In wireless sensor network, the primary design is to save the energy consumption as much as possible while achieving the given task. Most of recent researches works have only focused on the individual layer issues and...In wireless sensor network, the primary design is to save the energy consumption as much as possible while achieving the given task. Most of recent researches works have only focused on the individual layer issues and ignore the importance of inter working between different layers in a sensor network. In this paper, we use a cross-layer approach to propose an energy-efficient and extending the life time of the sensor network. This protocol which uses routing in the network layer, and the data scheduling in MAC layer. The main ob-jective of this paper is to provide a possible and flexible approach to solve the conflicts between the require-ments of large scale, long life-time, and multi-purpose wireless sensor networks. This OEEXLM module gives better performance compared to all other existing protocols. The performance of OEEXLM module compared with S-MAC and directed diffusion protocol.展开更多
Advances in Information Technology (IT) have enhanced our ability to gather, collect and analyze information from individuals and specific groups of people online. The emergence of online networks has facilitated conn...Advances in Information Technology (IT) have enhanced our ability to gather, collect and analyze information from individuals and specific groups of people online. The emergence of online networks has facilitated connections between individuals by leveraging data exchange in a variety of fields. Online networking in life sciences transforms data collection into actionable information that will improve individual and population health, deliver effective therapies and, consequently, reduce the cost of healthcare. These novel tools might also have a direct impact in personalized medicine programs, since the adoption of new products by health care professionals in life sciences and peer-to-peer learning could be improved using social networks and big data analytics. However, one of the main concerns of information exchange online is data privacy. In this article, we will review how online networks and big data analytics are impacting the life sciences sector.展开更多
In vehicular ad-hoc networks (VANETs), store-carry-forward approach may be used for data sharing, where moving vehicles carry and exchange data when they go by each other. In this approach, storage resource in a vehic...In vehicular ad-hoc networks (VANETs), store-carry-forward approach may be used for data sharing, where moving vehicles carry and exchange data when they go by each other. In this approach, storage resource in a vehicle is generally limited. Therefore, attributes of data that have to be stored in vehicles are an important factor in order to efficiently distribute desired data. In VANETs, there are different types of data which depend on the time and location. Such kind of data cannot be deployed adequately to the requesting vehicles only by popularity-based rule. In this paper, we propose a data distribution method that takes into account the effective life and area in addition to popularity of data. Our extensive simulation results demonstrate drastic improvements on acquisition performance of the time and area specific data.展开更多
针对当前滨湖绿道普遍存在的与居民日常生活脱节、空间形态单一、功能复合度不足等问题,提出基于日常生活理论视角的“环湖为核、林荫为簇”的绿道网络构形假设。以武汉沙湖滨湖区域为例,构建“点-线-权”的绿道网络构形框架,将交通通...针对当前滨湖绿道普遍存在的与居民日常生活脱节、空间形态单一、功能复合度不足等问题,提出基于日常生活理论视角的“环湖为核、林荫为簇”的绿道网络构形假设。以武汉沙湖滨湖区域为例,构建“点-线-权”的绿道网络构形框架,将交通通勤、商业服务、文化活动与体育健身等日常情景兴趣点(point of interest,POI)作为点要素,满足植被覆盖率标准的林荫道作为线要素,借助UNA工具开展多情景路径模拟与使用频次量化分析。结果显示:研究区域内构形的绿道网络呈现“多簇网状”的复杂结构,约51.49%的道路未与任何生活情景重叠,主要分布于湖区外围。高重叠(≥5类情景)路段仅占总长度的32.46%,说明高频生活走廊在整体绿道网络中比例有限、分布集中,且居民日常高频使用的路径主要由密度高、长度短的内部道路构成。沙湖现状绿道与高频日常路径错位率较高,沙湖现有绿道覆盖约9.3%的日常活动POI,构建的网络将覆盖度提升至约48.76%,显著增强了绿道与居民日常出行链条的耦合程度。展开更多
滚动轴承是机械设备中的常见关键部件,准确预测其剩余使用寿命对机械设备的安全稳定运行至关重要。针对目前轴承寿命预测存在的轴承退化特征不明显、模型泛化能力差以及数据长期依赖关系难以捕捉的问题,提出基于时频域信号优化器(Time-F...滚动轴承是机械设备中的常见关键部件,准确预测其剩余使用寿命对机械设备的安全稳定运行至关重要。针对目前轴承寿命预测存在的轴承退化特征不明显、模型泛化能力差以及数据长期依赖关系难以捕捉的问题,提出基于时频域信号优化器(Time-Frequency domain signal Ratio Optimizer,TFRO)的多重膨胀多核时间卷积网络(Multi inflated Multi kernel Time Convolutional Network,Mi-MkTCN)模型。TFRO优化器为了精准记忆重要信息,在每一个时间节点上,将过去信息和当前信息重组,其中过去信息中的重要的时频域特征经过了有比例的分配。Mi-MkTCN利用多重膨胀确保重要特征不丢失,再利用多核时间卷积网络实现对不同尺度特征的提取。最终的消融对比实验验证了改进方法的有效性,模型的平均绝对误差、均方误差及均方根误差指标分别为0.00145、0.05069和0.12045。实验结果表明,所提方法显著提升了轴承剩余使用寿命的预测精度,为轴承剩余使用寿命预测提供了高精度、高鲁棒性的解决方案。展开更多
