Clustering Based Affinity Propagation In Vanets : Taxonomy And Opportunity Of Research

Vehicular communication networks received good consideration and focusing on diverse researchers in the latest years. Vehicular Adhoc Networks (VANETs) represents a developed type of an effective communication technology to facilitate the process of information dissemination among vehicles. VANETs established the cornerstone to develop the Intelligent Transport Systems (ITS). The great challenging task in routing the messages in VANETs is related to the different velocities of the moving vehicles on the streets in addition to their sparse distribution. Clustering approach is broadly used to report this challenge. It represents the mechanism of the alliance the vehicles based on certain metrics such as velocity, location, density, direction and lane position. This paper is to investigate and analyze several challenges and their present solutions which based on different developed clustering approaches based on the affinity propagation algorithm. This paper isaim to present a complete taxonomy on vehicles clustering and analyzing the existing submitted proposals in literature based on affinity propagation. Presenting and analyzing the submitted proposals will provide these domain researchers with a good flexibility to select or apply the suitable approach to their future application or research activities. To prepare this paper in a systematic manner, a total of 1444 articles concerning the Affinity Propagation in clustering published in the era of 2008 to 2019 were collected from the reliable publishing sources namely (ScienceDirect, IEEE Xplore, and SCOPUS). Due to their relevance, applicability, generality level and comprehensiveness, only nineteen articles among the collected articles were assigned and eventually analyzed in a systematic review method.A considerable success has been achieved in revealing the essential challenges and necessities for clustering based affinity Propagation in VANETs to guide the researchers in their upcoming investigations. This paper also contributes in dealing with open problems issues, challenges and guidelines for the upcoming investigations

FANET Drone’s Data Applications, Mobility Models and Wi-Fi IEEE 802.11n Standards for Real and Non-Real Time Traffic

Data traffic is the most important data transmission between users inside every network, these data traffic can be videos, files, voice, pictures, and many more, that divided into two types, real-time and non-real-time traffic. Most real-time traffic data has a low tolerance for the delay during transmission as they need to be fast received between communication devices. In this paper, a comprehensive analysis will be made to evaluate the two types of data transmitted through FANET drones, with different mobility models and two types of IEEE 802.11 2.4 GHz and 5 GHz by using OLSR routing protocol. Metrics such as delay and throughput will be measured. This paper gives an important overview of how real and non-real-time traffic will be handled during data transmission in FANET networks

Minimizing End-To-End Time Delay in Mobile Ad-Hoc Network using Improved Grey Wolf Optimization Based Ad-Hoc On-demand Distance Vector Protocol (IGWO-AODV)

Mobile Ad-Hoc network is one of the instant network with unknown mobile devices. Each and every node in the network may act differently. Because of this time taken to travel packet from source to destination will take more time. This work proposes Improved Grey Wolf Optimization Based Ad-Hoc On-Demand Distance Vector routing protocol for reduce the End-to-End time delay in MANET