural soil around the gold-mining area have been determined in this work. Firstly, a total of 91 samples were collected by field surveying, and the concentrations of 7 elements (As, Cd, Cr, Cu, Ni, Pb and Zn) in those soil samples were analyzed. After that, geoaccumulation index (Igeo), pollution index (PI) and potential ecological risk (PER) were selected as the evaluation indexes to assess heavy metal pollution. In addition, the varimax normalized rotation was applied to trace the source of pollution. The results indicate that the study area was contaminated on a moderate level.

Deep generative models such as the generative adversarial network (GAN) and the variational autoencoder (VAE) have obtained increasing attention in a wide variety of applications. Nevertheless, the existing methods cannot fully consider the inherent features of the spectral information, which leads to the applications being of low practical performance. In this article, in order to better handle this problem, a novel generative model named the conditional variational autoencoder with an adversarial training process (CVA2E) is proposed for hyperspectral imagery classification by combining variational inference and an adversarial training process in the spectral sample generation. Moreover, two penalty terms are added to promote the diversity and optimize the spectral shape features of the generated samples. The performance on three different real hyperspectral data sets confirms the superiority of the proposed method.

The increase in the spectral and spatial information of hyperspectral imagery poses challenges in classification due to the fact that spectral bands are highly correlated, training samples may be limited, and high resolution may increase intraclass difference and interclass similarity. In this paper, in order to better handle these problems, a Caps-TripleGAN framework is proposed by exploring the 1-D structure triple generative adversarial network (TripleGAN) for sample generation and integrating CapsNet for hyperspectral image classification. Moreover, spatial information is utilized to verify the learning capacity and discriminative ability of the Caps-TripleGAN framework. The experimental results obtained with three real hyperspectral data sets confirm that the proposed method outperforms most of the state-of-the-art methods.

The potential hazard of heavy metals in reclaimed mine soil has been attracted more and more attention. Hyperspectral inversion can be applied to predict the heavy metal content of the soil effectively. Three machine learning methods, Support Vector Machine (SVM), Random Forest (RF) and Extreme Learning Machine (ELM), are introduced in this paper, and then are compared with the Partial Least Squares (PLS) method. With the correlation analysis of heavy metal content and pretreatment spectral band, the models are constructed to predict the content of heavy metal in soil. The results show that the prediction results of machine learning methods are better than PLS, and ELM and RF are better than SVM. Analyzing the stability of the model, it can be found that the concentration of heavy metal samples will affect the prediction of ELM. Meanwhile, the stability of RF is the best than the other three models. RF algorithm has also the highest accuracy in the inversion of soil heavy metal research.

With the Geographic Information System (GIS) analytical tools and Remote Sensing technology, the main territory's Net Primary Productivity (NPP) in China from 2001 to 2010 is estimated based on CASA model. The analysis of the temporal and spatial variations in NPP shown that NPP in the southern regions shows increase trend after the first five years' decrease, in addition, the southern region of China generally has higher NPP as the northwest is significantly lower than other regions Moreover, the influence of climatic changes on the NPP's evolution represent that the NPP have different influent factors in different regions and periods.

In the process of hyperspectral image classification, the number of training samples is the key problem in improvement of classification performance. However, finding training samples are generally difficult and time-consuming. In this paper, we propose a novel semi-supervised approach and attempt to utilize unlabeled samples to improve classification accuracy. Specifically, active learning (AL) and multi-scale homogeneity (MSH) are integrated in a tri_training framework, where unlabeled samples are selected using AL and the labels of unlabeled samples are predicted from rough classification results with consideration of spatial neighborhood information. The MSH method is utilized to process the classification results to generate the final classification results. Moreover, we propose a novel diversity measure to select optimal classifier combination from different classifiers including support vector machine (SVM), multinomial logistic regression (MLR), extreme learning machine (ELM), k-nearest neighbor (KNN), and random forest (RF) etc. Experiments on two real hyperspectral data indicate that the new diversity measure can select an optimal classifier combination, and the proposed approach can effectively improve classification performance.

This paper presents a new approach for class-oriented spectral partitioning for hyperspectral image classification. First, without empirical information, we automatically search the spectral bands that correspond to a specific class by using different band selection approaches. Then, the obtained class-oriented spectral partitions are used respectively as the input of a group of classifiers, the results of which are combined together to generate a final one by a multiple classifier system. Our experimental results, conducted with the well-known Indians Pines test site hyperspectral image collected by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) in NW Indiana, suggest that our presented spectral partitioning method leads to competitive results when compared with other state-of-the-art approaches.

