Tuning OLSR

Huang, Yao; Bhatti, Saleem N.; Parker, D.

doi:10.1109/pimrc.2006.254160

Cited by 55 publications

(32 citation statements)

References 7 publications

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“…We find by reducing the intervals we can recover the OLSR performance. we find that impacts of the interval time grows with the increase of network mobility and node density [28]. Kazuyoshi Soga, TomoyaTakenaka 2010: Localization protocol is important for guessing node positions in a wireless multi-hop network.…”

Section: Related Workmentioning

confidence: 88%

OLSR Parameter Tunning

Kamboj¹,

Singh²

2017

IJARCSSE

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Abstract-Vehicular ad hoc network is an infrastructure less network where each network node not only acts as a host but also acts as a router. The environment is highly dynamic due to mobile nature of nodes. The proper functioning of these networks depends upon a routing protocol that can respond to the rapid changes in the topology. Our interest is focused on the OLSR routing protocol, which uses hello and topology control (TC) messages to discover and then disseminate link state information throughout the mobile ad hoc network. We discuss the impact of Hello messages on the performance of OLSR in term of packet delivery ratio, delay and throughput. The objective of this thesis is to study the impact of tuning on the performance of mobile routing Protocol, OLSR, which is proactive routing protocol. Since not many VANETs have been deployed, most of the studies are based on simulation. Also for this thesis, experiments are conducted by network simulator 2.34 by using tool command language. A basic framework is employed to analyze the performance of routing protocol OLSR by tuning its parameters. We firstly evaluated the performance in terms of QOS by applying an optimization strategy that obtains automatically efficient OLSR parameter configurations by coupling two different stages: an optimization procedure and a simulation stage. It is observed that tuned-OLSR outperformed OLSR. The three basic parameters are tuned by applying genetic (GA), simulate annealing (SA) and particle swarm (PSO) algorithms. It shows considerable increase in throughput, packet delivery ratio and a substantial decrease in delay as compared to the respective performance of OLSR. The optimization methodology presented in this work (coupling meta heuristics and a simulator) offers the possibility of automatically and efficiently customizing any protocol for any VANET scenario.

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Section: Related Workmentioning

confidence: 88%

OLSR Parameter Tunning

Kamboj¹,

Singh²

2017

IJARCSSE

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“…However, this precise performance of OLSR significantly depends on the selection of its parameters [9], [12], [29]. For example, the detection of topological changes can be adjusted by changing the time interval for broadcasting HELLO messages.…”

Section: Problem Overviewmentioning

confidence: 99%

“…The OLSR mechanisms are regulated by a set of parameters predefined in the OLSR RFC 3626 [7] (see Table I). These parameters have been tuned by different authors without using any automatic tool in [12] and [29], and they are the timeouts before resending HELLO, MID, and TC messages (HELLO_INTERVAL, REFRESH_INTERVAL, and TC_INTERVAL, respectively); the "validity time" of the information received via these three message types, which are NEIGHB_HOLD_TIME (HELLO), MID_HOLD_TIME (MID), and TOP_HOLD_TIME (TC); the WILLINGNESS of a node to act as an MPR (to carry and forward traffic to other nodes); and DUP_HOLD_TIME, which represents the time during which the MPRs record information about the forwarded packets.…”

Section: ) Hello Messages Are Exchanged Between Neighbormentioning

confidence: 99%

“…To evaluate the quality or fitness of the different OLSR configurations (tentative solutions), we have defined a communication cost function in terms of three of the most commonly used QoS metrics in this area [9], [29]: 1) the packet delivery ratio (PDR), which is the fraction of data packets originated by an application that is completely and correctly delivered; 2) the network routing load (NRL), which is the ratio of administrative routing packet transmissions to data packets delivered, where each hop is counted separately; and finally, 3) the end-to-end delay (E2ED), which is the difference between the time a data packet is originated by an application and the time this packet is received at its destination.…”

Section: B Olsr Parameter Tuningmentioning

confidence: 99%

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Intelligent OLSR Routing Protocol Optimization for VANETs

Toutouh

García-Nieto

Alba

2012

IEEE Trans. Veh. Technol.

180

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Abstract-Recent advances in wireless technologies have given rise to the emergence of vehicular ad hoc networks (VANETs). In such networks, the limited coverage of WiFi and the high mobility of the nodes generate frequent topology changes and network fragmentations. For these reasons, and taking into account that there is no central manager entity, routing packets through the network is a challenging task. Therefore, offering an efficient routing strategy is crucial to the deployment of VANETs. This paper deals with the optimal parameter setting of the optimized link state routing (OLSR), which is a well-known mobile ad hoc network routing protocol, by defining an optimization problem. This way, a series of representative metaheuristic algorithms (particle swarm optimization, differential evolution, genetic algorithm, and simulated annealing) are studied in this paper to find automatically optimal configurations of this routing protocol. In addition, a set of realistic VANET scenarios (based in the city of Málaga) have been defined to accurately evaluate the performance of the network under our automatic OLSR. In the experiments, our tuned OLSR configurations result in better quality of service (QoS) than the standard request for comments (RFC 3626), as well as several human experts, making it amenable for utilization in VANET configurations.Index Terms-Metaheuristics, optimization algorithms, optimized link state routing (OLSR), vehicular ad hoc networks (VANET).

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“…Regarding self-healing and convergence performance, while a tremendous amount of work has been employed in the last years on simulation-based research on the performance of mobile ad-hoc networking and mesh routing protocols regarding their performance [21,22,23,24] and tuning [25,26], much less insight hs been published based on experimental analysis using emulation [27,20,3] or testbeds [28,29,19], leaving the experimental performance evaluation of changing topology aspects on mesh routing protocols surprisingly unattended.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of mesh routing protocols for wireless community networks

Neumann¹,

Lopez

Navarro

2015

Computer Networks

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In recent years, we have witnessed the exponential growth of wireless community networks as a response to the clear necessity of Internet access for participation in society. For wireless mesh networks that can scale to up to thousands of nodes, which are owned and managed in a decentralized way, it is imperative for their survival to provide the network with self-management mechanisms that reduce the requirements of human intervention and technological knowledge in the operation of a community network. In this paper, we focus on one important self-management mechanism, routing, and we study the scalability, performance, and stability of three proactive mesh routing protocols: BMX6, OLSR, and Babel. We study different metrics on an emulation framework and on the W-ILab.T testbed at iMinds, making the most of the two worlds. Emulation allows us to have more control over the topology and more systematically repeat the experiments, whereas a testbed provides a realistic wireless medium and more reliable measurements, especially in terms of interference and CPU consumption. Results show the relative merits, costs, and limitations of the three protocols.

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Tuning OLSR

Cited by 55 publications

References 7 publications

OLSR Parameter Tunning

OLSR Parameter Tunning

Intelligent OLSR Routing Protocol Optimization for VANETs

Evaluation of mesh routing protocols for wireless community networks

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