Belief Propagation-Based Cognitive Routing in Maritime Ad Hoc Networks

Cognitive routing for maritime wireless ad hoc networks is proposed in this paper to find a stable path between source and destination. This is ship-to-ship communication where two ships can communicate when they not only have consensus on a common idle channel but also are in the transmission range of each other. We apply belief propagation algorithm for collaborative spectrum sensing. Every user (ship) exchanges its local decisions with its neighbors to compute the final belief about the state of the channel. These beliefs are applied for estimating the link duration for total number of hops between source and destination. Then, a path is selected which maximizes the path duration among all the paths in the network to reach the destination. We apply both flood-based and geographical routing protocols to find a route between source and destination for different scenarios. We simulate our scheme for different ocean settings and evaluate path durations for different number of ships. The results report a significant increase in path duration as the number of nodes increases in the network. In addition, we verify that path duration increases with an increase in probability of primary user being idle via extensive simulations. Hence, our scheme provides stable path selection for communication among users onboard.

[1]  Chen Tang,et al.  Spectrum sensing for cognitive maritime VHF networks , 2014, OCEANS 2014 - TAIPEI.

[2]  Ming-Tuo Zhou,et al.  A routing approach for inter-ship communications in wireless multi-hop networks , 2008, 2008 8th International Conference on ITS Telecommunications.

[3]  W. Pierson,et al.  A proposed spectral form for fully developed wind seas based on the similarity theory of S , 1964 .

[4]  Ian J. Timmins,et al.  Marine Communications Channel Modeling Using the Finite-Difference Time Domain Method , 2009, IEEE Transactions on Vehicular Technology.

[5]  Charles E. Perkins,et al.  Ad-hoc on-demand distance vector routing , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[6]  John Woods,et al.  Performance evaluation of MANET routing protocols in a maritime environment , 2014, 2014 6th Computer Science and Electronic Engineering Conference (CEEC).

[7]  Ian F. Akyildiz,et al.  Optimal spectrum sensing framework for cognitive radio networks , 2008, IEEE Transactions on Wireless Communications.

[8]  Husheng Li,et al.  Collaborative Spectrum Sensing in Cognitive Radio Vehicular Ad Hoc Networks: Belief Propagation on Highway , 2010, 2010 IEEE 71st Vehicular Technology Conference.

[9]  Ming-Tuo Zhou,et al.  Cognitive maritime wireless mesh/ad hoc networks , 2012, J. Netw. Comput. Appl..

[10]  Brad Karp,et al.  GPSR : Greedy Perimeter Stateless Routing for Wireless , 2000, MobiCom 2000.

[11]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[12]  Li Hao,et al.  Spectrum sensing with energy detection in cognitive Vehicular Ad hoc Networks , 2014, 2014 IEEE 6th International Symposium on Wireless Vehicular Communications (WiVeC 2014).

[13]  Hyung Seok Kim,et al.  Two-state routing protocol for maritime multi-hop wireless networks , 2013, Comput. Electr. Eng..

[14]  William T. Freeman,et al.  Understanding belief propagation and its generalizations , 2003 .

[15]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[16]  Hao Chen,et al.  Expected path duration maximized routing algorithm in CR-VANETs , 2012, 2012 1st IEEE International Conference on Communications in China (ICCC).

[17]  Ming-Tuo Zhou,et al.  TRITON: high-speed maritime wireless mesh network , 2013, IEEE Wireless Communications.

[18]  Hyung Seok Kim,et al.  Optimal entropy-based spectrum sensing for cognitive radio networks under severe path loss conditions , 2013, 8th International Conference on Cognitive Radio Oriented Wireless Networks.