This paper concerns the data-driven sensor deployment problem in large spatiotemporal fields.Traditionally,sensor deployment strategies have been heavily dependent on model-based planning approaches.However,model-base...This paper concerns the data-driven sensor deployment problem in large spatiotemporal fields.Traditionally,sensor deployment strategies have been heavily dependent on model-based planning approaches.However,model-based approaches do not typically maximize the information gain in the field,which tend to generate less effective sampling locations and lead to high reconstruction error.In the present paper,a data-driven approach is developed to overcome the drawbacks of the model-based approach and improve the spatiotemporal field reconstruction accuracy.The proposed method can select the most informative sampling locations to represent the entire spatiotemporal field.To this end,the proposed method decomposes the spatiotemporal field using principal component analysis(PC A)and finds the top r essential entities of the principal basis.The corresponding sampling locations of the selected entities are regarded as the sensor deployment locations.The observations collected at the selected sensor deployment locations can then be used to reconstruct the spatiotemporal field,accurately.Results are demonstrated using a National Oceanic and Atmospheric Administration sea surface temperature dataset.In the present study,the proposed method achieved the lowest reconstruction error among all methods.展开更多
In this paper,three distributed and scalable nonuniform deployment algorithms in order to enhance the quality of monitoring(QoM).Mobile sensors are to be deployed around a target of interest which can be stationary or...In this paper,three distributed and scalable nonuniform deployment algorithms in order to enhance the quality of monitoring(QoM).Mobile sensors are to be deployed around a target of interest which can be stationary or moving,and to approximate a given weight function which is a measure of information or event density.The first two algorithms generate nonuniform deployments by inverse-transformations from a uniform deployment.They handle the situations of global coordinate system which is available and not with appropriate assumptions,respectively.The third algorithm,which relocates sensors to adjust inter-node distances based on the local measurements only,is suitable for general cases.The simulation results demonstrate the proposed algorithms can achieve reliable and satisfactory deployments.展开更多
Target tracking using wireless sensor networks offers multiple challenges because it usually involves intensive computation and requires accurate methods for tracking and energy consumption. Above all, scalability, en...Target tracking using wireless sensor networks offers multiple challenges because it usually involves intensive computation and requires accurate methods for tracking and energy consumption. Above all, scalability, energy optimization, efficiency, and overhead reduction are some among the key tasks for any protocol designed to perform target tracking using large scale sensor networks. Border surveillance systems, on the other side, need to report border crossings in a real time manner. They should provide large coverage, lower energy consumption, real time crossing detection, and use efficient tools to report crossing information. In this paper, we present a scheme, called Border Cooperative and Predictive Tracking protocol (BCTP), capable of energyaware surveillance and continuous tracking of objects and individuals’ crossing a country border and anticipating target motion within a thick strip along the border and estimating the target exit zone and time.展开更多
文摘This paper concerns the data-driven sensor deployment problem in large spatiotemporal fields.Traditionally,sensor deployment strategies have been heavily dependent on model-based planning approaches.However,model-based approaches do not typically maximize the information gain in the field,which tend to generate less effective sampling locations and lead to high reconstruction error.In the present paper,a data-driven approach is developed to overcome the drawbacks of the model-based approach and improve the spatiotemporal field reconstruction accuracy.The proposed method can select the most informative sampling locations to represent the entire spatiotemporal field.To this end,the proposed method decomposes the spatiotemporal field using principal component analysis(PC A)and finds the top r essential entities of the principal basis.The corresponding sampling locations of the selected entities are regarded as the sensor deployment locations.The observations collected at the selected sensor deployment locations can then be used to reconstruct the spatiotemporal field,accurately.Results are demonstrated using a National Oceanic and Atmospheric Administration sea surface temperature dataset.In the present study,the proposed method achieved the lowest reconstruction error among all methods.
基金Sponsored by the National Natural Science Foundation of China (Grant No. 61174016,61171197)
文摘In this paper,three distributed and scalable nonuniform deployment algorithms in order to enhance the quality of monitoring(QoM).Mobile sensors are to be deployed around a target of interest which can be stationary or moving,and to approximate a given weight function which is a measure of information or event density.The first two algorithms generate nonuniform deployments by inverse-transformations from a uniform deployment.They handle the situations of global coordinate system which is available and not with appropriate assumptions,respectively.The third algorithm,which relocates sensors to adjust inter-node distances based on the local measurements only,is suitable for general cases.The simulation results demonstrate the proposed algorithms can achieve reliable and satisfactory deployments.
文摘Target tracking using wireless sensor networks offers multiple challenges because it usually involves intensive computation and requires accurate methods for tracking and energy consumption. Above all, scalability, energy optimization, efficiency, and overhead reduction are some among the key tasks for any protocol designed to perform target tracking using large scale sensor networks. Border surveillance systems, on the other side, need to report border crossings in a real time manner. They should provide large coverage, lower energy consumption, real time crossing detection, and use efficient tools to report crossing information. In this paper, we present a scheme, called Border Cooperative and Predictive Tracking protocol (BCTP), capable of energyaware surveillance and continuous tracking of objects and individuals’ crossing a country border and anticipating target motion within a thick strip along the border and estimating the target exit zone and time.
