Performance evaluation of PUMA routing protocol for Manhattan mobility model on vehicular ad-hoc network

Perdana, Doan; Nanda, Muhammad Achirul; Ode, Revient Noor; Sari, Riri Fitri

doi:10.1109/ict.2015.7124661

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…It assumes that vehicle has a numerical map using destination also source addresses to find the best route. Authors in [14] presented an approach in which each node has a weight. Weights are calculated using connection quality, mobility and so on.…”

Section: Literaturementioning

confidence: 99%

Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks

Javadpour

Rezaei

et al. 2021

Preprint

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VANETs are organized to progress road protection with no specific need for any fixed infrastructure. Subsequently, the movement of all vehicles can be planned in the upcoming future, based on perceived information, Quality of Services Routing (QoSR) algorithms can be pressured on its available options, paths, and links and according to criteria and reliability of the QoSR. Awareness of QoSR to the environmental conditions of the network of vehicles, such as the location of vehicles, direction and speed that can be obtained. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path / link. In this article, A QoSR with Particle Swarm Optimization (QoSR-PSO) for improving QoSs in vehicular ad-hoc networks has been used. The particle swarm optimization algorithm by modeling the behavior of a set of particles looks for the optimal solution of the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop and overload.

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Section: Literaturementioning

confidence: 99%

Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks

Javadpour

Rezaei

et al. 2021

Preprint

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“…We also propose a comparison between static, dynamic and probabilistic algorithms used by the PRE-VE protocol. The parameters shown in Table V represent the main simulation environment while, as area map, we choose a portion of NYC (USA), because its particular road configuration has been already used in several works [40]. The simulation is performed by using two different types of load: the first one limits the number of vehicles across the city and the frequency of vehicle generation; the second one presents a huge number of vehicles generated with a higher frequency.…”

Section: B Simulation Campaignsmentioning

confidence: 99%

A New Distributed Predictive Congestion Aware Re-Routing Algorithm for CO₂ Emissions Reduction

Santamaria

Fazio

Raimondo

et al. 2019

IEEE Trans. Veh. Technol.

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In the last years, vehicular networking has grown up in terms of interest and transmission capability, due to the possibility of exploiting the distributed communication paradigm in a mobile scenario, where moving nodes are represented by vehicles. The different existing standards for vehicular ad-hoc networks, such as dedicate short range communication (DSRC), wireless access for vehicular environment (WAVE)/IEEE802.11p, have given to the research community the possibility of developing new medium access control (MAC) and routing schemes, in order to enhance the quality and the comfort of mobile users who are driving their vehicles. In this paper, we focus our attention on the optimization of traffic flowing in a vehicular environment with vehicle-2-roadside capability. As shown later, the proposed idea exploits the information that is gathered by road-side units to redirect traffic flows (in terms of vehicles) to less congested roads, with an overall system optimization, also in terms of carbon dioxide emissions reduction. An analytical model, as well as a set of pseudo-code instructions, have been introduced in the paper. A deep campaign of simulations has been carried out to give more effectiveness to our proposal. Index Terms-VANET, IEEE 802.11p, wireless access for vehicular environment (WAVE), dedicate Short Range Communication (DSRC), congestion, traffic flow, predictive re-routing, CO 2 emissions. I. INTRODUCTIONI N THE last years, the interest in vehicular opportunities and potentialities has drastically grown, due to the numerous advantages inherited from mobility solutions in urban/suburban environments. In particular, Vehicular Ad-hoc NETworks (VANETs) represent a new and modern paradigm of communication, where the nodes are able to communicate in a distributed manner, based on the ad-hoc paradigm [1], [2].VANETs are going to be considered as one of the first adhoc networking reality in nowadays applications, enabling the transmission among nearby vehicles as well as between vehicles and nearby external devices. In fact, each node is equipped with a wireless device, the On-Board Unit (OBU), which is able to interact with the infrastructure equipment for comfort/security applications, trade and infotainment services.

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“…It can be used by attackers to perform the attack. The attack aims to destabilize a running cooperative algorithm [3]. It makes VANET network vulnerable to attacks that can cause problems even on small networks.…”

Section: Introductionmentioning

confidence: 99%

Network Security Analysis in Vanet Against Black Hole and Jellyfish Attack with Intrusion Detection System Algorithm

Mustikawati

Perdana

Negara

2017

CommIT (Communication and Information Technology) Journal

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VANET is the key to the Intelligent Transportation Systems (ITS), where vehicles can communicate with others to exchange information in real time. VANET is an ad-hoc that has no fixed infrastructure and rapidly changing network topology. As the result, the network is insecure and vulnerable to various attacks both from within and outside the network. This research analyzes AODV routing protocol comparing the conditions without the attacks and with the attacks with the of black hole and jellyfish using the algorithm of Intrusion Detection System with the number of nodes changing from 10 to 100 nodes at the change speeds of 70, 80, 90, 100, 110, and 120 km/h. This research is simulated using Network Simulator 2 to model the network and ONESimulator to model node mobility. The analyzed QoS parameters are packet delivery ratio (PDR), throughput, and end-to-end delay. The results of the simulation show that changing the number of nodes and node velocity affects the performance in the network. On the number of nodes scenario with attacks, the average value of PDR decreases by 48.03%, throughput decreases by 50.23%, and delay, for black hole, decreases by 80.18% but increases by 47.87% for jellyfish. Whereas in the node velocity scenario, the average values of PDR, throughput, and delay decrease by 58.52%, 60.34%, 13.81% for blackhole attack, respectively. However, the delay increases by 123.91% for jellyfish attack.

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Performance evaluation of PUMA routing protocol for Manhattan mobility model on vehicular ad-hoc network

Cited by 14 publications

References 12 publications

Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks

Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks

A New Distributed Predictive Congestion Aware Re-Routing Algorithm for CO₂ Emissions Reduction

Network Security Analysis in Vanet Against Black Hole and Jellyfish Attack with Intrusion Detection System Algorithm

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Performance evaluation of PUMA routing protocol for Manhattan mobility model on vehicular ad-hoc network

Cited by 14 publications

References 12 publications

Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks

Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks

A New Distributed Predictive Congestion Aware Re-Routing Algorithm for CO2 Emissions Reduction

Network Security Analysis in Vanet Against Black Hole and Jellyfish Attack with Intrusion Detection System Algorithm

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A New Distributed Predictive Congestion Aware Re-Routing Algorithm for CO₂ Emissions Reduction