A performance‐aware routing mechanism for flying ad hoc networks

Different from mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs), a flying ad hoc network (FANET) is a very low-density network where node topology changes rapidly and irregularly. These characteristics, the density, mobility, and speed of flight nodes, affect the performance of FANET. Furthermore, application scenarios and environmental settings could affect the performance of FANETs. In this paper, we analyzed the representative FANET protocols, AODV, DSDV, and OLSR, according to mobility models, SRWP, MP, RDPZ, EGM, and DPR, under the multi-UAV-based reconnaissance scenario. We evaluated them in terms of the number of nodes, network connectivity, mobility model’s reconnaissance rate, speed of nodes, and ground control station (GCS) location. As a result, we found that AODV showed the highest PDR performance (81%) with SRWP in multiple UAV-based reconnaissance scenarios. As for a mobility model under the consideration of reconnaissance rate, SRWP was excellent at 76%, and RDPZ and EGM mobility models were reasonable at 62% and 60%, respectively. We also made several interesting observations such as how when the number of nodes increases, the connectivity of the network increases, but the performance of the routing protocol decreases, and how the GCS location affects the PDR performance of the combination of routing protocols and mobility models.

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FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models

A system of several wireless sensor nodes and one unmanned aerial vehicle (UAV) is considered in this research. The nodes are only floating and drifting with the sea stream. The UAV will be operating as a data mule to gather sensing information from wireless sensor nodes. Unlike prior studies, this paper addressed a realistic ocean model for the nodes movements which will be the references to the Kalman Filter (KF) in estimating for the nodes’ positions. Simulation results are evaluated for an optimal flight-able path for the UAV under several constraints by particle swarm optimization (PSO). Specifically, the deviation between the estimated positions and the referenced positions, total energy consumption by the sensors network, data rates between UAV and the nodes, flight time for the UAV, and frequency of visiting the nodes by the UAV will be considered for optimization. The systems performances will be evaluated based on these scenarios: a) an ideal and unrealistic scenario where the UAV follows the nodes continuously; b) a realistic case where the UAV only flies periodically. Discussions and solutions were also addressed for the situations when the deployed nodes are more significantly separated than the cases simulated in the paper. 

Path planning for UAV harvesting information from dynamical wireless sensor nodes at sea

In this research, highly dynamic and sparse wireless sensor nodes floating at sea with a fixed-wing unmanned aerial vehicle (UAV) formulated the system. The UAV is responsible for flying to the most possible predicted locations for the nodes to collect sensing data and fly back to the operation centre for offloading the data. This paper is different from research in the same context of dynamic sensor networks at sea by addressing a realistic ocean model for node movements forecast and a Kalman Filter (KF) to estimate the nodes’ positions. The way-points for the UAV flight were done by a multi-objective optimization algorithm, particle swarm optimization (PSO), and the flight path was formulated by using a Dubins object in Matlab. PSO is adopted to take the following terms into account: total energy consumption by the sensor nodes, the error in positions estimation, data rates for communications between the UAV and the nodes, flight time for the UAV and the waiting time by the nodes for being communicated by the UAV. Intensive simulations were implemented to provide system performances in continuous and periodic flights scenarios. Extra simulations for the case that the nodes were located closer to each other were also run to give more references and comparisons. The paper provided discussions on the performances and abilities for opportunistic data collection from drifting and sparse distributed wireless sensor nodes by using one UAV with periodic flights.

Performance evaluations for opportunistic data acquisitions from sparse and drifting wireless sensor networks with a UAV

The major goal of this paper is to use image enhancement techniques for enhancing and extracting data in FANET applications to improve the efficiency of surveillance. The proposed conceptual system design can improve the likelihood of FANET operations in oil pipeline surveillance, and sports and media coverage with the ultimate goal of providing efficient services to those who are interested. The system architecture model is based on current scientific principles and developing technologies. A FANET, which is capable of gathering image data from video-enabled drones, and an image processing system that permits data collection and analysis are the two primary components of the system. Based on the image processing technique, a proof of concept for efficient data extraction and enhancement in FANET situations and possible services is illustrated.

https://doi.org/10.59568/kjset-2023-2-1-02

FANET Experiment: Real-Time Surveillance Applications Connected to Image Processing System

Recently, drones also known as UAVs for Unmanned Aerial Vehicles have had a great place in the civil and industrial field. They can be deployed in an ad hoc manner and operate collaboratively within a fleet known as Flying AdHoc Network (FANET). This kind of deployment can be found in some real-time applications where it is difficult to intervene, as it is the case in natural disasters. In our paper, we proposed a new application using drones to help police officers to verbalize drivers on real-time. The idea is to use radar drones to monitor highways against traffic violations and then transmit the captured images to a ground police patrol or to the police data centre. If the UAV fleet is close to the ground Base Station (BS), drones broadcast the captured images directly to the BS. However, if the fleet is far away, the UAVs are forced to go back and forth to the BS while the major issue for UAVs is the limited lifetime of their batteries. To solve this problem and ensure real-time transmission during highways surveillance, we introduced vehicles on the ground as an alternative solution that meets the requirements of our network. This mechanism is called vehicles-assisted UAVs Network. For the validation of our proposed model, some simulations have been made using NS-3 simulator. The results of our simulation show that our model is very promising in terms of delay, packet delivery ratio, and energy consumption.

UAVs-Based Mobile Radars for Real-Time Highways Surveillance

Among the technological advances, the use of Unmanned Aerial Vehicles (UAVs) saw an evolution in this last decade, their employment in civil applications, such as firefighting, disaster management, environmental monitoring and policing, is increased recently. The UAVs are connected with Ad- hoc modes like Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs), this is another innovation in the wireless network where requirements change largely from long-established networking systems. Furthermore, the cooperative work is required in both civil and military applications to accomplish intensive missions. Flying Ad-hoc Networks (FANETs) have been developed using techniques that assist the transmission of information in real-time. The high mobility and quick topology change in UAV networks caused the difficulty for specialists to insert routing in FANETs. Routing protocols have a useful role to improve the performance of these networks. This paper presents a comparison between the different dynamic routing protocols to attain the high quality of service using the NS3 simulator.

ChemsEddine Bemmoussat

Papers

UAVs-Based Mobile Radars for Real-Time Highways Surveillance

A Comparison of Different Dynamic Routing Protocols in FANETs