Internet of Unmanned Aerial Vehicles: QoS Provisioning in Aerial Ad-Hoc Networks

Aerial ad-hoc networks have the potential to enable smart services while maintaining communication between the ground system and unmanned aerial vehicles (UAV). Previous research has focused on enabling aerial data-centric smart services while integrating the benefits of aerial objects such as UAVs in hostile and non-hostile environments. Quality of service (QoS) provisioning in UAV-assisted communication is a challenging research theme in aerial ad-hoc networks environments. Literature on aerial ad hoc networks lacks cooperative service-oriented modeling for distributed network environments, relying on costly static base station-oriented centralized network environments. Towards this end, this paper proposes a quality of service provisioning framework for a UAV-assisted aerial ad hoc network environment (QSPU) focusing on reliable aerial communication. The UAV’s aerial mobility and service parameters are modelled considering highly dynamic aerial ad-hoc environments. UAV-centric mobility models are utilized to develop a complete aerial routing framework. A comparative performance evaluation demonstrates the benefits of the proposed aerial communication framework. It is evident that QSPU outperforms the state-of-the-art techniques in terms of a number of service-oriented performance metrics in a UAV-assisted aerial ad-hoc network environment

Routing for Flying Networks using Software-Defined Networking

Nos últimos anos, os Veículos Aéreos Não Tripulados (UAVs) estão a ser usados de forma crescente em inúmeras aplicações, tanto militares como civis. A sua miniaturização e o preço reduzido abriram o caminho para o uso de enxames de UAVs, que permitem melhores resultados na realização de tarefas em relação a UAVs independentes. Contudo, para permitir a cooperação entre UAVs, devem ser asseguradas comunicações contínuas e fiáveis.Além disso, os enxames de UAVs foram identificados pela comunidade científica como meio para permitir o acesso à Internet a utilizadores terrestres em cenários como prestação de socorros e Eventos Temporários Lotados (TCEs), tirando partido da sua capacidade para transportar Pontos de Acesso (APs) Wi-Fi e células Long-Term Evolution (LTE). Soluções que dependem de uma Estação de Controlo (CS) capaz de posicionar os UAVs de acordo com as necessidades de tráfego dos utilizadores demonstraram aumentar a Qualidade de Serviço (QoS) oferecida pela rede. No entanto, estas soluções introduzem desafios importantes no que diz respeito ao encaminhamento do tráfego.Recentemente, foi proposta uma solução que tira partido do conhecimento da CS sobre o estado futuro da rede para atualizar dinamicamente as tabelas de encaminhamento de modo a que as ligações na rede voadora não sejam interrompidas, em vez de se recuperar da sua interrupção, como é o caso na maioria dos protocolos de encaminhamento existentes. Apesar de não considerar o impacto das reconfigurações na rede de acesso, como consequência da mobilidade dos APs, ou o balanceamento da carga na rede, esta abordagem é promissora e merece ser desenvolvida e implementada num sistema real.Esta dissertação tem como foco a implementação de um protocolo de encaminhamento para redes voadoras baseado em Software-Defined Networking (SDN). Especificamente, aborda os problemas de mobilidade e de balanceamento da carga na rede de uma perspetiva centralizada, garantindo simultaneamente comunicações ininterruptas e de banda-larga entre utilizadores terrestres e a Internet, permitindo assim que os UAVs se possam reposicionar e reconfigurar sem interferir com as ligações dos terminais à rede.In recent years, Unmanned Aerial Vehicles (UAVs) are being increasingly used in various applications, both military and civilian. Their miniaturisation and low cost paved the way to the usage of swarms of UAVs, which provide better results when performing tasks compared to single UAVs. However, to enable cooperation between the UAVs, always-on and reliable communications must be ensured.Moreover, swarms of UAVs are being targeted by the scientific community as a way to provide Internet access to ground users in scenarios such as disaster reliefs and Temporary Crowded Events (TCEs), taking advantage of the capability of UAVs to carry Wi-Fi Access Points (APs) or Long-Term Evolution (LTE) cells. Solutions relying on a Control Station (CS) capable of positioning the UAVs according to the users' traffic demands have been shown to improve the Quality of Service (QoS) provided by the network. However, they introduce important challenges regarding network routing.Recently, a solution was proposed to take advantage of the knowledge provided by a CS regarding how the network will change, by dynamically updating the forwarding tables before links in the flying network are disrupted, rather than recovering from link failure, as is the case in most of the existing routing protocols. Although it does not consider the impact of reconfigurations on the access network due to the mobility of the APs, it is a promising approach worthy of being improved and implemented in a real system.This dissertation focuses on implementing a routing solution for flying networks based on Software-Defined Networking (SDN). Specifically, it addresses the mobility management and network load balancing issues from a centralised perspective, while simultaneously enabling uninterruptible and broadband communications between ground users and the Internet, thus allowing UAVs to reposition and reconfigure themselves without interfering with the terminals' connections to the network

ASR-FANET: An adaptive SDN-based routing framework for FANET

Flying ad hoc network (FANET) is widely used in many military, commercial and civilian applications. Compared with mobile adhoc network (MANET) and vehicular ad hoc network (VANET), FANET holds unique characteristics such as high mobility, intermittent links and frequent topology changes, which cause a challenging task in the design of routing protocols. A novel adaptive software defined networking (SDN)-based routing framework for FANET called ASR-FANET is proposed in this article to solve the above challenges. The ASR-FANET framework is mainly composed of three important parts, which are the topology discovery mechanism, statistics gathering mechanism and route computation mechanism. In topology discovery mechanism, the periodic information about network topology is collected, including nodes and links. In statistics gathering mechanism, the status of the wireless network connection and flight statistics are collected. In route computation mechanism, the optimal path is calculated based on link costs. The performance of ASR-FANET framework is also has been evaluated by comprehensive simulations. The simulation results show that proposed framework is much better than other traditional protocols in packet delivery fraction, average end to end delay, normalized routing load, packet loss and throughput

K-means online-learning routing protocol (K-MORP) for unmanned aerial vehicles (UAV) adhoc networks

Unmanned Aerial Vehicles (UAVs) have become a hot topic due to their flexible architecture adopted in many wireless technologies. In UAV ad hoc networks, traditional routing protocols with a fixed topology are ineffective due to dynamic mobility and unstable paths. Therefore, the mobility patterns of UAVs challenge efficient and reliable routing in UAV networks. Traditional routing algorithms are often based on assumptions of static nodes and predetermined network topologies. Which are not suitable for the dynamic and unpredictable nature of UAV mobility patterns. To address this problem, this paper introduces a K-means online learning routing protocol (KMORP) scheme employing a Markov mobility model for UAV ad hoc networks. Initially, the proposed method utilizes a 3D Gauss Markov mobility model to accurately estimate UAV positions, while K-means online learning is adopted for dynamic clustering and load balancing. Designed for real-time data processing, KMORP is well suited for UAV ad hoc networks, quickly adapting to network environmental changes such as UAV mobility, interference, and signal degradation to ensure efficient data transmission and communication. This is achieved while reducing the overall communication overhead and increasing the packet delivery ratio(PDR%). In the routing phase, the proposed scheme employs inter-cluster forwarding nodes to transmit messages among different clusters. Extensive simulations demonstrate the performance of the proposed KMORP, showing a 38% better PDR compared to OLSR and over 50% less end-to-end(E2E) delay compared to typical K-Means. Furthermore, the proposed KMORP exhibited an average throughput of 955 kbps, showing a substantial improvement in network performance. The results underscore that the proposed KMORP outperforms existing techniques in terms of PDR, E2E delay, and throughput.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed