The structure of topology underpins much of the research on performance and robustness, yet available topology data are typically scarce, necessitating the generation of synthetic graphs with desired properties for testing or release. Prior diffusion-based approaches either embed conditions into the diffusion model, requiring retraining for each attribute and hindering real-time applicability, or use classifier-based guidance post-training, which does not account for topology scale and practical constraints. In this paper, we show from a discrete perspective that gradients from a pre-trained graph?level classifier can be incorporated into the discrete reverse diffusion posterior to steer generation toward specified structural properties. Based on this insight, we propose Classifier-guided Conditional Topology Generation with Persistent Homology (CoPHo), which builds a persistent homology filtration over intermediate graphs and interprets features as guidance signals that steer generation toward the desired properties at each denoising step. Experiments on four generic/network datasets demonstrate that CoPHo outperforms existing methods at matching target metrics, and we further validate its transferability on the QM9 molecular dataset. The code is available at https://github.com/Lrbomchz/CoPHo.

Distributed training has become a cornerstone of large-scale deep learning, yet gradient synchronization remains a critical bottleneck—particularly in high packet loss environments such as wide-area networks (WANs), where frequent retransmissions lead to long-tail latency and degraded training performance. In this paper, we propose a novel approach that leverages spatiotemporal gradient importance modeling to optimize both the transmission protocol and switch queue dropping strategy. Our method dynamically evaluates the importance of each gradient based on its position in the model layers and the current training stage, enabling adaptive packet loss control. In terms of implementation, we integrate a hybrid UDP/TCP transmission protocol: UDP ensures high-throughput gradient delivery, while TCP carries control signals to provide reliable loss feedback. Additionally, our system uses DSCP marking to map gradients to different switch queues, with each queue configured with a distinct WRED (Weighted Random Early Detection) loss threshold that is periodically updated based on the dynamically assessed gradient importance. Extensive experiments demonstrate that our model effectively captures the intrinsic spatiotemporal variations in gradient behavior and significantly reduces long-tail latency while maintaining model accuracy. This provides a robust framework for optimizing gradient synchronization and network resource allocation in distributed deep learning systems.

Segment Routing over IPv6 (SRv6) is a promising source routing solution with wide-ranging applications in the field of Traffic Engineering. By adding SR tags into IPv6 packets, traffic can be directed to various SR segments, effectively distributing traffic. However, upgrading all network nodes to SRv6 nodes simultaneously is impractical. This paper addresses the incremental deployment of SRv6 from traffic engineering, aiming to minimize MLU within the network. We introduce the theory of celestial equilibrium and model the problem as a celestial equilibrium-like model with a global distribution of SR nodes and their corresponding areas of influence. To address this problem model, we propose a novel algorithm based on the EM algorithm, EM-SRTE. In our proposed framework, step E leverages a reinforcement learning algorithm combined with a self-attention module for graph learning to optimize the selection of SR nodes. Meanwhile, step M utilizes a similar algorithm to optimize the region range of SR nodes. Experimental results using publicly available datasets demonstrate that our model outperforms state-of-the-art baselines.

Advancements in distributed machine learning have led to a demand for engineers with knowledge in AI, hardware, and networking. Research aims to automate deployment based on user intent to improve development efficiency. Large Language Models (LLMs) assist in translating user intent for resource allocation but face challenges in translation accuracy and contextual integrity. The Chain-of-Thought (CoT) improves LLM reasoning by breaking down problems into sequential steps. However, the reasoning steps and dependencies between the model requirements and the computation and network configurations are complex, requiring the selection of an appropriate thought path to construct the CoT framework. To address these issues, we propose a computation and network resource deployment intent translation framework based on CoT reasoning and create a benchmark for user intent in distributed learning. This framework translates user intent into hardware and network configurations, using LLMs to optimize translation accuracy through logical reasoning. Experiments on four LLM models show significant accuracy improvements with CoT compared to traditional prompts. The feasibility of our framework has been validated through its implementation and testing on a real-world testbed.

Large-scale incast traffic generated by AI training tasks poses significant challenges to RDMA networks. Existing congestion control mechanisms, such as Artificial Intelligence ECN (AI ECN) and fixed-ratio PFC, struggle to mitigate frequent PFC pauses caused by ECN's delayed feedback. This paper proposes DSCC, a synergistic algorithm of PFC and ECN. Based on the AI ECN algorithm, DSCC adjusts the PFC threshold according to the incast degree. The adjustment process adheres to the threshold constraints of PFC and ECN. Experiments show that DSCC can significantly improve network performance.