Today's WLANs are struggling to provide desirable features like high efficiency, fairness and QoS because of the use of Distributed Coordination Function (DCF). In this paper we present OpenTDMF, an architecture to enable TDMA on commodity WLAN devices. Our hope is to provide the desirable features without entirely rebuilding the WLAN infrastructure. OpenTDMF is inspired by and architecturally similar to Software Defined Networking (SDN). Specifically, we leverage the backhaul of WLAN to coordinate all the stations for channel access. This fine-grained coordination is performed in a decoupled control plane which includes a central controller and programmable APs. To realize OpenTDMF on commodity WLAN devices, we develop several novel techniques to achieve μs-level time synchronization among all the APs. We also enable AP-triggered uplink transmission so that all the transmissions in the WLAN can be determined. We implemented a prototype of OpenTDMF based on commodity WLAN devices. Empirical results validate the OpenTDMF design and demonstrate its benefits.

Similar to TV bands, the guardband frequencies are not occupied therefore are whitespace that potentially allows additional communication activities. Considering the difference to TV whitespace, we propose independent communication for guardband whitespace. In this paper, we present the Pilotfish system which realizes independent communication and turns guardband whitespace into new communication channels. To address the big challenges of interference mitigation, we employ novel PHY design which includes specially customized FBMC and an Nulled Decoding technique to null the strong background signal in guardbands. We implemented Pilotfish using software radio system. Empirical evaluation results validate the Pilotfish design in both PHY and MAC.

In this letter, a semisupervised block-sparse graph is proposed for discriminant analysis of hyperspectral imagery. To overcome the difficulty of not having enough training samples in the previously developed block-sparse graph approach, unlabeled samples are selected to participate in graph construction. Both sparse and collaborative representations are used for unlabeled sample selection. The experimental results demonstrate that the proposed semisupervised block-sparse graph can significantly outperform the supervised version with limited training samples. The sparse and collaborative representation-based selection methods perform comparably with the collaborative version requiring much lower computational cost.

Retransmissions reduce the efficiency of data communication in wireless networks because of: (i) per-retransmission packet headers, (ii) contention overhead on every retransmission, and (iii) redundant bits in every retransmission. In fact, every retransmission nearly doubles the time to successfully deliver the packet. To improve spectrum efficiency in a lossy environment, we propose a new in-frame retransmission scheme using uACKs. Instead of waiting for the entire transmission to end before sending the ACK, the receiver sends smaller uACKs for every few symbols, on a separate narrow feedback channel. Based on these uACKs, the sender only retransmits the lost symbols after the last data symbol in the frame, thereby adaptively changing the frame size to ensure it is successfully delivered. We have implemented uACK on the Sora platform. Experiments with our prototype validate the feasibility of symbol-level uACK . By significantly reducing the retransmistion overhead, the sender is able to aggressively use higher data rate for a lossy link. Both improve the overall network efficiency. Our experimental results from a controlled environment and an 9-node software radio testbed show that uACK can have up to 140% throughput gain over 802.11g and up to 60% gain over the best known retransmission scheme.

The goal of data-center networking is to interconnect a large number of server machines with low equipment cost while providing high network capacity and high bisection width. It is well understood that the current practice where servers are connected by a tree hierarchy of network switches cannot meet these requirements. In this paper, we explore a new server-interconnection structure. We observe that the commodity server machines used in today's data centers usually come with two built-in Ethernet ports, one for network connection and the other left for backup purposes. We believe that if both ports are actively used in network connections, we can build a scalable, cost-effective interconnection structure without either the expensive higher-level large switches or any additional hardware on servers. We design such a networking structure called FiConn. Although the server node degree is only 2 in this structure, we have proven that FiConn is highly scalable to encompass hundreds of thousands of servers with low diameter and high bisection width. We have developed a low-overhead traffic-aware routing mechanism to improve effective link utilization based on dynamic traffic state. We have also proposed how to incrementally deploy FiConn.

The goal of data center networking is to interconnect a large number of server machines with low equipment cost, high and balanced network capacity, and robustness to link/server faults. It is well understood that, the current practice where servers are connected by a tree hierarchy of network switches cannot meet these requirements (Fares et al., 2008 and Guo et al., 2008). In this paper, we explore a new server-interconnection structure. We observe that the commodity server machines used in today's data centers usually come with two built-in Ethernet ports, one for network connection and the other left for backup purpose. We believe that, if both ports are actively used in network connections, we can build a low-cost interconnection structure without the expensive higher-level large switches. Our new network design, called FiConn, utilizes both ports and only the low-end commodity switches to form a scalable and highly effective structure. Although the server node degree is only two in this structure, we have proven that FiConn is highly scalable to encompass hundreds of thousands of servers with low diameter and high bisection width. The routing mechanism in FiConn balances different levels of links. We have further developed a low-overhead traffic-aware routing mechanism to improve effective link utilization based on dynamic traffic state. Simulation results have demonstrated that the routing mechanisms indeed achieve high networking throughput.

