A new routing metric and protocol for multipath routing in cognitive networks

Routing in cognitive networks is a challenging problem due to the primary users' (PU) activities and mobility. Multipath routing is a general solution to improve reliability of connections. Existing multipath routing metrics for traditional wireless networks do not take into account PUs' activities. This work introduces a new routes selection metric for multipath routing in cognitive networks. Routes closeness, the proposed metric, favors routes that are not close to each other. Selecting non-close routes makes them less vulnerable to PUs' activities as it would be less possible for an active mobile PU to interrupt all the routes at the same time. We describe our new routes closeness metric along with a routing protocol that make use of it to enhance the performance in cognitive networks. Simulation results show the effectiveness of the new metric in increasing connection reliability and end-to-end throughput to up to 48% compared to other algorithms, especially with increasing PUs' mobility and secondary user's (SUs) density.

[1]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[2]  Sang-Hwa Chung,et al.  A Node-Disjoint Multipath Routing Protocol Based on AODV in Mobile Ad Hoc Networks , 2010, 2010 Seventh International Conference on Information Technology: New Generations.

[3]  Giorgio Ventre,et al.  Network Simulator NS2 , 2008 .

[4]  David A. Maltz,et al.  DSR: the dynamic source routing protocol for multihop wireless ad hoc networks , 2001 .

[5]  Sung-Ju Lee,et al.  Split multipath routing with maximally disjoint paths in ad hoc networks , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).

[6]  N. Okazaki,et al.  Proposal of a Robust Zone-based Hierarchical Routing Method for Ad Hoc Networks , 2006, 2006 Asia-Pacific Conference on Communications.

[7]  Laurie G. Cuthbert,et al.  On-demand node-disjoint multipath routing in wireless ad hoc networks , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[8]  Jorge Urrutia,et al.  Compass routing on geometric networks , 1999, CCCG.

[9]  Dipak Ghosal,et al.  Multipath Routing in Mobile Ad Hoc Networks: Issues and Challenges , 2003, MASCOTS Tutorials.

[10]  Xiaofei Wang,et al.  A multipath routing and spectrum access (MRSA) framework for cognitive radio systems in multi-radio mesh networks , 2009, CoRoNet '09.

[11]  Mahesh K. Marina,et al.  On-demand multipath distance vector routing in ad hoc networks , 2001, Proceedings Ninth International Conference on Network Protocols. ICNP 2001.

[12]  Jack Snoeyink,et al.  Intersecting Red and Blue Line Segments in Optimal Time and Precision , 2000, JCDCG.

[13]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[14]  Francesca Cuomo,et al.  Routing in cognitive radio networks: Challenges and solutions , 2011, Ad Hoc Networks.

[15]  Jonas Holmerin,et al.  Clique Is Hard to Approximate within n1-o(1) , 2000, ICALP.

[16]  Ian F. Akyildiz,et al.  NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey , 2006, Comput. Networks.