Junfei Xie

A Survey and Analysis of Mobility Models for Airborne Networks

Mobility models serve as the foundation for evaluating and designing airborne networks (ANs). Due to the significant impact of mobility models on the networking performance, the mobility models must realistically capture the attributes of ANs. In this paper, we present a comprehensive survey and comparative analysis of mobility models that are either adapted to or developed for AN evaluation purposes. We evaluate these mobility models based on the following metrics: adaptability, networking performance, and ability to realistically capture the mobility attributes of ANs (including high mobility, mechanical and aerodynamic constraint, and safety requirements). To provide a deeper understanding and facilitate the selection and configuration of these mobility models, we also evaluate them based on randomness levels and associated applications.

UAV-carried Long-distance Wi-Fi Communication Infrastructure

We develop an Unmanned Aerial Vehicle (UAV)-carried long distance Wi-Fi communication infrastructure for smart city applications such as emergency response. In this paper, we describe the mobility of UAVs, the heading control mechanisms of directional antennae to reject wind disturbance, and practical UAV design issues such as the flight time of battery-powered UAVs. We demonstrate the performance of the UAV-carried communication infrastructure through a simulation study.

Analysis of Mobility Models for Airborne Networks

Mobility models serve as the foundation for evaluating and designing airborne networks. Due to the significant impact of mobility models on the network performance, mobility models for airborne networks (ANs) must realistically capture the attributes of ANs. In this paper, we perform a comprehensive and comparative analysis of AN mobility models and evaluate the models based on the following metrics: networking performance and ability to capture the mobility attributes of ANs. To provide a deeper understanding and facilitate the selection and configuration of these mobility models, we also evaluate them based on randomness levels and associated applications.

A Comprehensive Modeling Framework for Airborne Mobility

Mobility models serve as the foundation for evaluating and designing airborne networks. Due to the significant impact of mobility models on the network performance, mobility models for airborne networks (ANs) must realistically capture the attributes of ANs. In this paper, I develop a comprehensive modeling framework for ANs. The work I have done is concluded as the following three parts. First, I perform a comprehensive and comparative analysis of AN mobility models and evaluate the models based on several metrics: 1) networking performance, 2) ability to capture the mobility attributes of ANs, 3) randomness levels and 4) associated applications. Second, I develop two 3D mobility models and realistic boundary models. The mobility models follow physical laws behind aircraft maneuvering and therefore capture the characteristics of aircraft trajectories. Third, I suggest an estimation procedure to extract parameters in one of the models that I developed from real flight test data. The good match between the estimated trajectories and real flight trajectories also validate the suitability of the model. The mobility models and the estimation procedure lead to the creation of “realistic” simulation and evaluation environment for airborne networks.

Effective and scalable uncertainty evaluation for large-scale complex system applications

Effective uncertainty evaluation is a critical step toward real-time and robust decision-making for complex systems in uncertain environments. A Multivariate Probabilistic Collocation Method (M-PCM) was developed to effectively evaluate system uncertainty. The method smartly chooses a limited number of simulations to produce a low-order mapping, which precisely predicts the mean output of the original system mapping up to certain degrees. While the M-PCM significantly reduces the number of simulations, it does not scale with the number of uncertain parameters, making it difficult to use for large-scale applications that typically involve a large number of uncertain parameters. In this paper, we develop a method to break the curse of dimensionality. The method integrates M-PCM and Orthogonal Fractional Factorial Designs (OFFDs) to maximally reduce the number of simulations from 22m to 2⌈log2(m+1)⌉ for a system mapping of m parameters. The integrated M-PCM-OFFD predicts the correct mean of the original system mapping, and is the most robust to numerical errors among all possible designs of the same number of simulations. The analysis also provides new insightful formal interpretations on the optimality of OFFDs.

