Efficient routing technique in cooperative cognitive radio networks in underwater environment

Abstract

newline The characteristics of Underwater Acoustic Networks make routing a challenging task. In underwater, the temperature, level of saltiness, density of sea water keeps changing and other factors such as Water Column Variations, Sedimentation Drift, Doppler Effect, Geometric Spreading, Ocean Circulation, and so on influences the communication between the nodes. The UWASN routing algorithms considers only temperature and salinity in ocean. The proposed algorithms such as Cat Optimized Cognitive Acoustic Network (COCAN) algorithm and Lion Optimized Cognitive Acoustic Network (LOCAN) algorithm focuses on channel coherence and multipath interference caused during data transmission, the water column variation due to geometric spreading, the Doppler effect due to the nodes frequent movement, and Sedimentation drift due to wave fronts in the ocean. The feature of Cat and Lion in COCAN and LOCAN respectively improves the performance of the sensor nodes. Furthermore, The algorithms incorporate cognitive thinking in finding the effective best channel for communication. The best next hop for forwarding the packet is chosen based on the nodes distance to the destination node. The proposed Cooperative Ray Optimization Algorithm (Co-ROA) uses relay nodes for transferring the data packets in addition to the next hop nodes. The sensor node with the higher energy level and closer in distance to the destination node is selected as the relay node that uses Amplify and Forward technique to route the packet to the destination node. LOCAN and COCAN are simulated using AquaSim Simulation tool. Co-ROA is simulated in NS2 Simulation tool. The overall performance of the proposed algorithms LOCAN, COCAN out stands the performance of existing algorithms such as AODV and OR. Co-ROA out stands the performance of AODV, DSDV, DSR and DEAC. newline newline