Software Defined Space Information Networks (SDSIN) is increasingly applied to life and production, the reason is that it can cope with various complex environments and tasks in the future communication environment. Owing to the performance advantages of the Software Defined Networking (SDN), some advanced technologies based on SDN are more and more applied to satellite network. In order to better provide users with high-quality services, network providers must predict and analyze End-to-End (E2E) traffic. Hence, this poses the natural question of how to accurately predict the future trend of E2E traffic to load balance, network optimization, and network security. Different from the previous 2-dimension terrestrial network, we study a 3-dimension SDSIN in this paper. Firstly, we analyze the difficulties and challenges of traffic engineering in SDSIN. Subsequently, an improved Hidden Markov Model (HMM) to E2E traffic prediction method is proposed. Finally, simulation results show that our improved HMM can be well applied for E2E traffic prediction in SDSIN.

A Prediction Approach to End-to-End Traffic in Space Information Networks

Accurately estimating network traffic from the partial measurements plays a crucial role in network management. However, the potential anomaly existing in real networks usually makes this goal difficult to achieve. Existing network traffic estimation methods generally impute network traffic independent of anomaly detection, which incurs significant performance degradation with network anomaly. To address this issue in the realistic network scenario, we propose a novel anomaly-aware network traffic estimation method to recover network traffic data concurrently with network anomaly detection. Specifically, by exploiting the inherent spatio-temporal characteristics, we first formulate the network traffic estimation as a low-rank tensor completion problem. Then, an outlier-robust tensor completion (OrTC) model is constructed by introducing both L2,1-norm regularization and ${\mathrm {L}}_{F}$ -norm regularization, which can not only well fit the intrinsic low-rank property of real traffic data, but also is robust against both the dense noise and the sparse anomaly. Furthermore, an effective optimization algorithm OrTC-AM is designed to solve the non-convex and non-smooth OrTC model based on the popular alternating minimization method. Finally, the extensive experiments performed on the public dataset demonstrate that our proposed OrTC-AM method outperforms the previously widely used network traffic estimation methods.

/pdf/anomaly-aware-network-traffic-estimation-via-outlier-robust-4hig80b5fi.pdf

Anomaly-Aware Network Traffic Estimation via Outlier-Robust Tensor Completion

In wireless networks, it is cost-effective to estimate the node-to-anchor distance by using the distribution characteristics of nodes. Unfortunately, in practice, the network topology may be irregular, which makes the estimated distances inaccurate. In this paper, by analyzing the distribution characteristics of nodes, we propose a novel scheme called Accurate Analytical-based Multi-hop Localization (AAML), which can greatly enhance estimation accuracy with requiring less communication and lower energy consumption. We first develop a new method that uses the common numbers of neighbors to estimate the distances between pairs of directly connected nodes during the network initialization phase. We then employ an optimal weighted matrix and hyperbolic estimation to reduce the effect of cumulative error. Finally, we utilize the weighted Taylor series to further improve the estimation accuracy; and utilize the geometric constraint to rectify the inaccurate estimated location, based on the relationship between the estimated locations and the anchor node locations. The proposed AAML method has been validated through analysis and experiments over different irregular networks and parameters, and AAML is demonstrated to be superior to existing methods in terms of estimated accuracy and energy consumption. The code is publicly available at https://pan.baidu.com/s/1k5o4aNGqa6iuxNskxRMMdw , pwd: n6ht.

Accurate Analytical-Based Multi-Hop Localization With Low Energy Consumption for Irregular Networks

Wi-Fi-based indoor localization has received extensive attention in wireless sensing. However, most Wi-Fi-based indoor localization systems have complex models and high localization delays, which limit the universality of these localization methods. To solve these problems, a depthwise separable convolution-based passive indoor localization system (DSCP) is proposed. DSCP is a lightweight fingerprint-based localization system that includes an offline training phase and an online localization phase. In the offline training phase, the indoor scenario is first divided into different areas to set training locations for collecting CSI. Then, the amplitude differences of these CSI subcarriers are extracted to construct location fingerprints, thereby training the convolutional neural network (CNN). In the online localization phase, CSI data are first collected at the test locations, and then, the location fingerprint is extracted and finally fed to the trained network to obtain the predicted location. The experimental results show that DSCP has a short training time and a low localization delay. DSCP achieves a high localization accuracy, above 97%, and a small median localization distance error of 0.69 m in typical indoor scenarios.

/pdf/dscp-depthwise-separable-convolution-based-passive-indoor-2k0822vxsg.pdf

DSCP: Depthwise Separable Convolution-Based Passive Indoor Localization Using CSI Fingerprint

With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications. However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity. Deep learning, with its powerful capabilities in representation learning and multi-level abstractions, has recently become the most effective approach in many intelligent sensing systems. In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.

/pdf/deep-learning-for-intelligent-traffic-sensing-and-prediction-33e325o0q7.pdf

Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Accurately estimating origin-destination (OD) network traffic is crucial for network management and capacity planning. However, the potential network anomaly and complex noise make this goal difficult to achieve. Existing network traffic estimation methods usually impute network traffic independent of anomaly detection, which ignores the potential relationship between the two tasks to help each other in achieving better performance. Moreover, these approaches can only be suitable for simple Gaussian or outlier noise assumptions, which cannot be applied to more complex noise distributions in practical applications. To address these issues, we propose a novel anomaly-tolerant network traffic estimation approach for simultaneously estimating network traffic and detecting network anomaly. Specifically, by utilizing the inherent low-rank property and temporal characteristic of traffic matrix, we formulate the network traffic estimation problem as a noise-immune temporal matrix completion (NiTMC) model, where the complex noise is fitted by mixture of Gaussian (MoG), and the network anomaly is smoothed by the $L_{2,1}$ -norm regularization. In addition, we also design a convergence-guaranteed optimization algorithm based on the expectation maximization (EM) and block coordinate update (BCU) methods to solve the proposed model. Furthermore, to deal with large-scale network problems, we develop a scalable and memory-efficient algorithm by employing stochastic proximal gradient descent (SPGD) method. Finally, the extensive experiments performed on real datasets demonstrate that our proposed NiTMC model outperforms the previously widely used network traffic estimation methods.

Hai Zhu

Papers

Anomaly-Tolerant Network Traffic Estimation via Noise-Immune Temporal Matrix Completion Model