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

Evaluation of Flying Ad Hoc Network Topologies, Mobility Models, and IEEE Standards for Different Video Applications

Nowadays, drones became very popular with the enhancement of the technological progress of moving devices with a connection to each other, known as Flying Ad Hoc Network (FANET). It is used in most worldwide necessary life scenarios such as video recording, search and rescue, military missions, moving items between different areas, and many more. This leads to the necessity to evaluate different network strategies between these flying drones, which are essential to improve their quality of performance in the field. Several challenges must be addressed to effectively use FANET, to provide stable and reliable transmission for different types of data during vast changing topologies, such as different video sizes, different types of mobility models, recent Wireless Fidelity standards, types of routing protocols used, security problems, and many more. In this paper, a fully comprehensive analysis of FANET will be done to evaluate and enhance these challenges that concern different video types, mobility models, and IEEE 802.11n standards for best performance, by measuring throughput, retransmission attempt, and delay metrics. The result shows that Gauss–Markov mobility model gives the highest result using Ad Hoc On-Demand Vector and lowest delay, whereas for retransmission attempts, 2.4 GHz frequency has the lowest as it can reach more coverage area than 5 GHz

Resources Efficient Dynamic Clustering Algorithm for Flying Ad-Hoc Network

Unmanned Aerial Vehicles, often known as UAVs, are connected in the form of a Flying Ad-hoc Network, or FANET, and placed to use in a variety of applications to carry out efficient remote monitoring. Their great mobility has an adverse effect on their energy consumption, which in turn has a detrimental effect on the network's stability and the effectiveness of communication. To manage the very dynamic flying behavior of UAVs and to keep the network stable, novel clustering algorithms have been implemented. In this context, a novel clustering technique is developed to meet the rapid mobility of UAVs and to offer safe inter-UAV distance, reliable communication, and an extended network lifespan. It also provides a detailed analysis of the similarities and distinctions between AODV, AOMDV, DSDV, and DumbAgent.The performance of these protocols is analyzed using the NS-2 simulator. The simulation results are shown in our study with AODV, AOMDV, DSDV, and DumbAgent. The results of the simulation make it abundantly evident that the AODV routing protocol outperforms the other routing protocols DSDV, AOMDV, and DumbAgent in terms of the number of packets lost, the amount of throughput achieved, the amount of routing overhead generated, and the amount of delay

RGIM: An Integrated Approach to Improve QoS in AODV, DSR and DSDV Routing Protocols for FANETS Using the Chain Mobility Model

Flying ad hoc networks (FANETs) are a collection of unmanned aerial vehicles that communicate without any predefined infrastructure. FANET, being one of the most researched topics nowadays, finds its scope in many complex applications like drones used for military applications, border surveillance systems and other systems like civil applications in traffic monitoring and disaster management. Quality of service (QoS) performance parameters for routing e.g. delay, packet delivery ratio, jitter and throughput in FANETs are quite difficult to improve. Mobility models play an important role in evaluating the performance of the routing protocols. In this paper, the integration of two selected mobility models, i.e. random waypoint and Gauss–Markov model, is implemented. As a result, the random Gauss integrated model is proposed for evaluating the performance of AODV (ad hoc on-demand distance vector), DSR (dynamic source routing) and DSDV (destination-Sequenced distance vector) routing protocols. The simulation is done with an NS2 simulator for various scenarios by varying the number of nodes and taking low- and high-node speeds of 50 and 500, respectively. The experimental results show that the proposed model improves the QoS performance parameters of AODV, DSR and DSDV protocol

FANET Drone’s 4K Data Applications, Mobility Models and Wi-Fi IEEE802.11n Standards, Journal of Telecommunications and Information Technology, 2021, nr 1

With growing popularity of unmanned aerial vehicles (UAVs), the importance of flying ad-hoc networks (FANETs) is enhanced by such applications as 4K video recording, communications in search and rescue missions and goods deliveries, to name just a few. This, in turn, stimulates research on different topologies of networks existing between UAVs, with studies in this field being essential to improving performance of such networks. Several problems must be solved to effectively use UAVs in order to offer stable and reliable massive data transmission capabilities, taking into consideration quickly changing FANET topologies, types of routing, security issues, etc. In this paper, a comprehensive evaluation of FANETs used by UAVs is presented in terms of communication network challenges, data types, mobility models and standards applied in order to achieve best performance. The evaluation presented herein covers such areas as data throughput, retransmission attempts and delay

DLSA: Delay and Link Stability Aware Routing Protocol for Flying Ad-hoc Networks (FANETs)

Flying Ad-hoc Network (FANET) is a new class of Mobile Ad-hoc Network in which the nodes move in three-dimensional (3-D) ways in the air simultaneously. These nodes are known as Unmanned Aerial Vehicles (UAVs) that are operated live remotely or by the pre-defined mechanism which involves no human personnel. Due to the high mobility of nodes and dynamic topology, link stability is a research challenge in FANET. From this viewpoint, recent research has focused on link stability with the highest threshold value by maximizing Packet Delivery Ratio and minimizing End-to-End Delay. In this paper, a hybrid scheme named Delay and Link Stability Aware (DLSA) routing scheme has been proposed with the contrast of Distributed Priority Tree-based Routing and Link Stability Estimation-based Routing FANET’s existing routing schemes. Unlike existing schemes, the proposed scheme possesses the features of collaborative data forwarding and link stability. The simulation results have shown the improved performance of the proposed DLSA routing protocol in contrast to the selected existing ones DPTR and LEPR in terms of E2ED, PDR, Network Lifetime, and Transmission Loss. The Average E2ED in milliseconds of DLSA was measured 0.457 while DPTR was 1.492 and LEPR was 1.006. Similarly, the Average PDR in %age of DLSA measured 3.106 while DPTR was 2.303 and LEPR was 0.682. The average Network Lifetime of DLSA measured 62.141 while DPTR was 23.026 and LEPR was 27.298. At finally, the Average Transmission Loss in dBm of DLSA measured 0.975 while DPTR was 1.053 and LEPR was 1.227.- Key Research and Development Program of Zhejiang Province - grant No. 2020C01076. - National Natural Science Foundation of China - grant No. 62072403