The increasing demand for resource-efficient AI training has positioned Mobile Edge Computing (MEC) as a pivotal component in distributed machine learning tasks. Traditional scheduling methods, however, often fail to account for the inherent characteristics of training tasks, such as periodicity and task correlation, leading to sub-optimal resource utilization. To address these limitations, we propose the Multi-Graph to Graph Scheduler (MG2GS), a novel framework designed to optimize resource allocation and scheduling in MEC environments. MG2GS employs a graph neural network-based feature extractor to capture the spatiotemporal availability of network resources, while a reinforcement learning-based scheduler ensures optimal task scheduling decisions. By incorporating task structure and long-term resource distribution, MG2GS enhances resource utilization by more than 20% compared to existing methods. Simulation results demonstrate its effectiveness in increasing the number of scheduled tasks and improving overall resource efficiency for distributed AI model training.

Federated learning (FL) has made significant advancements in recent years. However, its efficiency on unstable distributed data remains a critical challenge. This stems from oversights in existing FL frameworks regarding the instability of global and client private data distributions or their assumption of stable distributions over time. To address this challenge, we present HybridCom, an efficient FL framework on unstable data. The core concept of HybridCom is to adjust the client participation probability based on their contributions to adapting the global model to data distribution changes. This is achieved through a hybrid contribution indicator that includes a performance-based client-side indicator and a gradient-based server-side indicator. Based on the results of the contribution indicator, HybridCom integrates a probabilistic participation controller to dynamically adjust the participation probability of each client during the FL process. By utilizing Hybrid-Com, clients undergoing data distribution changes that are not perceived by the global model have a higher probability of participating. This makes HybridCom more efficient in adapting to unstable data distributions. The experimental results demonstrate that HybridCom surpasses the baseline models, achieving an approximate 1.3% improvement across diverse simulation settings and communication resource constraints.

Federated Learning (FL) enables collaborative training of a global model without exposing raw data by aggregating local updates from clients. However, the convergence efficiency on non-i.i.d. data remains challenging, leading to performance loss and resource bottleneck. Meanwhile, the nature of non-i.i.d. challenge are not yet fully understood. In this paper, we first reveal that non-i.i.d. data leads to server-side multi-objective aggregation conflict challenge which hampers the convergence efficiency. We then propose Federated Gradient Rotation Unit (FGRU), a simple yet general approach to mitigate this challenge by deliberately aligning optimization trajectories across clients. FGRU is a server-side plugin that rotates gradients to other before aggregation. On a series of challenging non-i.i.d. FL tasks, FGRU leads to significant gains in convergence efficiency and performance. Experimental results demonstrate that FGRU improves inference accuracy by approximately 3-5% and accelerates convergence by 2-3X on simulated non-i.i.d. data using MNIST, CIFAR-10, and Fashion-MNIST datasets. The results also show that FGRU is model agnostic and can be combined with existing non-i.i.d. FL frameworks such as SCAFFOLD and FedProx to further improve performance.

In the age of 5G and the advent of 6G technology, meeting various applications' diverse and stringent QoS requirements has gained greater urgency. Most existing works only address this problem by optimizing network resource allocation or routing generation, ignoring their mutual influence. In this paper, we jointly optimize resource allocation and routing generation while comprehensively considering the mutual influence and demonstrating that joint optimization is a bilevel optimization problem. We propose BIJO, consisting of two-level agents, to explore using DRL to address the bilevel optimization problem. The upper agent allocates resources for different types of services and the lower agents choose the forwarding path for each type of service. The two-level agents are optimized interactively and iteratively. Extensive experiments on Mininet confirm that BIJO provides a win-win outcome, where various requirements are met as much as possible and network resources are utilized evenly.

Point cloud segmentation is crucial for various industrial applications, such as autonomous driving and robotics. Recent developments underscore the significant potential of transformer models in this field. However, existing attention mechanisms apply the same feature learning paradigm for all points equally, ignoring the considerable size differences among objects in a scene. To rectify this, we introduce the field-aware transformer (FAT), engineered to tailor effective receptive fields to objects of varying sizes. Our FAT achieves field-aware learning through two primary components: the multigranularity attention (MGA) scheme and the reattention module. The MGA scheme is proficient in aggregating tokens from distant areas while preserving multiscale features within each attention layer. The reattention module dynamically adjusts the attention scores to the fine- and coarse-grained features output by MGA for each point. Extensive experimental results underscore the effectiveness and efficiency of our FAT, which delivers state-of-the-art performance on both the stanford 3D indoor scene dataset (S3DIS) and ScanNetV2 datasets.

At present, with the rapid advancement of mul-timedia technology, more and more multimodal applications have emerged. The data communication of multimodal applications involves multiple modalities, and the transmitted data has deadline requirements and block transmission characteristics. However, existing transport layer protocols cannot meet the transmission requirements of applications by perceiving the data attributes and it is difficult to avoid high-priority modalities from excessively seizing transmission resources, resulting in transmission starvation in other modalities. Therefore, this paper considers the data blocks and their transmission requirements of the upper layer application as independent data objects and proposes an object multipath transmission scheduling al-gorithm (OMTS) that can consider the fairness of multimodal transmission. OMTS uses reinforcement learning algorithms to comprehensively consider the transmission requirements of data objects and the quality of multipath network transmission, to determine the transmission order and path allocation strategy of objects. In addition, we also design a model structure that separates scheduling and learning, allowing the algorithm to learn more valuable scheduling strategies through continuous interaction with the environment. The comparative experiment of data transmission through the network simulation environment shows that OMTS is superior to existing scheduling algorithms.

Transformer models have achieved promising performances in point cloud segmentation. However, most existing attention schemes provide the same feature learning paradigm for all points equally and overlook the enormous difference in size among scene objects. In this paper, we propose the Field-Aware Transformer (FAT) that adjusts the attentive receptive fields for objects of different sizes. Our FAT achieves field-aware learning via two steps: introduce multi-granularity features to each attention layer and allow each point to choose its attentive fields adaptively. It contains two key designs: the Multi-Granularity Attention (MGA) scheme and the Re-Attention module. Extensive experimental results demonstrate that FAT achieves state-of-the-art performances on S3DIS [1] and ScanNetV2 [2] datasets.

Gland segmentation is crucial for computer-aided diagnosis of adenocarcinoma. However, Topologically Critical Areas (TCAs), such as background tissues between two adjacent glands, can easily cause under- or over-connection of gland topological structures that may lead to the opposite diagnostic of the malignancy degree. Therefore, we provide a novel perspective for gland segmentation by incorporating gland connectivity information to locate critical errors within TCAs. We propose a Connectivity-Aware Cascade framework (CoCa) that explicitly encodes gland connectivity information into the network to locate all connectivity errors during training and then leverage attention operations to focus on these errors. Since under- or over-connected glands can change the Betti number (e.g., number of connected components) of glands, we design a Connectivity Refinement Module (CRM) to compare the Betti number of each gland to locate connectivity errors. We propose CoCa-Net to mine the topological relations among different biomedical entities to guide gland prediction. We also use contrastive learning to separate pixel embeddings of different classes within TCAs through our connectivity-aware hard example sampling strategy. Extensive experiments on the GlaS and CRAG datasets demonstrate the effectiveness of CoCa over state-of-the-art methods.

As High-speed rail becomes a popular way to travel, users have a high demand for streaming services. In High-speed rail scenarios, users move fast and base stations handover frequently. Most of the existing network bandwidth prediction algorithms and bitrate selection algorithms are proposed based on low-speed scenarios. These algorithms are difficult to adapt to high-speed mobile scenarios. To solve this problem, this paper proposes an adaptive streaming media transmission method using edge computing, High-speed rail status and cross-layer information (EHCI) in the 5G network environment. Firstly, a QoE model and a coordinated transmission architecture using edge computing, High-speed rail operation status and cross-layer information are proposed. Secondly, a media transcoding algorithm and rate selection algorithm are proposed. Finally, the simulation experiment is carried out in this paper. Simulation results demonstrate that the method proposed in this paper can well improve the QoE of High-speed rail passengers, and is helpful to the study of the optimized transmission of streaming media in High-speed rail scenarios.

Along with the ever-increasing amount of data generated from industrial devices, the cross domain [also known as autonomous systems (ASs)] data transmission problem has attracted more and more attention in the Industrial Internet of Things (IIoT). As mature and widely used interdomain routing protocols, border gateway protocol-based solutions often take the number of domains (i.e., AS hops) of each path as a criterion to make routing decisions, which is simple and effective. However, such protocols can only meet the reachability requirements while ignoring the performance requirements. That is, the path with the minimum AS hops will be selected to carry flows, even if the actual performance of this path does not meet the transmission requirements due to the unawareness of intradomain information on that path. But it is not impractical to directly access intradomain information for making better routing decisions given data privacy concerns. In this article, we propose M-DIT, which can make interdomain routing decisions with the assistance of desensitized intradomain information for multiple-requirement transmissions. To do so, we design a homomorphic encrypted-based private number comparison scheme to export intradomain information securely and, thus, assist in routing decisions. The results of some experiments based on five real topologies (ATMnet,Claranet,Compuserve,NSFnet, andPeer1) with thousands of interdomain flows demonstrate that M-DIT reduced flow completion time by about 60% or selected high bandwidth paths flexibly for interdomain routing for IIoT scenarios.

Along with the ever-increasing amount of data generated from edge networks, cross domain (also known as Autonomous Systems, AS) transmission problem has attracted more and more attention. As mature and widely used inter-domain routing protocols, BGP-based solutions often use the number of domains (i.e. AS hops) of each path to make inter-domain routing decisions, which is simple and effective, but usually can not get the optimal routing results due to the lack of real state/information within ASes. These protocols choose the path with less AS hops as the forwarding path, even if the total latency or cost of the domains on this path is higher. While to solve this problem, directly access to intra-domain information as the assistance to make routing decisions is impractical due to data privacy.In this paper, we propose DIT, which makes near-optimal inter-domain routing decisions with desensitized intra-domain information. To do so, we design a homomorphic encrypted-based private number comparison scheme to export intra-domain information securely and thus assist in routing decisions. We conduct a series of experiments according to five real network topologies with nearly 900 simulated flows, and the results show that DIT reduces the number of forwarding hops by about 45% in average and reduces flow completion time by about 60%.

Graph-based approaches are successful for histology image classification tasks but still face many challenges, such as: 1) the lack of nuclei-level labels and the significant variations between histology images make it extremely difficult to extract discriminative high-level nuclei features like nuclei type, texture and micro-environment; 2) graph-based approaches cannot handle large-scale cell graph nodes typically contained in histology images; and 3) graph neural networks (GNNs) struggle to learn the long-range dependency of cell graphs. To address the above challenges, we propose a scalable graph-based framework for multi-organ histology image classification. We develop a two-step masked nuclei patches supervised training approach to extract discriminative high-level nuclei features for histology images without nuclei-level labels. Additionally, we introduce a nuclei sampling strategy to make our graph-based framework scalable for large-scale cell graphs. Furthermore, we proposeHierArchicalTransformer Graph NeuralNetwork (HAT-Net+) for cell graph classi- fications. HAT-Net+ adopts Transformer to model the long-range dependency of cell graphs and a parameter-free approach to adaptively fuse different hierarchical graph representations of each layer. We achieved the state-of-the-art results on four public histology image classification datasets: CRC dataset (100%), Extended CRC dataset (98%), UZH dataset (96.9%) and BACH dataset (88%). Unlike other methods, our approach can be used in various histology image classification tasks, even for images without nuclei-level labels, indicating its potential in cancer diagnosis. The code is available athttps://github.com/suyouooooo/HAT-Net.

With the development of 5G access technology and the corresponding explosive growth of user requests, service providers have to activate more and more physical machines (PM) in cloud datacenters. This simple expansion of PMs results in not only low utilization of servers but also high maintenance cost. Existing researches that try to release unnecessary servers by migrating VMs always face a severe challenge-the large searching space for the optimal VM placement solution. In this paper, we prose a two-stage variable neighborhood searching (STVNS) algorithm, named FreeVM, which can significantly reduce the number of occupied servers. FreeVM works harmoniously with all kinds of VM placement schemes under various scenarios. We conducted an extensive series of experiments using real traces, and the results show that FreeVM can release at least 15% physical machines compared with existing solutions.

Since the advent of the Internet, IP addresses have been the core of the Internet. However, with the rapid development of the Internet in recent years, IP addresses are facing more and more problems, such as address exhaustion, low packet efficiency and low flexibility. The reason is that IP addresses use a fixed-length design and lack extensibility. The New IP network architecture and addressing method were born to solve these problems. Based on this architecture, the addressing scheme adopts variable-length and structured addresses. The address space can be smoothly expanded according to the network scale without modifying the old network address configuration. But there are some challenges about New IP, and the greatest one lies in the route lookup of variable-length IP addresses. Content Addressable Memories (CAMs) are widely used in high speed routers to find matching routes for packets in a routing table. They enable the longest prefix matching on fixed-length addresses to be completed in a single clock cycle. However, they can not deal with New IP prefixes with variable lengths directly. In this paper, we propose a mechanism using Binary CAMs (BCAMs) and Ternary CAMs (TCAMs) to efficiently store New IP addresses and complete a route lookup in constant time. Moreover, we combine the hash scheme and CAMs matching scheme to shorten the extremely long New IP addresses and reduce TCAM storage space consumption. The simulation results show that our mechanism can provide high speed route lookup with low power consumption.