The imbalance of energy consumption in wireless sensor networks(WSNs)easily results in the“hot spot”problem that the sensor nodes in a particular area die due to fast energy consumption.In order to solve the“hot s...The imbalance of energy consumption in wireless sensor networks(WSNs)easily results in the“hot spot”problem that the sensor nodes in a particular area die due to fast energy consumption.In order to solve the“hot spot”problem in WSNs,we propose an unequal clustering routing algorithm based on genetic algorithm(UCR-GA).In the cluster head election phase,the fitness function is constructed based on the residual energy,density and distance between nodes and base station,and the appropriate node is selected as the cluster head.In the data transmission phase,the cluster head selects single-hop or multi-hop communication mode according to the distance to the base station.After we comprehensively consider the residual energy of the cluster head and its communication energy consumption with the base station,an appropriate relay node is selected.The designed protocal is simulated under energy homogeneous and energy heterogeneity conditions,and the results show that the proposed routing protocal can effectively balance energy consumption,prolong the life cycle of network,and is appicable to heterogeneous networks.展开更多
Wireless sensor networks are provided with a limited source of power. The lifetime of such networks is an overwhelming matter in most network applications. This lifetime depends strongly on how efficiently such energy...Wireless sensor networks are provided with a limited source of power. The lifetime of such networks is an overwhelming matter in most network applications. This lifetime depends strongly on how efficiently such energy is distributed over the nodes especially during transmitting and receiving data. Each node may route messages to destination nodes either through short hops or long hops. Optimizing the length of these hops may save energy, and therefore extend the lifetime of WSNs. In this paper, we propose a theorem to optimize the hop’s length so to make WSN power consumption minimal. The theorem establishes a simple condition on hop’s length range. Computer simulation when performing such condition on Mica2 sensors and Mica2dot sensors reveals good performance regarding WSNs energy consumption.展开更多
As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract ke...As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).展开更多
文摘In wireless sensor network, the primary design is to save the energy consumption as much as possible while achieving the given task. Most of recent researches works have only focused on the individual layer issues and ignore the importance of inter working between different layers in a sensor network. In this paper, we use a cross-layer approach to propose an energy-efficient and extending the life time of the sensor network. This protocol which uses routing in the network layer, and the data scheduling in MAC layer. The main ob-jective of this paper is to provide a possible and flexible approach to solve the conflicts between the require-ments of large scale, long life-time, and multi-purpose wireless sensor networks. This OEEXLM module gives better performance compared to all other existing protocols. The performance of OEEXLM module compared with S-MAC and directed diffusion protocol.
文摘Advances in Information Technology (IT) have enhanced our ability to gather, collect and analyze information from individuals and specific groups of people online. The emergence of online networks has facilitated connections between individuals by leveraging data exchange in a variety of fields. Online networking in life sciences transforms data collection into actionable information that will improve individual and population health, deliver effective therapies and, consequently, reduce the cost of healthcare. These novel tools might also have a direct impact in personalized medicine programs, since the adoption of new products by health care professionals in life sciences and peer-to-peer learning could be improved using social networks and big data analytics. However, one of the main concerns of information exchange online is data privacy. In this article, we will review how online networks and big data analytics are impacting the life sciences sector.
文摘In vehicular ad-hoc networks (VANETs), store-carry-forward approach may be used for data sharing, where moving vehicles carry and exchange data when they go by each other. In this approach, storage resource in a vehicle is generally limited. Therefore, attributes of data that have to be stored in vehicles are an important factor in order to efficiently distribute desired data. In VANETs, there are different types of data which depend on the time and location. Such kind of data cannot be deployed adequately to the requesting vehicles only by popularity-based rule. In this paper, we propose a data distribution method that takes into account the effective life and area in addition to popularity of data. Our extensive simulation results demonstrate drastic improvements on acquisition performance of the time and area specific data.