It has been a challenging problem to support VoIP-type delay sensitive applications in an 802.11 wireless LAN, because the standard handoff procedure implemented in many current 802.11 products occurs a delay deem unacceptable to VoIP users. To reduce this delay, we have developed a fast handoff scheme called Proactive Scan. It employs two new techniques. The first is to decouple the time-consuming channel scan from the actual handoff, and to eliminate channel scan delay by doing scan early and interleaving it with ongoing traffic in a non-intrusive way. The second technique is a smart trigger that takes into account both uplink and downlink quality and explicitly addresses the link asymmetry which has yet not been touched in previous work. Through implementation and experimentation study, we have shown that Proactive Scan does provide fast handoff and satisfactory performance to VoIP applications. Further, it is a software-only client-only solution that any mobile device can use in any existing 802.11 networks.

Transferring voice traffic over multihop wireless mesh network (WMN) based on IEEE 802.11 is a challenging job. One main reason is that many small VoIP packets introduce significant overhead, and therefore greatly limits the capacity of VoIP support in WMN. To alleviate this inefficiency, in this paper, we argue for aggregation at routing layer, termed as path aggregation. With path aggregation, flows with similar directions choose routes that share as many as common links. This way, small VoIP packets from different flows can be effectively multiplexed into large packets over these common links and thus increases the utilization of wireless channel. We introduce an important performance metric, Channel Time Cost (CTC), to reflex the channel time used to deliver a voice flow. We formulate the path aggregation as an optimization problem that minimizes the sum of CTC of all flows over the network. We further propose a greedy distributed heuristic algorithm to yield approximate solution. We conduct extensive packet-level simulations and the results confirm that our path aggregation algorithm can effectively improve the VoIP capacity and increase the total throughput of WMN where mixed VoIP traffic and other Best Effort traffic co-exist.

This paper considers the problem of how to maximize throughput in multi-radio multi-channel wireless mesh networks. With mathematical model based on radio and radioto-radio link, we introduce a scheduling graph and show that the feasibility problem of time fraction vector is equal to the problem of whether the scheduling graph is [M]-colorable, where M is the number of slots in one period. We use this equivalence property to derive a sufficient condition of feasibility, and then, using this sufficient condition, we mathematically formulate the joint routing and channel assignment problem as a linear programming problem. Finally, we use vertex coloring to get a feasible schedule and lift the resulting flows. We prove that the optimality gap is above a constant factor. The numeric results demonstrate the effectiveness of our proposed algorithm.

The newly emerging wireless backhaul network has fundamental difficulties in supporting Voice over IP (VoIP) applications due to the MAC overheads introduced by huge amounts of small packets. Packet aggregation is a promising approach to mitigate these overheads. However, previous approaches to such problems are often stringent, not adaptive to the change of channel conditions. They are operated by each TAP (Transit Access Point) separately without any coordination in the use of shared channels. As a result, they fail to ensure the VoIP quality in terms of delay and loss. The major contribution of this paper is the proposal of a coordinated aggregation algorithm, which is adaptive and distributed. By coordinating with neighboring TAPs, the proposed algorithm is able to assign an appropriate aggregation rate to each TAP, aiming at better channel utilization and lower packet loss and delay. We evaluate this design by comprehensive analysis and simulations. The simulation results show that our algorithm significantly improves the VoIP capacity in wireless backhaul networks and outperforms existing aggregation algorithms.

Abstract—Many applications require fast data transfer over high speed and long distance networks. However, standard TCP fails to fully utilize the network capacity due to the limitation in its conservative congestion control (CC) algorithm. Some works have been proposed to improve the connection’s throughput by adopting more aggressive loss-based CC algorithms. These algorithms, although can effectively improve the link utilization, have the weakness of poor RTT fairness. Further, they may severely decrease the performance of regular TCP flows that traverse the same network path. On the other hand, pure delay-based approaches that improve the throughput in high-speed networks may not work well when the traffic is mixed with both delaybased and greedy loss-based flows. In this paper, we propose a novel Compound TCP (CTCP) approach, which is a synergy of delay-based and loss-based approach. Specifically, we add a scalable delay-based component into the standard TCP Reno congestion avoidance algorithm (a.k.a., the loss-based component). The sending rate of CTCP is controlled by both components. This new delay-based component can rapidly increase sending rate when network path is under utilized, but gracefully retreat in a busy network when bottleneck queue is built. Augmented with this delay-based component, CTCP provides very good bandwidth scalability with improved RTT fairness, and at the same time achieves good TCP-fairness, irrelevant to the windows size. We developed an analytical model of CTCP and implemented it on the Windows operating system. Our analysis and experiment results verify the properties of CTCP. Index Terms—TCP performance, delay-based congestion control, high speed network I.

