A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and infrastructure.The nodes in the MANET are highly mobile and it results in adequ...A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and infrastructure.The nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery process.Route planning in MANET is a multi-hop communication process due to the restricted transmission range of the nodes.Location aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy consumption.Though few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design issue.In this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for MANETs.The EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR protocol.The fitness value of the created solutions is determined next to pass the solutions to the objective function.The MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy utilization.To ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take place.The resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared methods.The resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.展开更多
虚拟电厂中分布式能源和电动汽车等设备之间数据交换量大,需要借助通信技术实现分布式能源聚合和调度优化管理,但分配给电力通信的频谱资源有限。文章建立基于认知无线电的虚拟电厂分层架构,提出一种基于蝠鲼觅食优化–梯度优化(manta r...虚拟电厂中分布式能源和电动汽车等设备之间数据交换量大,需要借助通信技术实现分布式能源聚合和调度优化管理,但分配给电力通信的频谱资源有限。文章建立基于认知无线电的虚拟电厂分层架构,提出一种基于蝠鲼觅食优化–梯度优化(manta ray foraging optimization-gradient based optimizer,MRFO-GBO)算法的协作频谱感知技术,优化融合中心的加权向量,进而提高检测概率以及频谱利用率。与其他算法相比,MRFO-GBO算法可以更有效地用于电动汽车中的协作频谱感知,提高认知用户感知主用户的准确性。展开更多
A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious ta...A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.展开更多
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2021-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)and the Soonchunhyang University Research Fund.
文摘A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and infrastructure.The nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery process.Route planning in MANET is a multi-hop communication process due to the restricted transmission range of the nodes.Location aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy consumption.Though few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design issue.In this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for MANETs.The EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR protocol.The fitness value of the created solutions is determined next to pass the solutions to the objective function.The MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy utilization.To ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take place.The resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared methods.The resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.
文摘虚拟电厂中分布式能源和电动汽车等设备之间数据交换量大,需要借助通信技术实现分布式能源聚合和调度优化管理,但分配给电力通信的频谱资源有限。文章建立基于认知无线电的虚拟电厂分层架构,提出一种基于蝠鲼觅食优化–梯度优化(manta ray foraging optimization-gradient based optimizer,MRFO-GBO)算法的协作频谱感知技术,优化融合中心的加权向量,进而提高检测概率以及频谱利用率。与其他算法相比,MRFO-GBO算法可以更有效地用于电动汽车中的协作频谱感知,提高认知用户感知主用户的准确性。
文摘A semi supervised image classification method for satellite images is proposed in this paper.The satellite images contain enormous data that can be used in various applications.The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data.Thus,in this paper,a Radial Basis Function Neural Network(RBFNN)trained using Manta Ray Foraging Optimization algorithm(MRFO)is proposed.RBFNN is a three-layer network comprising of input,output,and hidden layers that can process large amounts.The trained network can discover hidden data patterns in unseen data.The learning algorithm and seed selection play a vital role in the performance of the network.The seed selection is done using the spectral indices to further improve the performance of the network.The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays.It emulates three unique foraging behaviours namelys chain,cyclone,and somersault foraging.The satellite images contain enormous amount of data and thus require exploration in large search space.The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively.The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager(OLI)images of New Brunswick area.The method was applied to identify and classify the land cover changes in the area induced by flooding.The images are classified using the proposed method and a change map is developed using post classification comparison.The change map shows that a large amount of agricultural area was washed away due to flooding.The measurement of the affected area in square kilometres is also performed for mitigation activities.The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased.The performance of the proposed method is done with existing state-of-the-art methods.
