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OPERA: Optimal Routing Metric for Cognitive Radio Ad Hoc Networks

Identifieur interne : 000905 ( Main/Merge ); précédent : 000904; suivant : 000906

OPERA: Optimal Routing Metric for Cognitive Radio Ad Hoc Networks

Auteurs : Marcello Caleffi [États-Unis, Italie] ; Ian F. Akyildiz [États-Unis] ; Luigi Paura [Italie]

Source :

RBID : Pascal:12-0341345

Descripteurs français

English descriptors

Abstract

Two main issues affect the existing routing metrics for cognitive radio ad hoc networks: i) they are often based on heuristics, and thus they have not been proved to be optimal; ii) they do not account for the route diversity effects, and thus they are not able to measure the actual cost of a route. In this paper, an optimal routing metric for cognitive radio ad hoc networks, referred to as OPERA, is proposed. OPERA is designed to achieve two features: i) Optimality: OPERA is optimal when combined with both Dijkstra and Bellman-Ford based routing protocols; ii) Accuracy: OPERA exploits the route diversity provided by the intermediate nodes to measure the actual end-to-end delay, by taking explicitly into account the unique characteristics of cognitive radio networks. A closed-form expression of the proposed routing metric is analytically derived for both static and mobile networks, and its optimality is proved rigorously. Performance evaluation is conducted through simulations, and the results reveal the benefits of adopting the proposed routing metric for cognitive radio ad hoc networks.

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Pascal:12-0341345

Le document en format XML

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<div type="abstract" xml:lang="en">Two main issues affect the existing routing metrics for cognitive radio ad hoc networks: i) they are often based on heuristics, and thus they have not been proved to be optimal; ii) they do not account for the route diversity effects, and thus they are not able to measure the actual cost of a route. In this paper, an optimal routing metric for cognitive radio ad hoc networks, referred to as OPERA, is proposed. OPERA is designed to achieve two features: i) Optimality: OPERA is optimal when combined with both Dijkstra and Bellman-Ford based routing protocols; ii) Accuracy: OPERA exploits the route diversity provided by the intermediate nodes to measure the actual end-to-end delay, by taking explicitly into account the unique characteristics of cognitive radio networks. A closed-form expression of the proposed routing metric is analytically derived for both static and mobile networks, and its optimality is proved rigorously. Performance evaluation is conducted through simulations, and the results reveal the benefits of adopting the proposed routing metric for cognitive radio ad hoc networks.</div>
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