Performance Evaluation of Network Slicing for Aerial Vehicle Communications

Using 5G networks for flying vehicles is an opportunity to provide reliable connectivity while reducing cost and requirements on size, weight and power consumption. Network slicing is one feature which is particularly of interest. It enables a reliable aerial vehicle control slice independent from payload communication, such as video streaming to the ground. For the Unmanned Aerial Vehicle (UAV) use case, we rely on a 5G testbed that serves cars on an operational highway and trains on a parallel rail section. To test the effectiveness of network slicing, we show that the UAV control slice is unaffected by an overloaded UAV payload slice.Expósito García, A.; Hofmann, S.; Sous, C.; García, L.; Baltaci, A.; Bach, C.; Wellens, R.... (2019). Performance Evaluation of Network Slicing for Aerial Vehicle Communications. IEEE. 1-6. https://doi.org/10.1109/ICCW.2019.8756738S1

Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0

Within the context of Industry 4.0, mobile robot systems such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major areas challenging current communication and localization technologies. Due to stringent requirements on latency and reliability, several of the existing solutions are not capable of meeting the performance required by industrial automation applications. Additionally, the disparity in types and applications of unmanned vehicle (UV) calls for more flexible communication technologies in order to address their specific requirements. In this paper, we propose several use cases for UVs within the context of Industry 4.0 and consider their respective requirements. We also identify wireless technologies that support the deployment of UVs as envisioned in Industry 4.0 scenarios.Comment: 7 pages, 1 figure, 1 tabl

Technological Trends and Key Communication Enablers for eVTOLs

The world is looking for a new exciting form of transportation that will cut our travel times considerably. In 2021, the time has come for flying cars to become the new transportation system of this century. Electric vertical take-off and landing (eVTOL) vehicles, which are a type of flying cars, are predicted to be used for passenger and package transportation in dense cities. In order to fly safely and reliably, wireless communications for eVTOLs must be developed with stringent eVTOL communication requirements. Indeed, their communication needs to be ultra-reliable, secure with ultra-high data rate and low latency to fulfill various tasks such as autonomous driving, sharing a massive amount of data in a short amount of time, and high-level communication security. In this paper, we propose major key communication enablers for eVTOLs ranging from the architecture, air-interface, networking, frequencies, security, and computing. To show the relevance and the impact of one of the key enablers, we carried out comparative simulations to show the superiority compared to the current technology. We compared the usage of an air-based communication infrastructure with a tower mast in a realistic scenario involving eVTOLs, delivery drones, pedestrians, and vehicles.Comment: 8 pages, 10 figure

Network slice allocation for 5G V2X networks: A case study from framework to implementation and performance assessment

Empowered by the capabilities provided by fifth generation (5G) mobile communication systems, vehicle-to-everything (V2X) communication is heading from concept to reality. Given the nature of high-mobility and high-density for vehicle transportation, how to satisfy the stringent and divergent requirements for V2X communications such as ultra-low latency and ultra-high reliable connectivity appears as an unprecedented challenging task for network operators. As an enabler to tackle this problem, network slicing provides a power tool for supporting V2X communications over 5G networks. In this paper, we propose a network resource allocation framework which deals with slice allocation considering the coexistence of V2X communications with multiple other types of services. The framework is implemented in Python and we evaluate the performance of our framework based on real-life network deployment datasets from a 5G operator. Through extensive simulations, we explore the benefits brought by network slicing in terms of achieved data rates for V2X, blocking probability, and handover ratio through different combinations of traffic types. We also reveal the importance of proper resource splitting for slicing among V2X and other types of services when network traffic load in an area of interest and quality of service of end users are taken into account.publishedVersionPaid open acces

GPS Anomaly Detection And Machine Learning Models For Precise Unmanned Aerial Systems

The rapid development and deployment of 5G/6G networks have brought numerous benefits such as faster speeds, enhanced capacity, improved reliability, lower latency, greater network efficiency, and enablement of new applications. Emerging applications of 5G impacting billions of devices and embedded electronics also pose cyber security vulnerabilities. This thesis focuses on the development of Global Positioning Systems (GPS) Based Anomaly Detection and corresponding algorithms for Unmanned Aerial Systems (UAS). Chapter 1 provides an overview of the thesis background and its objectives. Chapter 2 presents an overview of the 5G architectures, their advantages, and potential cyber threat types. Chapter 3 addresses the issue of GPS dropouts by taking the use case of the Dallas-Fort Worth (DFW) airport. By analyzing data from surveillance drones in the (DFW) area, its message frequency, and statistics on time differences between GPS messages were examined. Chapter 4 focuses on modeling and detecting false data injection (FDI) on GPS. Specifically, three scenarios, including Gaussian noise injection, data duplication, data manipulation are modeled. Further, multiple detection schemes that are Clustering-based and reinforcement learning techniques are deployed and detection accuracy were investigated. Chapter 5 shows the results of Chapters 3 and 4. Overall, this research provides a categorization and possible outlier detection to minimize the GPS interference for UAS enhancing the security and reliability of UAS operations