With the tremendous growth of video contents and mobility demands, there is a need to develop more personalized video services. Split-screen services, such as picture in picture become more and more popular. Furthermore, the user's viewing environment affects the user's quality of experience (QoE). Therefore, video transmission of split-screen services face several major challenges, such as to quantify the impact of environmental factors on user's QoE; how to assess the user's QoE of the split-screen services; how to choose the bit-rate of each video stream to maximize user's QoE of the split-screen services. To address these challenges, in the paper, an environment-aware adaptive transmission (EAAT) scheme for split-screen video streaming is first presented. Then, we introduce a mathematical model for characterizing user's QoE to be affected by environmental factors in the proposed EAAT. In the model, the QoE of user's relationship with the viewing environment is proposed. Based on the model, a problem of maximizing user's QoE is formulated, and we develop a heuristic algorithm to solve the optimization problem. In addition, we conduct various trace-bandwidth experiments to rigorously evaluate the proposed EAAT scheme in different network environments, and show that EAAT can enrich the video quality while saving network resources.

Due to the low cost, high bandwidth and compatibility of Ethernet, Ethernet-based fronthaul has been utilized to carry sampled radio frequency (RF) signals from radio equipment (RE) to the radio equipment controller (REC). Meeting the stringent performance requirements regarding jitter for the Common Public Radio Interface (CPRI) over Ethernet is challenging. In this paper, a time division multiplexing (TDM) slot-based scheduling (TSS) algorithm is proposed to minimize the jitter; this algorithm consists of two main modules: slot greedy allocation and low-delay collaboration. Specifically, low-complexity slot greedy allocation aims to solve the NP-hard problem caused by the optimal slot allocation, and the low-delay collaboration among different switches is employed to avoid the large delays caused by a strict slot assignment. In the Ethernet-based fronthaul network, the simulation results demonstrate that, compared with the conventional benchmark algorithm, the TSS algorithm ensures lower jitter, and this significant decrease is achieved without incurring a large delay.

Time-sensitive Networking (TSN) has been extensively utilized to enable the transport of time-sensitive fronthaul flows in Ethernet-bridged networks. However, recently emerged ultra-low delay and jitter requirements of the TSN pose great challenges to Common Public Radio Interface (CPRI) over Ethernet. Considering this, we propose a Deterministic Transmittable Time-Based Asynchronous Scheduler (DTT-BAS) in this paper. The DTT- BAS uses the deterministic transmittable timestamp as the core scheduling mechanism, which numerically compares the timestamps to optimize the flow scheduling in the network. For the fronthaul scenario, simulation results validate that the DTT-BAS achieves a high probability of zero jitter and reduces the influence of clock skews with a delay performance close to IEEE 802.1CM frame preemption algorithm.

It's a great challenge to offer a fine-grained and accurate air quality monitoring service in urban areas limited to the cost of the professional facilities. With the development of the wireless sensor networks (WSNs), it brings new opportunity to achieve this goal at low cost. However, WSNs are quite different on temporal and spatial distribution, some WSNs even have irregular real-time features, which makes it a hard problem to use the data collected from different WSNs to achieve the same goal. In this paper, we propose a framework for air quality estimation based on multi-source heterogeneous data collected from WSNs. We collect five kinds of data from different sources in real world, including the data with irregular real-time features. We divide the data sets into three sub classifiers to make the analysis and get the final results with an extreme learning machine (ELM) based multilayer perceptron model. The results show that our method outperforms other methods, and the precision of classification can be 90.8%.

With the tremendous growth of video contents and mobility demands, there is a need to develop more personalized video services. Split-screen service, e.g., picture in picture, becomes more and more popular. However, the video transmission of split-screen service still faces several major challenges, one of which is how to assess the users' quality of experience (QoE) of the split-screen service. There may be multiple videos for different resolutions displayed on one screen. A consistent way is needed to assess the users' QoE of the split-screen service. The second challenge is how to choose bitrate of each video stream to maximize users' QoE of the split-screen service. Multiple video streams are transmitted to user over resource-constrained wireless networks. Therefore, it is a difficult issue to select an appropriate bitrate for every video streaming. To address these challenges, in this paper, we first present a dynamic adaptive video transmission scheme (DAVTS) based on MPEG Media Transport (MMT) protocol. Then we introduce a novel mathematical model for characterizing users' QoE to be affected by some factors in the proposed DAVTS. In the model, the objective method of QoE assessment of split-screen service is proposed. Based on the model a problem of maximum income in QoE of users is considered. We use a transmission bitrate selection (TBS) algorithm to solve the optimization problem. Simulation results demonstrate that DAVTS can enrich the video quality while reduce video jitters.

