Direct URL: http://www.ijtsrd.com/engineering/computer-engineering/18289/energy-aware-multipath-routing-scheme-in-ad-hoc-network-using-fitness-function/saba-nawashin-badal
open access journal of engineering, ugc approved journals for engineering, call for paper engineering
Mobile ad hoc network is a group of wireless mobile nodes that dynamically form a short-term network without the reliance of any infrastructure or central administration. Energy consumption is considered as one of the major constraints in MANET, as the mobile links do not possess permanent power supply and have to rely on batteries, thus reducing network lifetime as batteries get exhausted very quickly as links move and change their positions rapidly across MANET.. The proposed protocol is called AOMDV with the fitness function (FF-AOMDV). The fitness function is used to find the optimal path from source node to destination node to reduce the energy consumption in multipath routing. The performance of the proposed FF-AOMDV protocol has been evaluated by using network simulator version 2, where the performance was compared with AOMDV and ad hoc on demand multipath routing with life maximization (AOMR-LM) protocols, the two most popular protocols proposed in this area. The comparison was evaluated based on energy consumption, throughput, packet delivery ratio, end-to-end delay, network lifetime and routing overhead ratio performance metrics, varying the node speed, packet size, and simulation time. The results clearly demonstrate that the proposed FF-AOMDV outperformed AOMDV and AOMR-LM under majority of the network performance metrics and parameters.Â
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best international journal, manuscript submission, arts journal
Genetic Algorithm (GA) has emerged as a powerful tool to discover optimal for multidimensional knapsack problem (MDKP). Multidimensional knapsack problem has recognized as NP-hard problem whose applications in many areas like project selection, capital budgeting, loading problems, cutting stock etc. Attempts has made to develop cluster genetic algorithm (CGA) by mean of modified selection and modified crossover operators of GA. Clustered genetic algorithm consist of (1) fuzzy roulette wheel selection for individual selection to form the mating pool (2) A different kind of crossover operator which employ hierarchical clustering method to form two clusters from individuals of mating pool. CGA performance has examined against GA with respect to 30 benchmark problems for multi-dimensional knapsack. Experimental results show that CGA has significant improvement over GA in relation to discover optimal and CPU running time. The data set for MDKPÂ
