In Underwater Wireless Sensor Networks(UWSNs),the most important challenging issues are propagation delay,high error probability,high latency,high communication cost,limited bandwidth,limited memory,low packet deliver...In Underwater Wireless Sensor Networks(UWSNs),the most important challenging issues are propagation delay,high error probability,high latency,high communication cost,limited bandwidth,limited memory,low packet delivery ratio,and transmission loss.In our proposed work,the various efforts are taken to minimize the propagation delay and transmission loss during data transmission in an underwater environment.A hybrid mechanism is implemented to improve energy efficiency for faster data transmission in underwater WSN.In the underwater environment of acoustic channel condition,propagation delay and transmission loss lead to high complexity in accessing the information and also it is difficult to obtain the Channel Status Information(CSI).To address this problem,Ant Colony Optimization(ACO)routing with Markov Chain Monte Carlo(MCMC)algorithm is used and to capture the transmission loss in the MCMC approach,CSI Forecast Prediction(FP)algorithm is used.The experimental simulations are evaluated by utilizing the performance evaluation metrics such as Transmission Loss,Probability Density Function,Average Delay and Throughput.From the simulation results it is evident that the proposed algorithm,ACO-MCMC has recorded the minimum transmission loss,increase in probability density function,minimum average delay and maximum throughput of the network when compared to the existing algorithms.展开更多
文摘In Underwater Wireless Sensor Networks(UWSNs),the most important challenging issues are propagation delay,high error probability,high latency,high communication cost,limited bandwidth,limited memory,low packet delivery ratio,and transmission loss.In our proposed work,the various efforts are taken to minimize the propagation delay and transmission loss during data transmission in an underwater environment.A hybrid mechanism is implemented to improve energy efficiency for faster data transmission in underwater WSN.In the underwater environment of acoustic channel condition,propagation delay and transmission loss lead to high complexity in accessing the information and also it is difficult to obtain the Channel Status Information(CSI).To address this problem,Ant Colony Optimization(ACO)routing with Markov Chain Monte Carlo(MCMC)algorithm is used and to capture the transmission loss in the MCMC approach,CSI Forecast Prediction(FP)algorithm is used.The experimental simulations are evaluated by utilizing the performance evaluation metrics such as Transmission Loss,Probability Density Function,Average Delay and Throughput.From the simulation results it is evident that the proposed algorithm,ACO-MCMC has recorded the minimum transmission loss,increase in probability density function,minimum average delay and maximum throughput of the network when compared to the existing algorithms.
