D3S: A Framework for Enabling Unmanned Aerial Vehicles as a Service

In this paper, we consider the use of UAVs to provide wireless connectivity services, for example after failures of wireless network components or to simply provide additional bandwidth on demand, and introduce the concept of UAVs as a service (UaaS). To facilitate UaaS, we introduce a novel framework, dubbed D3S, which consists of four phases: demand, decision, deployment, and service. The main objective of this framework is to develop efficient and realistic solutions to implement these four phases. The technical problems include determining the type and number of UAVs to be deployed, and also their final locations (e.g., hovering or on-ground), which is important for serving certain applications. These questions will be part of the decision phase. They also include trajectory planning of UAVs when they have to travel between charging stations and deployment locations and may have to do this several times. These questions will be part of the deployment phase. The service phase includes the implementation of the backbone communication and data routing between UAVs and between UAVs and ground control stations

Towards Enabling Unmanned Aerial Vehicles as a Service for Heterogeneous Applications

The increasing use of Unmanned Aerial Vehicles (UAVs) in various commercial applications, such as precision agriculture and aerial remote sensing, is fast contributing to a significant growth in the UAV market. Also, it is crucial to provide continuous coverage after failures of wireless network components or additional bandwidth in high traffic situations. By introducing the concept of UAVs as a Service (UaaS), we propose a novel framework, dubbed D3S, consisting of four phases: demand, decision, deployment, and service. The main objective of this framework is to provide a realistic and streamlined approach to support the implementation of the UaaS paradigm. The technical problems involved include determining the type and number of UAVs to be deployed and their final locations (e.g., hovering or on-ground). They also include the trajectory planning, possibly several times, between charging stations and deployment locations. We present the application of the D3S framework to two case studies with the goal of providing wireless connectivity services to (i) static users after failures of wireless network components, including long-term and short-term failures, and (ii) dynamic users in wireless relaying systems

Early wildfire detection by air quality sensors on unmanned aerial vehicles: Optimization and feasibility

“Millions of acres of forests are destroyed by wildfires every year, causing ecological, environmental, and economical losses. The recent wildfires in Australia and the Western U.S. smothered multiple states with more than fifty million acres charred by the blazes. The warmer and drier climate makes scientists expect increases in the severity and frequency of wildfires and the associated risks in the future. These inescapable crises highlight the urgent need for early detection and prevention of wildfires. This work proposed an energy management framework that integrated unmanned aerial vehicle (UAV) with air quality sensors for early wildfire detection and forest monitoring. An autonomous patrol solution that effectively detects wildfire events, while preserving the UAV battery for a larger area of coverage was developed. The UAV can send real-time data (e.g., sensor readings, thermal pictures, videos, etc) to nearby communications base stations (BSs) when a wildfire is detected. An optimization problem that minimized the total UAV’s consumed energy and satisfied a certain quality-of-service (QoS) data rate were formulated and solved. More specifically, this study optimized the flight track of a UAV and the transmit power between the UAV and BSs. Finally, selected simulation results that illustrate the advantages of the proposed model were proposed”--Abstract, page iii

Short-Term and Long-Term Cell Outage Compensation Using UAVs in 5G Networks

The use of Unmanned Aerial Vehicles (UAVs) has gained interest in wireless networks for its many uses and advantages such as rapid deployment and multi-purpose functionality. This is why wide deployment of UAVs has the potential to be integrated in the upcoming 5G standard. They can be used as flying base-stations, which can be deployed in case of ground Base-Stations (GBSs) failures. Such failures can be short-term or long-term. Based on the type and duration of the failure, we propose a framework that uses drones or helikites to mitigate GBS failures. Our proposed short-term and long-term cell outage compensation framework aims to mitigate the effect of the failure of any GBS in 5G networks. Within our framework, outage compensation is done with the assistance of sky BSs (UAVs), An optimization problem is formulated to jointly minimize communication power of the UAVs and maximize the minimum rates of the Users' Equipment (UEs) affected by the failure. Also, the optimal placement of the UAVs is determined. Simulation results show that the proposed framework guarantees the minimum quality of service for each UE in addition to minimizing the UAVs' consumed energy.This is a manuscript of a proceeding published as Selim, Mohamed Y., Ahmad Alsharoa, and Ahmed E. Kamal. "Short-Term and Long-Term Cell Outage Compensation Using UAVs in 5G Networks." In 2018 IEEE Global Communications Conference (GLOBECOM). (2018): 1-6. DOI: 10.1109/GLOCOM.2018.8648054. Posted with permission.</p