Survey and Review on Various Topology and Geographical based Routing Protocol Parameters to Ensure the QOS Parameters of VANET

Vehicular Ad Hoc Network (VANET) is a type of wireless network that allows communication between vehicles and infrastructure. One of the critical considerations in VANET is Quality of Service (QoS) parameters, which determine the network's performance. The effective management of QoS parameters is essential for VANET's reliable and efficient operation. In this research paper, we aim to explore topology-based and geographical-based routing protocol parameters to ensure QoS parameters in VANET. The former uses the network topology to make routing decisions, while the latter uses the location information of vehicles.  We will first provide an overview of VANET and QoS parameters. Then, we will delve into the key parameters of topology-based and geographical-based routing protocols and how they affect QoS. We will also survey and review the existing routing protocols and parameter values used in these protocols. The findings of this research paper will provide insights into the effective management of QoS parameters in VANET and contribute to the development of more efficient routing protocols

Data Gathering and Dissemination over Flying Ad-hoc Networks in Smart Environments

The advent of the Internet of Things (IoT) has laid the foundations for new possibilities in our life. The ability to communicate with any electronic device has become a fascinating opportunity. Particularly interesting are UAVs (Unmanned Airborne Vehicles), autonomous or remotely controlled flying devices able to operate in many contexts thanks to their mobility, sensors, and communication capabilities. Recently, the use of UAVs has become an important asset in many critical and common scenarios; thereby, various research groups have started to consider UAVs’ potentiality applied on smart environments. UAVs can communicate with each other forming a Flying Ad-hoc Network (FANET), in order to provide complex services that requires the coordination of several UAVs; yet, this also generates challenging communication issues. This dissertation starts from this standpoint, firstly focusing on networking issues and potential solutions already present in the state-of-the-art. To this aim, the peculiar issues of routing in mobile, 3D shaped ad-hoc networks have been investigated through a set of simulations to compare different ad-hoc routing protocols and understand their limits. From this knowledge, our work takes into consideration the differences between classic MANETs and FANETs, highlighting the specific communication performance of UAVs and their specific mobility models. Based on these assumptions, we propose refinements and improvements of routing protocols, as well as their linkage with actual UAV-based applications to support smart services. Particular consideration is devoted to Delay/Disruption Tolerant Networks (DTNs), characterized by long packet delays and intermittent connectivity, a critical aspect when UAVs are involved. The goal is to leverage on context-aware strategies in order to design more efficient routing solutions. The outcome of this work includes the design and analysis of new routing protocols supporting efficient UAVs’ communication with heterogeneous smart objects in smart environments. Finally, we discuss about how the presence of UAV swarms may affect the perception of population, providing a critical analysis of how the consideration of these aspects could change a FANET communication infrastructure

Using artificial intelligence to support emerging networks management approaches

In emergent networks such as Internet of Things (IoT) and 5G applications, network traffic estimation is of great importance to forecast impacts on resource allocation that can influence the quality of service. Besides, controlling the network delay caused with route selection is still a notable challenge, owing to the high mobility of the devices. To analyse the trade-off between traffic forecasting accuracy and the complexity of artificial intelligence models used in this scenario, this work first evaluates the behavior of several traffic load forecasting models in a resource sharing environment. Moreover, in order to alleviate the routing problem in highly dynamic ad-hoc networks, this work also proposes a machine-learning-based routing scheme to reduce network delay in the high-mobility scenarios of flying ad-hoc networks, entitled Q-FANET. The performance of this new algorithm is compared with other methods using the WSNet simulator. With the obtained complexity analysis and the performed simulations, on one hand the best traffic load forecast model can be chosen, and on the other, the proposed routing solution presents lower delay, higher packet delivery ratio and lower jitter in highly dynamic networks than existing state-of-art methods

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: Smart city mobility off to a flying start with self‐organized drone‐based networks

Due to recent advancements in smart city traffic and transport monitoring industry 4.0 applications. Flying Ad-Hoc Networks (FANETs) ability to cover geographically large areas, makes it a suitable technology to address the challenges faced during remote areas traffic monitoring. The implementation of drone based FANETs have several advantages in remote traffic monitoring, including free air-to-air drone assisted communication zone and smart surveillance and security. The drone-based FANETs can be deployed within minutes without requiring physical infrastructure, making it suitable for mission critical applications in several areas of interests. Here a drone-based FANETs application for smart city remote traffic monitoring is presented while addressing several challenges including coverage of larger geographical area and data communication links between FANETs nodes. A FANET-inspired enhanced ACO algorithm that easily coped with drone assisted technology of FANETs is proposed to cover the large areas. Simulation results are presented to compare the proposed technique against different network lifetime and number of received packets. The presented results show that the proposed technique perform better compared to other state-of-the-art techniques

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

A dynamic clustering mechanism with load-balancing for Flying Ad Hoc NETworks

Flying Ad Hoc NETworks (FANETs) are expected to have a significant impact in several use-cases, from smart agriculture and cities, to mission critical scenarios. The recent surge in the use of FANETs is motivated by their adaptable and flexible behaviour in different scenarios (e.g. disaster-hit locations) allowing the usage of services that require information from remote locations, such as for assessment of damages, checking for survivors, or providing onsite views to assist rescue teams. While FANETs have been developed to provide such critical services, disseminating data with proper performance faces challenges due to inherent properties of FANETs, namely frequent wireless disconnections, intermittent available nodes, and dynamic topologies, mostly when facing an increasing number of deployed unmanned aerial vehicles. Aiming to tackle these challenges, we propose a new Dynamic Clustering Mechanism with Load-Balancing able to support efficient dissemination of data packets in FANETs while ensuring good reliability and scalability factors. The proposed solution is based on the combination of a new meta-heuristic optimization scheme, known as Political Optimizer, used to perform clustering while addressing limitations caused by topology changes, and a new Shannon entropy function implemented to address cluster fault tolerant and traffic overloads. Simulation results show that by combining our proposed model with standard position-based routing protocols, a higher number of end-to-end transmissions are ensured, while supporting an average packet delivery ratio of 97%, an average end-to-end delay of 0.225 seconds, and an average power consumption 37% lower than other state-of-the-art clustering protocols