This paper studies how to extract and group essential nodes to guarantee loop freedom during the update of the network, while minimizing update time. We propose a model to establish the relationships of loops which may arise during the updating procedure. Given the model, we design a heuristic algorithm, with the recursive optimizing for the relationships, to extract essential nodes for the loop-free update, and shorten the updating time. The performance of our proposal has been examined and we verified that our algorithm would obviously reduce the update time.

Recent studies have shown that the flow table size of hardware SDN switch cannot match the number of concurrent flows. Combined SDN Forwarding Element (CFE), which comprises software switch and hardware switch, becomes an alternative approach for tackling this problem. Because software switch has lower lookup speed than hardware switch, different proportions of traffic allocated to software switches in CFE have different effects on the delay bounds of all flows entering CFE. As delay-guarantee is a nontrivial task for network providers, especially with the increasing number of delay-sensitive applications, a model to analyze the delay bound given a flow allocation in CFE is important. With the one-to-one correspondence between flow allocation and rules placement solution, the analytical model can be used to evaluate and compare rules placement solutions and provide a basis for designing better rules placement solution in CFE. In this paper, we propose an analytical model for CFE based on network calculus, and then validate this model through simulations in NS-3.

PM2.5 (particulate matter in the atmosphere with a diameter no more than 2.5 microns) in the air can cause great damage to human beings. It's a great challenge to offer a fine-grained and accurate PM2.5 monitoring service in urban areas as the required facilities are very expensive and huge. Since the PM2.5 has a significant scattering effect on visible light, the large-scale user-contributed image data collected by the mobile crowd sensing bring a new opportunity for understanding the urban PM2.5. After the analysis of image data, we find that several image features are very discriminative in PM2.5 inference. In this paper, we propose a fine-grained PM2.5 estimation method based on the random forest model without any PM2.5 measurement devices. We evaluate our approach with experiments based on five data sources: the meteorological data, the traffic data, the records from the monitoring sites, the POIs and the photos collected. The experimental results show that we have a high evaluation accuracy on PM2.5 estimation (precision: 0.875, recall: 0.872), which outperforms existing methods (Logistic, Naive Bayes, Random Tree, and BP ANN).

Dynamic adaptive streaming over HTTP (DASH) has been a research hotspot, and the current studies focus on the DASH transmission optimization using unicast mode. However, DASH streaming also could be transmitted by multicast mode, which could effectively reduce transmission resource consumption especially when multiple DASH clients request the same video program in parallel. In this paper, we propose the Hybrid Transmission strategies for DASH (HTD) in LTE network, which is considered both unicast and multicast modes for DASH. The optimization problem is formulated as a Mixed Binary Integer Programming (MBIP) problem, and a two-level greedy algorithm is proposed, which could improve the quality of experience (QoE) of wireless DASH users, and save the wireless resources in LTE network. Simulation results demonstrate that our scheme achieves better performance than traditional single transmission mode in the literature.

By separating the control and forwarding planes, Software-Defined networking (SDN) enables the forwarding paths to be flexibly controlled by the logically centralized controllers using the global network view. To introduce SDN into existing networks, it is necessary to upgrade traditional devices to SDN- enabled ones. However, due to the business, economic and management limitations, it is difficult to realize full SDN deployment. As a result, how to migrate existing devices to SDN-compliant ones becomes the obvious dilemma for every network operator. In this paper, we address this question from the network performance perspective, and study how to leverage the capability of SDN to maximize traffic flow that can be achieved in hybrid SDNs. We formulate the maximum flow problem in networks with partial SDN deployment, and develop a fast Fully Polynomial Time Approximation Scheme (FPTAS) for solving it. Simulation results using real topologies show that hybrid SDNs outperform traditional networks, and we can obtain a near optimal network performance when 50% of SDN nodes are deployed.

