UAV (Unmanned Aerial Vehicle) refers to an aircraft that is operated without a human pilot on board. UAVs are commonly known as drones. These vehicles can be remotely controlled by a human operator or autonomously operated using onboard computers. UAVs come in various sizes and types, ranging from small consumer drones used for recreational purposes to large military drones employed for surveillance, reconnaissance, and other missions.

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Seamless Service Handover in UAV-based Mobile Edge Computing

Unmanned aerial vehicles (UAVs), such as drones, can carry high-performance computing devices (e.g., servers) to provide flexible and on-demand data processing services for theusers in the network edge, leading to the so-called mobile edge computing. In mobile edge computing, researchers have already explored how to optimize the computation offloading and the trajectory planning of UAVs, as well as how to perform the service handover when mobile users move from one location to another. However, there is one critical challenge that has been neglected in past research, which is the limited battery life of UAVs. On average, commercial-level drones only have a battery life of around 30 minutes to 2 hours. As a result, during operation, mobile edge computing carriers have to frequently deal with service handovers that require shifting users and their computing jobs from low-battery UAVs to new fully-charged UAVs. This is the first work that focuses on addressing this challenge with the goal of providing continuous and uninterrupted mobile edge computing service. In particular, we propose a seamless service handover system that achieves minimum service downtime when handling the duty shift between low-battery UAVs and new fullycharged UAVs. In addition, we propose a novel UAV dispatchalgorithm that provides guidelines about how to dispatch new fully-charged UAVs and where to retrieve low-battery UAVs, with the objective of maximizing UAVs’ service time. The effectiveness of the proposed service handover system and the proposed UAV dispatch algorithm is demonstrated through comprehensive simulations using a time-series event-driven simulator.

Remote Drone Detection and Localization with Optical Fiber Microphones and Distributed Acoustic Sensing

Remote Drone Detection and Localization with Optical Fiber Microphones and Distributed Acoustic Sensing We demonstrate the first fiber-optic drone detection method with ultra-highly sensitive optical microphones and distributed acoustic sensor. Accurate drone localization has been achieved through acoustic field mapping and data fusion.

TrackIO: Tracking First Responders Inside-Out

TrackIO: Tracking First Responders Inside-Out First responders, a critical lifeline of any society, often find themselves in precarious situations. The ability to track them real-time in unknown indoor environments, would significantly contributes to the success of their mission as well as their safety. In this work, we present the design, implementation and evaluation of TrackIO–a system capable of accurately localizing and tracking mobile responders real-time in large indoor environments. TrackIO leverages the mobile virtual infrastructure offered by unmanned aerial vehicles (UAVs), coupled with the balanced penetration-accuracy tradeoff offered by ultra-wideband (UWB), to accomplish this objective directly from outside, without relying on access to any indoor infrastructure. Towards a practical system, TrackIO incorporates four novel mechanisms in its design that address key challenges to enable tracking responders (i) who are mobile with potentially non-uniform velocities (e.g. during turns), (ii) deep indoors with challenged reachability, (iii) in real-time even for a large network, and (iv) with high accuracy even when impacted by UAV’s position error. TrackIO’s real-world performance reveals that it can track static nodes with a median accuracy of about 1–1.5m and mobile (even running) nodes with a median accuracy of 2–2.5m in large buildings in real-time.

SkyRAN: A Self-Organizing LTE RAN in the Sky

SkyRAN: A Self-Organizing LTE RAN in the Sky We envision a flexible, dynamic airborne LTE infrastructure built upon Unmanned Autonomous Vehicles (UAVs) that will provide on-demand, on-time, network access, anywhere. In this paper, we design, implement and evaluate SkyRAN, a self-organizing UAV-based LTE RAN (Radio Access Network) that is a key component of this UAV LTE infrastructure network. SkyRAN determines the UAV’s operating position in 3D airspace so as to optimize connectivity to all the UEs on the ground. It realizes this by overcoming various challenges in constructing and maintaining radio environment maps to UEs that guide the UAV’s position in real-time. SkyRAN is designed to be scalable in that it can be quickly deployed to provide efficient connectivity even over a larger area. It is adaptive in that it reacts to changes in the terrain and UE mobility, to maximize LTE coverage performance while minimizing operating overhead. We implement SkyRAN on a DJI Matrice 600 Pro drone and evaluate it over a 90 000 m2 operating area. Our testbed results indicate that SkyRAN can place the UAV in the optimal location with about 30 secs of a measurement flight. On an average, SkyRAN achieves a throughput of 0.9 – 0.95X of optimal, which is about 1.5 – 2X over other popular baseline schemes.

SkyCore: Moving Core to the Edge for Untethered and Reliable UAV-based LTE Networks

SkyCore: Moving Core to the Edge for Untethered and Reliable UAV-based LTE Networks The advances in unmanned aerial vehicle (UAV) technology have empowered mobile operators to deploy LTE base stations (BSs) on UAVs, and provide on-demand, adaptive connectivity to hotspot venues as well as emergency scenarios. However, today’s evolved packet core (EPC) that orchestrates the LTE RAN faces fundamental limitations in catering to such a challenging, wireless and mobile UAV environment, particularly in the presence of multiple BSs (UAVs). In this work, we argue for and propose an alternate, radical edge EPC design, called SkyCore that pushes the EPC functionality to the extreme edge of the core network – collapses the EPC into a single, light-weight, self-contained entity that is co-located with each of the UAV BS. SkyCore incorporates elements that are designed to address the unique challenges facing such a distributed design in the UAV environment, namely the resource-constraints of UAV platforms, and the distributed management of pronounced UAV and UE mobility. We build and deploy a fully functional version of SkyCore on a two-UAV LTE network and showcase its (i) ability to interoperate with commercial LTE BSs as well as smartphones, (ii) support for both hotspot and standalone multi-UAV deployments, and (iii) superior control and data plane performance compared to other EPC variants in this environment.