A Heuristic for Efficient Broadcasting in the Metropolitan Ad hoc Network

Hogie, Luc; Guinand, Frédéric; Bouvry, Pascal

doi:10.1007/978-3-540-30132-5_99

Cited by 14 publications

(12 citation statements)

References 6 publications

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“…Its behavior is twofold: stations carry out the broadcasting strategy both on message reception and on notification of a new connection [9].…”

Section: Broadcasting Strategymentioning

confidence: 99%

“…In order to allow the communication between stations that aren't directly connected, the networks establish routing protocols ad hoc as such LMR (Land Mobile Radio) [3], Link Reversal [4], DSR (Dynamic Source Routing) [5], OLRS (Optimized Link State Routing) [6], DSDV (Dynamic Destination Sequenced Distance-Vector Routing) [7], TORA (Temporally-Ordered Routing Algorithm) [8], or DFCN (Delayed Flooding with Cumulative Neighborhood) [9] to search routes towards a receptor. Hence, having a well-tuned broadcasting strategy will produce a major impact in network performance.…”

Section: Introductionmentioning

confidence: 99%

“…Optimizing a broadcasting strategy is a multiobjective problem where multiple functions have to be satisfied at the same time: maximizing the number of stations reached, minimizing the network use, and minimizing the makespan are three examples of the potential objectives. In this work, the broadcasting strategy considered for optimization is DFCN [9], and the target networks are metropolitan MANETs. Since manipulating such networks is difficult, we must rely on software simulations for evaluating the scenarios from the designer point of view.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Perez-Perez

Luque

Cervantes

et al. 2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-A mobile adhoc network (MANETs) is a selfconfiguring network of mobile routers (and associated hosts). The routers tend to move randomly and organize themselves arbitrarily; thus, the network's wireless topology may change fast and unpredictably. Nowadays, these networks are having a great influence due to the fact that they can create networks without a specific infrastructure. In MANETs message broadcasting is critical to network existence and organization. The broadcasting strategy in MANETs can be optimized by defining a multiobjective problem whose inputs are the broadcasting algorithm's parameters and whose objectives are: reaching as many stations as possible, minimizing the network utilization, and reducing the makespan. The network can be simulated to obtain the expected response to a given set of parameters. In this paper, we face this multiobjective problem with two algorithms: Multiobjective Particle Swarm Optimization and ESN (Evolution Strategy with NSGAII). Both algorithms are able to find an accurate approximation to the Paretooptimal front that is the solution of the problem. ESN improves the results of MOPSO in terms of the set coverage and hypervolume metrics used for comparison.

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“…Its behavior is twofold: stations carry out the broadcasting strategy both on message reception and on notification of a new connection [9].…”

Section: Broadcasting Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Perez-Perez

Luque

Cervantes

et al. 2007

2007 IEEE Congress on Evolutionary Computation

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“…In this work, the broadcasting strategy considered for optimization is DFCN [2], and the target networks are metropolitan MANETs. This MOP has been called DFCNT standing for DFCN Tuning.…”

Section: Fig 1 An Example Of Metropolitan Manetmentioning

confidence: 99%

“…Once we have the target network, the broadcasting strategy used to be tuned is DFCN [2]. We have chosen this protocol because it has been specifically designed for metropolitan MANET scenarios.…”

Section: Dfcnt: Problem Statementmentioning

confidence: 99%

Metaheuristic Approaches for Optimal Broadcasting Design in Metropolitan MANETs

Alba

Cervantes

Gómez

et al.

Computer Aided Systems Theory – EUROCAST 2007

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Abstract. Mobile Ad-hoc Networks (MANETs) are composed of a set of communicating devices which are able to spontaneously interconnect without any pre-existing infrastructure. In such scenario, broadcasting becomes an operation of tremendous importance for the own existence and operation of the network. Optimizing a broadcasting strategy in MANETs is a multiobjective problem accounting for three goals: reaching as many stations as possible, minimizing the network utilization, and reducing the duration of the operation itself. This research, which has been developed within the OPLINK project (http://oplink.lcc.uma.es), faces a wide study about this problem in metropolitan MANETs with up to seven different advanced multiobjective metaheuristics. They all compute Pareto fronts of solutions which empower a human designer with the ability of choosing the preferred configuration for the network. The quality of these fronts is evaluated by using the hypervolume metric. The obtained results show that the SPEA2 algorithm is the most accurate metaheuristic for solving the broadcasting problem.

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Broadcasting Protocol

2014

Evolutionary Algorithms for Mobile Ad Hoc Networks

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A Heuristic for Efficient Broadcasting in the Metropolitan Ad hoc Network

Cited by 14 publications

References 6 publications

Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Metaheuristic Approaches for Optimal Broadcasting Design in Metropolitan MANETs

Broadcasting Protocol

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