Location based services are getting increasingly popular in participatory sensing systems. They make use of location information on the mobile devices to support applications that improve personal health, object search, and entertainment. However, GPS positioning consumes a lot of energy, which can drain a mobile device's battery. Although WiFi localization and cell tower localization have been suggested as alternatives, they have lower localization accuracy and limited coverage. In this paper, we suggest a novel solution for multiple mobile devices to perform collaborative localization to reduce energy consumption and provide accurate localization. We divide the mobile devices into two groups, the aggregator group and the collector group. The aggregator group turns on their GPS periodically, while the collector group uses the locations of the aggregators to estimate their own locations. We formulate the aggregator set selection problem and propose two novel algorithms to minimize the energy consumption in collaborative localization. Simulations with real traces showed that our proposed solution can save up to 88% of the energy of the entire network.

The increasingly popular smartphones enable participatory sensing systems to collect location-based sensing data for different tasks. However, GPS positioning is very energy consuming, which could drain a mobile device's battery quickly. High energy consumption may threaten the participants and reduce the sustainability of the participatory sensing systems. In this paper, we propose a collaborative localization strategy with load balancing. Simulations with real traces showed that our proposed solution can save more than 80% of the energy consumption for localization in the entire network with load balancing.

Interactive multiview video streaming (IMVS) allows viewers to periodically switch viewpoint. Its user experience can be further enhanced by creating virtual views from neighboring coded views using view synthesis techniques. Dynamic adaptive streaming over HTTP (DASH) is a new standard that can adjust the quality of video streaming according to the network condition. In this paper, we propose an improved DASH-based IMVS scheme over wireless networks. The main contributions are twofold. First, our scheme allows virtual views to be generated at either the cloud-based server or the client, and can adaptively select the optimal approach based on the network condition and the cost of the cloud. Second, scalable video coding is used in our system. Simulations with the NS3 tool demonstrate the advantage of our proposed scheme over the existing approach with client-based view synthesis and single-layer video coding.

Recently, Dynamic Adaptive Streaming over HTTP (DASH) has attracted significant attention. In this paper, we consider DASH-based transmission of scalable videos in wireless broadband access networks (e.g., long-term evolution and WiMAX), and propose three methods to enhance the quality of experience of wireless DASH users. First, we design an improved mapping scheme from scalable video coding layers to DASH layers that can provide the desired bitrates, enhance the video end-to-end throughput, and reduce the HTTP communication overhead. Second, we develop a DASH-friendly scheduling and resource allocation algorithm by integrating the DASH-based media delivery and the radio-level adaptation via a cross-layer approach. It utilizes the characteristics of video content and scalable video coding, and greatly reduces the possibility of video playback interruption by considering the client buffer status. The optimization problem is formulated as a mixed binary integer programming problem, and is solved by a subgradient method. Finally, a DASH proxy-based bitrate stabilization algorithm is proposed to improve the video playback smoothness that can achieve the desired tradeoff between playback quality and stability. Simulations with the Qualnet tool demonstrate that our schemes achieve better performances than other methods in the literature.

Software Defined Networking (SDN) is an emerging networking paradigm that assumes a logically centralized control plane separated from the data plane. Despite all its advantages, separating the control and data planes introduces new challenges regarding resilient communications between the two. That is, disconnections between switches and their controllers could result in substantial packet loss and performance degradation. To achieve resilient control traffic forwarding, this paper investigates the protection of control traffic in SDNs with multiple controllers. We propose a control traffic protection scheme that combines both local rerouting and constrained reverse forwarding protections. This scheme enables switches to locally react to failures and redirect the control traffic to controllers by using standby backup forwarding options. Our goal is then to find a set of primary routes for control traffic, called protection control network, where as much control traffic as possible can benefit from the proposed protection scheme. We formulate the protection control network problem and develop an algorithm to solve it. Simulation results on real topologies show that our approach significantly improves the resilience of control traffic.

Recently dynamic adaptive streaming over HTTP (DASH) has gained significant attentions. In this paper, we study DASH-based transmission of scalable videos in wireless broadband access networks (e.g. LTE, WiMAX), and propose a DASH-friendly scheduling and resource allocation scheme (DFSRA) to enhance the Quality-of-Experience (QoE) of wireless DASH users. By integrating DASH-based media delivery and radio-level adaptation in a cross-layer manner, the scheme can determine the optimal wireless resource allocation. A gradient-based algorithm is proposed to solve the optimization problem, which has the following advantages over traditional algorithms: (1) The characteristics of video content and scalable video coding are utilized. (2) The client buffer status is considered to reduce video playback interruption and buffer overflow. Simulations with the Qualnet tool demonstrate that our scheme achieves better performance than existing methods in the literature.

