Energy Consumption Minimization in Cognitive Radio Mobile Ad-Hoc Networks using Enriched Ad-hoc On-demand Distance Vector Protocol

Ad-hoc networks with poor routing affects the complete network and it leads to enhanced delay in delivering the packets to destination and consumption of energy in an exhaustive manner. Significant task of CR-MANET is to find a better route which consumes less delay and energy. This paper proposes an enriched version of ad-hoc on-demand distance vector (EAODV) routing protocol to meet the challenges faced in CR-MANET. EAODV involves four different phases which are route request method, updating the routing table, route request on demand and route lifetime. These four phases help EAODV to avoid congestion and discover better route to destination. EAODV is intended to locate the best alternative route in a short period of time amid route failure by dynamically selecting the optimal route. Before sending the packets, EAODV evaluates the selected routes using the residual energy of nodes in the path. If it is not satisfied, then next best route is selected. Efficiency of EAODV is demonstrated by a comprehensive simulation and it got resulted in better outcomes in terms of minimizing delay and energy consumption.