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1. Temporal and Spatial Routing for Large Scale Safe and Connected UAS Traffic Management in Urban Areas

   https://doi.org/10.1109/RTCSA.2019.8864561  

   Zhao, Ziyi; Jin, Zhao; Luo, Chen; Fang, Haowen; Basti, Franco; Gursoy, M. Cenk; Caicedo, Carlos; Qiu, Qinru (August 2019, IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA))

   Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic scheduling and management functions are needed to coordinate the launching of sUAS from different launch sites and plan their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airbone sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm for sUAS trajectory management in a high density urban area. It plans sUAS movements in a spatial and temporal maze with the consideration of obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms reference route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, the conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used as a solution to improve the efficiency of airspace and communication resource utilization for next generation smart city and smart transportation. 

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2. Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems

   https://doi.org/10.3390/app12020576  

   Kim, Joseph; Atkins, Ella (January 2022, Applied Sciences)

   Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM. 

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3. Dual Timescale Orchestration System for Elastic Control of NextG Cloud-Integrated Networks

   https://doi.org/10.1109/ICIN60470.2024.10494452  

   Pagliuca, Quirino; Chaves, Luciano Jerez; Imputato, Pasquale; Tulino, Antonia; Llorca, Jaime (March 2024, IEEE)

   The confluence of advanced networking (5G/6G) and distributed cloud technologies (edge/fog computing) are rapidly transforming next-generation networks into highly distributed computation platforms, especially suited to host emerging resource-intensive and latency-sensitive services (e.g., smart transportation/city/factory, real-time computer vision, augmented reality). In this paper, we leverage the recently proposed Cloud Network Flow (CNF) modeling and optimization framework to design a novel two-timescale orchestration system for the joint control of communication and computation resources in cloud-integrated networks. The Long-Term Controller solves a properly constructed CNF optimization problem at a longer timescale that determines i) the end-to-end CNF routes (defining data paths and processing locations) for each service chain and ii) the associated allocation of communication and computation resources. The Short-Term Controller uses a local control policy to adjust the allocation of communication and computation resources based on queue state observations at a shorter timescale. Driven by the lack of proper simulation tools, we also develop new ns-3 features that allow modeling and simulation of cloud-integrated networks equipped with both communication and computation resources hosting arbitrary service chains. Finally, we integrate the proposed orchestration system into ns-3 to evaluate and analyze the dynamic orchestration of a set of representative service chains over a hierarchical cloud-integrated network. 

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4. Advanced Air Mobility Operation and Infrastructure for Sustainable Connected eVTOL Vehicle

   https://doi.org/10.3390/drones7050319  

   Al-Rubaye, Saba; Tsourdos, Antonios; Namuduri, Kamesh (May 2023, Drones)

   Advanced air mobility (AAM) is an emerging sector in aviation aiming to offer secure, efficient, and eco-friendly transportation utilizing electric vertical takeoff and landing (eVTOL) aircraft. These vehicles are designed for short-haul flights, transporting passengers and cargo between urban centers, suburbs, and remote areas. As the number of flights is expected to rise significantly in congested metropolitan areas, there is a need for a digital ecosystem to support the AAM platform. This ecosystem requires seamless integration of air traffic management systems, ground control systems, and communication networks, enabling effective communication between AAM vehicles and ground systems to ensure safe and efficient operations. Consequently, the aviation industry is seeking to develop a new aerospace framework that promotes shared aerospace practices, ensuring the safety, sustainability, and efficiency of air traffic operations. However, the lack of adequate wireless coverage in congested cities and disconnected rural communities poses challenges for large-scale AAM deployments. In the immediate recovery phase, incorporating AAM with new air-to-ground connectivity presents difficulties such as overwhelming the terrestrial network with data requests, maintaining link reliability, and managing handover occurrences. Furthermore, managing eVTOL traffic in urban areas with congested airspace necessitates high levels of connectivity to support air routing information for eVTOL vehicles. This paper introduces a novel concept addressing future flight challenges and proposes a framework for integrating operations, infrastructure, connectivity, and ecosystems in future air mobility. Specifically, it includes a performance analysis to illustrate the impact of extensive AAM vehicle mobility on ground base station network infrastructure in urban environments. This work aims to pave the way for future air mobility by introducing a new vision for backbone infrastructure that supports safe and sustainable aviation through advanced communication technology. 

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5. Strategic Deconfliction of Unmanned Aircraft Based on Hexagonal Tessellation and Integer Programming

   https://doi.org/10.2514/1.G007459  

   Liu, Yanchao; Zhou, Zhenyu; Naqvi, Waseem; Chen, Jun (December 2023, Journal of Guidance, Control, and Dynamics)

   Unmanned aircraft systems service suppliers adhere to interoperability standards that require unmanned aircraft operators to submit an operational intent, which describes the planned flight path in four-dimensional space. To ensure fairness, the central database follows a first-come, first-served approach, accepting new operational intents as long as they do not conflict with any active ones. However, creating a viable operational intent is challenging due to moving obstacles. This paper introduces an innovative optimization-based procedure to automate the intent filing process. It utilizes a stacked hexagonal tessellation to model the airspace, offering adjustable granularity. Path finding is accomplished using integer programming on the hex grid. The integer program is solved on a grid canvas that includes only necessary cells, striking a balance between computational efficiency and optimality. Simulation experiments demonstrate the procedure’s effectiveness in generating feasible trajectories, even in scenarios with dense, omnidirectional air traffic. This procedure has the potential to become the foundational software core for low-altitude air traffic management systems, providing strategic deconfliction and constraint management services. 

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