文摘针对当前滨湖绿道普遍存在的与居民日常生活脱节、空间形态单一、功能复合度不足等问题,提出基于日常生活理论视角的“环湖为核、林荫为簇”的绿道网络构形假设。以武汉沙湖滨湖区域为例,构建“点-线-权”的绿道网络构形框架,将交通通勤、商业服务、文化活动与体育健身等日常情景兴趣点(point of interest,POI)作为点要素,满足植被覆盖率标准的林荫道作为线要素,借助UNA工具开展多情景路径模拟与使用频次量化分析。结果显示:研究区域内构形的绿道网络呈现“多簇网状”的复杂结构,约51.49%的道路未与任何生活情景重叠,主要分布于湖区外围。高重叠(≥5类情景)路段仅占总长度的32.46%,说明高频生活走廊在整体绿道网络中比例有限、分布集中,且居民日常高频使用的路径主要由密度高、长度短的内部道路构成。沙湖现状绿道与高频日常路径错位率较高,沙湖现有绿道覆盖约9.3%的日常活动POI,构建的网络将覆盖度提升至约48.76%,显著增强了绿道与居民日常出行链条的耦合程度。
文摘滚动轴承是机械设备中的常见关键部件,准确预测其剩余使用寿命对机械设备的安全稳定运行至关重要。针对目前轴承寿命预测存在的轴承退化特征不明显、模型泛化能力差以及数据长期依赖关系难以捕捉的问题,提出基于时频域信号优化器(Time-Frequency domain signal Ratio Optimizer,TFRO)的多重膨胀多核时间卷积网络(Multi inflated Multi kernel Time Convolutional Network,Mi-MkTCN)模型。TFRO优化器为了精准记忆重要信息,在每一个时间节点上,将过去信息和当前信息重组,其中过去信息中的重要的时频域特征经过了有比例的分配。Mi-MkTCN利用多重膨胀确保重要特征不丢失,再利用多核时间卷积网络实现对不同尺度特征的提取。最终的消融对比实验验证了改进方法的有效性,模型的平均绝对误差、均方误差及均方根误差指标分别为0.00145、0.05069和0.12045。实验结果表明,所提方法显著提升了轴承剩余使用寿命的预测精度,为轴承剩余使用寿命预测提供了高精度、高鲁棒性的解决方案。
基金National Natural Science Foundation of China(No.61862038)Lanzhou Talent Innovation and Entrepreneurship Technology Plan Project(No.2019-RC-14)Foundation of a Hundred Youth Talents Training Program of Lanzhou Jiaotong University。
文摘The imbalance of energy consumption in wireless sensor networks(WSNs)easily results in the“hot spot”problem that the sensor nodes in a particular area die due to fast energy consumption.In order to solve the“hot spot”problem in WSNs,we propose an unequal clustering routing algorithm based on genetic algorithm(UCR-GA).In the cluster head election phase,the fitness function is constructed based on the residual energy,density and distance between nodes and base station,and the appropriate node is selected as the cluster head.In the data transmission phase,the cluster head selects single-hop or multi-hop communication mode according to the distance to the base station.After we comprehensively consider the residual energy of the cluster head and its communication energy consumption with the base station,an appropriate relay node is selected.The designed protocal is simulated under energy homogeneous and energy heterogeneity conditions,and the results show that the proposed routing protocal can effectively balance energy consumption,prolong the life cycle of network,and is appicable to heterogeneous networks.
文摘Wireless sensor networks are provided with a limited source of power. The lifetime of such networks is an overwhelming matter in most network applications. This lifetime depends strongly on how efficiently such energy is distributed over the nodes especially during transmitting and receiving data. Each node may route messages to destination nodes either through short hops or long hops. Optimizing the length of these hops may save energy, and therefore extend the lifetime of WSNs. In this paper, we propose a theorem to optimize the hop’s length so to make WSN power consumption minimal. The theorem establishes a simple condition on hop’s length range. Computer simulation when performing such condition on Mica2 sensors and Mica2dot sensors reveals good performance regarding WSNs energy consumption.
基金Dr.Deepak Dahiya would like to thank Deanship of Scientific Research at Majmaah University for supporting his work under Project No.(R-2022-96)。
文摘As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).