On Properties of Quantized Consensus in Layered Sensor Networks

In this paper, we study properties of distributed consensus in layered sensor networks of the multi-layer multi-group (MLMG) structure. We show that properly designed MLMG networks maintain decentralized communication, whereas show the advantage of centralized structures. In particular, they require less number of transmissions required to reach consensus. This feature is critical for efficient distributed computing in large-scale sensor network applications. For typical classes of MLMG networks, we mathematically characterize the reduced number of transmissions compared to equivalent egalitarian decentralized structures of the same consensus dynamics. This explicit characterization based on simple graphical characteristics of MLMG structures permits an efficient design of large-scale network structures to meet desired performance requirements. In addition, we characterize the asymptotic and transient properties of consensus in MLMG networks of limited channel rates, using the probabilistic quantization schemes.

A Scalable Sampling Method to High-Dimensional Uncertainties for Optimal and Reinforcement Learning-Based Controls

Modern dynamical systems often operate in environments of high-dimensional uncertainties that modulate system dynamics in a complicated fashion. These high-dimensional uncertainties, non-Gaussian in many realistic scenarios, complicate real-time system analysis, design, and control tasks. In this letter, we address the scalability of computation for systems of high-dimensional uncertainties by introducing new sampling methods, the multivariate probabilistic collocation method (M-PCM), and its extension called M-PCM-orthogonal fractional factorial design (OFFD) which integrates M-PCM with the OFFDs to break the curse of dimensionality. We explore the capabilities of M-PCM and M-PCM-OFFD-based optimal control and adaptive control using the reinforcement learning approach. The analyses and simulation studies illustrate the efficiency and effectiveness of these two approaches.

A Network Condition-Centric Flow Selection and Rerouting Strategy to Mitigate Air Traffic Congestion under Uncertainties

Rerouting is an effective air traffic flow management strategy to reduce en-route traffic congestion caused by convective weather or other off-nominal situations. In this paper, we introduce a network condition-centric flow selection and rerouting method to improve the efficiency of rerouting in an uncertain and dynamically changing airspace environment. The method adjusts the flows to be rerouted according to different network conditions to maximally reduce traffic congestion. To address the challenges such as high-dimensional demands and weather uncertainties, we adopt a scalable sampling-based control method that enables an efficient and robust optimal rerouting design. A series of analysis and simulation studies motivate and illustrate good performance of the proposed method.

Scalable Multidimensional Uncertainty Evaluation Approach to Strategic Air Traffic Flow Management

Convective weather events cause capacity reduction in the National Airspace System (NAS), and lead to traffic congestion. To mitigate congestion, strategic air traffic flow management plans traffic flows at a long look-ahead time (2-15 hour). Planning at this timeframe is challenging, due to the wide possibility of weather events and requirement for real-time management. To conquer these challenges, we need an approach to quickly assess the impact of predicted weather events on the performance of air traffic system. In this paper, we use a scalable multidimensional uncertainty evaluation approach, called M-PCM-OFFD, to address this problem. Simulation studies show the effectiveness of this approach for the performance evaluation of air traffic system. In addition, we investigate further capability of M-PCM-OFFD through exploring higher-level OFFDs. Finally, we introduce an uncertainty-exploiting framework to enable real-time strategic air traffic management under weather uncertainty.

RSSI-based heading control for robust long-range aerial networking using directional antennas

Aerial networking using directional antennas (ANDA) is considered as a promising solution for the networking of unmanned aerial vehicles (UAVs) over long distance. The cyber constraints on communication channel characteristics and physical constraints on the payload, power, and mobility of UAVs produce challenges to achieve a robust ANDA. In this paper, an RSSI and fuzzy logic based control algorithm is developed to control directional antennas mounted on two moving UAVs to achieve a robust broad-band long-distance communication channel. In particular, the self-alignment of UAV-mounted directional antennas over a long distance is achieved through fusing GPS and communication channel characteristic measured by received signal strength indicator (RSSI), using unscented Kalman filter and fuzzy logic strategies. Simulations are performed to validate the RSSI and fuzzy logic based directional antenna control approach. The solution developed in this paper can significantly enhance the performance of wireless communication channel in imperfect environment subject to the unavailability of GPS and unstable strength of wireless signals.