31 research outputs found
Cellular for the skies: exploiting mobile network infrastructure for low altitude air-to-ground communications
In this article we presented an overview of UASs for civil applications focusing on the communication component. We identified several available communication technologies for UAVs, their constraints, and also protocols available for implementing the remote operation of the vehicles. As an attractive solution for the A2G communication link for UAVs, we discussed the potential of mobile networks with their fully deployed infrastructures, wide radio coverage, high throughputs, reduced latencies, and large availability of radio modems. We described how a UAS can be implemented in a flexible and modular approach that allows it to rely on one or several wireless (UAVs and GCSs) and wired (GCSs) technologies. Despite the advantages of a system based on cellular and IP networks, there are problems that must be dealt with, namely, possible loss of radio coverage, presence of NAT, delay, jitter, and packet loss. Following the proposed architecture, we implemented an UAS and conducted some flight tests, which showed that the operation of the vehicles in semi-automatic or fully-automatic modes is feasible. It is expected that future enhancements for 4G networks and evolution to 5G will benefit UAV communications even further with lower latencies, higher throughput, and higher reliability.info:eu-repo/semantics/acceptedVersio
A Survey on Cellular-connected UAVs: Design Challenges, Enabling 5G/B5G Innovations, and Experimental Advancements
As an emerging field of aerial robotics, Unmanned Aerial Vehicles (UAVs) have
gained significant research interest within the wireless networking research
community. As soon as national legislations allow UAVs to fly autonomously, we
will see swarms of UAV populating the sky of our smart cities to accomplish
different missions: parcel delivery, infrastructure monitoring, event filming,
surveillance, tracking, etc. The UAV ecosystem can benefit from existing 5G/B5G
cellular networks, which can be exploited in different ways to enhance UAV
communications. Because of the inherent characteristics of UAV pertaining to
flexible mobility in 3D space, autonomous operation and intelligent placement,
these smart devices cater to wide range of wireless applications and use cases.
This work aims at presenting an in-depth exploration of integration synergies
between 5G/B5G cellular systems and UAV technology, where the UAV is integrated
as a new aerial User Equipment (UE) to existing cellular networks. In this
integration, the UAVs perform the role of flying users within cellular
coverage, thus they are termed as cellular-connected UAVs (a.k.a. UAV-UE,
drone-UE, 5G-connected drone, or aerial user). The main focus of this work is
to present an extensive study of integration challenges along with key 5G/B5G
technological innovations and ongoing efforts in design prototyping and field
trials corroborating cellular-connected UAVs. This study highlights recent
progress updates with respect to 3GPP standardization and emphasizes
socio-economic concerns that must be accounted before successful adoption of
this promising technology. Various open problems paving the path to future
research opportunities are also discussed.Comment: 30 pages, 18 figures, 9 tables, 102 references, journal submissio
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Design and development of an SDN robotic system with intelligent openflow IOT testbeds for power assessment, prediction and fault management
This thesis was submitted for the award of Docctor of Philosophy and was awarded by Brunel University LondonCurrent wind turbine and power grid industry have relatively little research and
development with regards to implementing novel communication network and intel-
ligent system to overcome issues that pertain to network failure and lack of monitor-
ing. Wind turbine location could be a big concern when it comes to identifying an
efficient location for future wind turbine and the impact of a site with non-efficient
meteorological parameters can result in relocation of a wind turbine and revenue-
loss. Unplanned wind turbine shutdowns that are considered to be one of the major
revenue-loss factors of a modern wind farm business. Typically, the unplanned wind
turbine shutdown is a result of sensors fail due to harsh environment challenges that
prevent hardware status from being available on the monitoring system. The above
mentioned research problems pertain to wind turbine site assessment and predic-
tion of power. In this thesis, a novel programmable software-defined robotics and
IoT testbeds are proposed with the fusion of Artificial Intelligence and optimiza-
tion methods to solve specific problems related to wind turbine site assessment and
fault management. The site selection process is implemented using proposed aerial
and ground robotic systems that are incorporated with Software-Defined Networks
and OpenFlow switching capabilities. A second stage development of the system is
proposing a prediction platform that run on the aerial robot cluster using neural net-
works optimization regression techniques. To overcome the unplanned wind turbine
network outage, an IoT micro cloud cluster system is proposed that act as immedi-
ate fail-over platform to provide continuous health readings of the wind turbine to
ensure the turbine in question will not get shutdown unnecessarily. The proposed
system help in minimizing revenue-loss caused by stopping a wind turbine from op-
eration and help maintain generated power stability on the grid. Additionally, since
large wind farms require an agile and scalable management of selecting the most
efficient wind turbine location install. Thus, a softwarized cognitive routing proto-
col is proposed. The group of quadcopters is a redundant failover Software-Defined
Network/OpenFlow system that can cover every single way point of the farm land.
Although, power consumption is essential for the continuity the service, a Software-
Defined charging system testbed is proposed that uses inductive power transfer wit
Situation-aware Edge Computing
Future wireless networks must cope with an increasing amount of data that needs to be transmitted to or from mobile devices. Furthermore, novel applications, e.g., augmented reality games or autonomous driving, require low latency and high bandwidth at the same time. To address these challenges, the paradigm of edge computing has been proposed. It brings computing closer to the users and takes advantage of the capabilities of telecommunication infrastructures, e.g., cellular base stations or wireless access points, but also of end user devices such as smartphones, wearables, and embedded systems. However, edge computing introduces its own challenges, e.g., economic and business-related questions or device mobility. Being aware of the current situation, i.e., the domain-specific interpretation of environmental information, makes it possible to develop approaches targeting these challenges.
In this thesis, the novel concept of situation-aware edge computing is presented. It is divided into three areas: situation-aware infrastructure edge computing, situation-aware device edge computing, and situation-aware embedded edge computing. Therefore, the concepts of situation and situation-awareness are introduced. Furthermore, challenges are identified for each area, and corresponding solutions are presented. In the area of situation-aware infrastructure edge computing, economic and business-related challenges are addressed, since companies offering services and infrastructure edge computing facilities have to find agreements regarding the prices for allowing others to use them. In the area of situation-aware device edge computing, the main challenge is to find suitable nodes that can execute a service and to predict a node’s connection in the near future. Finally, to enable situation-aware embedded edge computing, two novel programming and data analysis approaches are presented that allow programmers to develop situation-aware applications.
To show the feasibility, applicability, and importance of situation-aware edge computing, two case studies are presented. The first case study shows how situation-aware edge computing can provide services for emergency response applications, while the second case study presents an approach where network transitions can be implemented in a situation-aware manner
Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks
Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique
Feature Papers of Drones - Volume I
[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin
Multi-Robot Systems: Challenges, Trends and Applications
This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics