Optimal traffic engineering via Newton's method

Abstract—Recently, it has been proven that the minimum-cost multicommodity flow can be realized by a link-state routing protocol, PEFT, using uneven traffic splitting [1]. The achieve-ment is due to solving a new convex optimization problem, Network Entropy Maximization (NEM), by a gradient descent algorithm. However, gradient descent algorithms are notorious for slow convergence to optimality whereas Newton’s method usually converges much faster. Solving NEM by Newton’s method is neither trivial nor standard because of the infinite number of variables NEM has. In this paper, we develop efficient Newton methods for the NEM problem

Learning-to-Dispatch: Reinforcement Learning Based Flight Planning under Emergency

The effectiveness of resource allocation under emergencies especially hurricane disasters is crucial. However, most researchers focus on emergency resource allocation in a ground transportation system. In this paper, we propose Learning-to- Dispatch (L2D), a reinforcement learning (RL) based air route dispatching system, that aims to add additional flights for hurricane evacuation while minimizing the airspace’s complexity and air traffic controller’s workload. Given a bipartite graph with weights that are learned from the historical flight data using RL in consideration of short- and long-term gains, we formulate the flight dispatch as an online maximum weight matching problem. Different from the conventional order dispatch problem, there is no actual or estimated index that can evaluate how the additional evacuation flights influence the air traffic complexity. Then we propose a multivariate reward function in the learning phase and compare it with other univariate reward designs to show its superior performance. The experiments using the real world dataset for Hurricane Irma demonstrate the efficacy and efficiency of our proposed schema

Real-Time 262-Mb/s Visible Light Communication With Digital Predistortion Waveform Shaping

A digital predistortion waveform shaping scheme combined with a blue filter is proposed to optimize both the rise and fall times of a light-emitting diode (LED) and the optical receiver current of the signal of the real-time visible light communication (VLC) system. The proposed scheme is implemented on a field-programmable gate array (FPGA) and a digital-to-analog converter based test bed, which is flexible and reconfigurable by programming the FPGA to match different LED characteristics and varied data rates. A 262-Mb/s non-return-to-zero on-off keying modulation based real-time VLC link with a bit error rate of less than 1.0×10−6 is achieved over a transmission distance of 5.0 m, which uses a single white phosphorous LED with a limited power of 0.1 W

FAST Copper for Broadband Access

FAST Copper is a multi-year, U.S. NSF funded project that started in 2004, and is jointly pursued by the research groups of Mung Chiang at Princeton University, John Cioffi at Stanford University, and Alexander Fraser at Fraser Research Lab, and in collaboration with several industrial partners including AT&T. The goal of the FAST Copper Project is to provide ubiquitous, 100 Mbps, fiber/DSL broadband access to everyone in the US with a phone line. This goal will be achieved through two threads of research: dynamic and joint optimization of resources in Frequency, Amplitude, Space, and Time (thus the name 'FAST') to overcome the attenuation and crosstalk bottlenecks, and the integration of communication, networking, computation, modeling, and distributed information management and control for the multi-user twisted pair network

Federated Variational Learning for Anomaly Detection in Multivariate Time Series

Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and dynamic nature of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework in a federated fashion to continuously monitor the behaviors of interconnected devices within a network and alert for abnormal incidents so that countermeasures can be taken before undesired consequences occur. To be specific, we leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model, which jointly captures feature and temporal dependencies in the multivariate time series data for representation learning and downstream anomaly detection tasks. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models. We also conduct extensive experiments to demonstrate the effectiveness of our detection framework under non-federated and federated settings in terms of overall performance and detection latency