In some practical scenarios such as tour guiding and children babysitting, one mobile device held by tour guides or parents need to know the distance and direction of another nearby mobile device held by tourist or children. However, to date, most existing pedestrian localization methods rely on a fixed external infrastructure, such as a global positioning system(GPS) or pre-deployed wifi access points to provide Localization service for mobile devices. Such methods are constrained either by limited GPS coverage or by complicated set-up procedures. We observe that, when two devices are moving, the change of their positions leads to the change of distance between them. Given the same movements, different relative locations between devices lead to different distance changes. Besides, the distance between and relative movement of devices can be measured by two phone-embedded sensors respectively. This motivates us to exploit the relative localization method by merely two mobile devices. In this work, we present Phone-Radar, which is an infrastructure-free device-to-device localization system. According to the propagation model of wireless signals, the change of distance between devices are modeled by the change of wireless signal strength between them. The movements of devices are recorded by the inertial sensors using step-counting method. We further study the relationship among the initial relative locations between the two devices, their relative movements and the change of received signal strength measurements. Moreover, we implement the proposed method and measure its performance under real world conditions. The testbed experiments show the efficiency of our proposed method.

Incentive strategy is important in participatory sensing, especially when the budget is limited, to decide how much and where the samples should be collected. Current auction-based incentive strategies purchase sensing data with lowest price requirements to maximize the amount of samples. However, such methods may lead to inaccurate sensing result after data interpolation, particularly for participants that are massing in certain subregions where the low-price sensing data are usually aggregated. In this paper, we introduce weighted entropy as a quantitative metric to evaluate the distribution of samples and find that the distribution of data samples is another important factor to the accuracy of sensing result. We further propose a greedy-based incentive strategy which considers both the amount and distribution of samples in data collection. Simulations with real datasets confirmed the impact of samples distribution to data accuracy and demonstrated the efficacy of our proposed incentive strategy.

Currently the increasing prevalence of multi-homing and traffic engineering leads to an explosive growth of the global routing table. It is well known that hierarchical addressing could improve the routing scalability. Hence, some proposals exploring the routing architecture for future Internet reuse the hierarchical addressing for the locator assignment. However, this may result in some ASes assigned too many prefixes which would consequently make hosts, routers, Internet Service Providers and Domain Name System faced with big challenges. By modeling the Internet AS-level topology using a hierarchical graph, we define the processes of prefix assignment and routing advertisement in different hierarchical addressing deployment ways. Then, we quantify the impact of these deployments on the prefix assignment and the routing scalability based on the real routing data. we find that when the deploying position gets lower, the prefix amount of arbitrary AS is getting smaller, while the size of the global Forwarding Information Base is monotonically increasing. Comparing with the actual Internet's data, suitable deployment ways for hierarchical addressing are obtained. With these deployment ways, the excessive prefix problem is solved and the size of the global Forwarding Information Base could be reduced into 56% or even 32% of the one in current Internet.

Tunneling technology has been widely applied in the network, but the lack of effective congestion exposure in the tunnel, seriously affect the performance of the tunnel technology. This document will focus on the tunnel scenario, to design a tunnel congestion exposure and feedback model, and the tunnel congestion marking scheme and congestion information feedback scheme. First, the document describes Tunneling Protocol, Secondly describes the congestion problems in the tunnel, Thirdly proposes a basic tunnel congestion exposure model, Finally, proposes the detail of tunnel congestion marking and feedback scheme for the model. Modifying the ECN tunnel rules of RFC3168 and RFC6040, congestion marking scheme can be applied to the feedback model. Congestion feedback scheme use GRE (Generic Routing Encapsulation) header format extension to carry the feedback information. Content and format of Congestion feedback, and the transmission of feedback information are also discussed in the document.

In depth-image-based-rendering (DIBR), the quality of the synthesized virtual view depends on that of the depth maps in the reference views. In this paper, we develop a framework to estimate the distortion of the synthesized view when a simplified two-step warping algorithm is used and when there are random errors in the reference depth maps. A graph-based method is developed to obtain the depth and texture distributions in the synthesized view. Experimental results demonstrate the accuracy of the estimated distortion.

In this paper, we present an effective scheduling algorithm based on the RPS model, called simple weighted fair queueing (SWFQ). In SWFQ, computation of the system potential function does not require such division or multiplication operations as in MD-SCFQ. Compared with MD-SCFQ, SWFQ has lower complexity and can be easily implemented in chips. We verify the effectivity of proposed SWFQ through strict theoretical analysis.