In this paper, we first identify several challenges in designing a joint channel assignment and routing (JCAR) protocol in heterogeneous multiradio multichannel multihop wireless networks (M3WNs) using commercial hardware [e.g., IEEE 802.11 Network Interface Card (NIC)]. We then propose a novel software solution, called Layer 2.5 JCAR, which resides between the MAC layer and routing layer. JCAR jointly coordinates the channel selection on each wireless interface and the route selection among interfaces based on the traffic information measured and exchanged among the two-hop neighbors. Since interference is one of the major factors that constrain the performance in a M3WN, in this paper, we introduce an important channel cost metric (CCM) which actually reflects the interference cost and is defined as the sum of expected transmission time weighted by the channel utilization over all interfering channels (for each node). In CCM, both the interference and the diverse channel characteristics are taken into account. An expression for CCM is derived in terms of equivalent fraction of air time by explicitly taking the radio heterogeneity into consideration. Using CCM as one of the key performance measures, we propose a distributed algorithm (heuristic) that produces near-optimal JCAR solution. To evaluate the efficacy of our heuristics, we conduct extensive simulations using the network simulator NS2. To demonstrate implementation feasibility, we conducted various experiments for the proposed distributed JCAR algorithm on a multihop wireless network testbed with nine wireless nodes, each is equipped with single/multiple 802.11a/g cards. Both experimental and simulation results demonstrate the effectiveness and implementation easiness of our proposed software solution

TCP frequently experiences spurious timeouts in wireless wide-area networks (WWAN), because of the large delay variance in wireless links. Due to the retransmission ambiguity, the conventional TCP sender fails to identify spurious timeouts and therefore unnecessarily decreases the throughput. Several schemes have been proposed to detect the spurious timeouts, but they have downsides like: 1) undesirable overhead; 2) fail to detect in certain situations; and 3) may bring new security concerns. In this paper, we present a novel spurious timeout detection algorithm, named STODER. STODER exploits TCP repacketization to detect spurious timeouts without any additional overhead in data packets. Since STODER relies only on the basic TCP reliable semantics, unlike existing works, it does not bring new attacks from misbehaving receivers. Our simulations show it yields improved performance comparing to existing schemes in general situations

In this paper, we present a novel Explicit Wireless Congestion Control Protocol (EWCCP) for stationary multihop wireless networks. By exploiting explicit coordination and multi-bit explicit feedback from routers, EWCCP gains fine-grain control and is robust to the dynamics of the wireless channel. EWCCP stabilizes at a lower but more optimal sending window regarding to TCP, and achieves low buffer occupation and low delay. With explicit coordination, EWCCP allocates resource fairly among flows that compete for the shared channel. With an analytical model, we show that EWCCP achieves proportional fairness in multi-hop wireless networks.

Numerous mobility solutions have been proposed in the past, but none of them have been widely deployed today. To address the deployment difficulty in previous work, we propose an end-system-based mobility solution for IPv6 (EMIPv6). In our design, we adhere to the end-to-end principle (Saltzer et al., 1984) by directly performing connection maintenance and data packet delivery between the two communicating hosts. And we leverage distributed hash table-based peer-to-peer (P2P) systems to carry out self-organized, scalable, and robust name lookup for mobile hosts. When the mobility messages such as address updates cannot be delivered directly between the end hosts (e.g., due to firewalls, network address translators, or simultaneous movement), we propose a distributed subscription/notification (S/N) service on top of the previously introduced P2P overlay to deliver them. This leads to a complete end-system solution with small handoff latency and efficient packet delivery. Our simulation results showed that our scheme achieves small name resolution latency by considering host heterogeneity into the design. We have implemented EMIPv6-based end-systems. The experiments in our testbed demonstrated that a complete end-system-based mobility solution is technically feasible and is easy to deploy in the real world without the need of introducing new network components.

Ad hoc communication among wireless enabled smart devices is increasingly playing an important role in coordinating distributed applications. Existing ad hoc routing algorithms usually assume the existence of end-to-end path connecting communication nodes. However, due to the power limitation, radio coverage and geography distribution of cooperating nodes, in some scenarios this assumption is unlikely to be valid. We address the issue of data routing in partially connected ad hoc networks. In this situation, data propagation is achieved via mainly pair-wise communication between any two nodes when they are in vicinity. We propose a novel routing framework named shortest expected path routing (SEPR). Instead of blindly flooding messages in the network, SEPR builds up a stochastic model of the ad hoc network and maintains it in a distributed way. A new routing metric, called expected path length, is proposed. By guiding messages flow to shortest expected path nodes, our approach dramatically reduces the number of unnecessary message copies as well as increases the message delivering rate. With a simulation, we evaluate the effectiveness and the efficiency of our approach.