Star sensors are an important means of autonomous navigation and access to space information for satellites.They have been widely deployed in the aerospace field.To satisfy the requirements for high resolution,timelin...Star sensors are an important means of autonomous navigation and access to space information for satellites.They have been widely deployed in the aerospace field.To satisfy the requirements for high resolution,timeliness,and confidentiality of star images,we propose an edge computing algorithm based on the star sensor cloud.Multiple sensors cooperate with each other to forma sensor cloud,which in turn extends the performance of a single sensor.The research on the data obtained by the star sensor has very important research and application values.First,a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition,which can reduce the amount of data computation.Then,a mappingmodel between content and space is constructed to achieve low-rank image representation and efficient computation.Finally,the data collected by the wireless sensor is delivered to the edge server,and a differentmethod is used to achieve privacy protection.Only a small amount of core data is stored in edge servers and local servers,and other data is transmitted to the cloud.Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage,and has strong privacy.展开更多
The current IT cloud computing is playing a vital role in most of the areas such as Education, Research, Health care, etc. The cloud computing technology involving in sensor networks embedded system and IOT (Inte...The current IT cloud computing is playing a vital role in most of the areas such as Education, Research, Health care, etc. The cloud computing technology involving in sensor networks embedded system and IOT (Internet of Things). At present scenario, the sensors collected the information from the particular environment, where the sensors are fixed and transfer the collected information to cloud storage, here the challenge is the data transmission i.e. data that traverse from sensor to cloud environment are the big issue and maximum number of data loss is very high especially in dynamic routing environment. If data loss is identified in any routing path then automatically the information will transfer to alternate routing path. In this paper, we introduce a new algorithm for automatic routing path selection that can be integrated with cloud technology. This algorithm supports when data loss is found in the particular path of a network, then it selects an alternate route to transfer the data. The proposed model is comparatively more efficient than the prior methodologies. The implementation of the proposed work is done on NS3 simulator, and the performance metric is analyzed.展开更多
Mobile edge users(MEUs)collect data from sensor devices and report to cloud systems,which can facilitate numerous applications in sensor‑cloud systems(SCS).However,because there is no effective way to access the groun...Mobile edge users(MEUs)collect data from sensor devices and report to cloud systems,which can facilitate numerous applications in sensor‑cloud systems(SCS).However,because there is no effective way to access the ground truth to verify the quality of sensing devices’data or MEUs’reports,malicious sensing devices or MEUs may report false data and cause damage to the platform.It is critical for selecting sensing devices and MEUs to report truthful data.To tackle this challenge,a novel scheme that uses unmanned aerial vehicles(UAV)to detect the truth of sensing devices and MEUs(UAV‑DT)is proposed to construct a clean data collection platform for SCS.In the UAV‑DT scheme,the UAV delivers check codes to sensor devices and requires them to provide routes to the specified destination node.Then,the UAV flies along the path that enables maximal truth detection and collects the information of the sensing devices forwarding data packets to the cloud during this period.The information collected by the UAV will be checked in two aspects to verify the credibility of the sensor devices.The first is to check whether there is an abnormality in the received and sent data packets of the sensing devices and an evaluation of the degree of trust is given;the second is to compare the data packets submitted by the sensing devices to MEUs with the data packets submitted by the MEUs to the platform to verify the credibility of MEUs.Then,based on the verified trust value,an incentive mechanism is proposed to select credible MEUs for data collection,so as to create a clean data collection sensor‑cloud network.The simulation results show that the proposed UAV‑DT scheme can identify the trust of sensing devices and MEUs well.As a result,the proportion of clean data collected is greatly improved.展开更多
This paper presents a prototype of an Integrated Cloud-Based Wireless Sensor Network (WSN) developed to monitor pH, conductivity and dissolved oxygen parameters from wastewater discharged into water sources. To provid...This paper presents a prototype of an Integrated Cloud-Based Wireless Sensor Network (WSN) developed to monitor pH, conductivity and dissolved oxygen parameters from wastewater discharged into water sources. To provide realtime online monitoring and Internet of Things (IoT) capability, the system collects and uploads sensor data to ThingSpeak cloud via GPRS internet connectivity with the help of AT commands in combination with HTTP GET method. Moreover, the system sends message alert to the responsible organ through GSM/GPRS network and an SMS gateway service implemented by Telerivet mobile messaging platform. In this prototype, Telerivet messaging platform gives surrounding communities a means of reporting observed or identified water pollution events via SMS notifications.展开更多
面向大规模感知与智能应用场景,集中式计算在时延、带宽、能耗与隐私保护的多重约束下逐渐呈现边际效益递减,计算范式因此由单一的“万物上云”模式,逐步转向“就地计算与云边协同”的新形态。在此背景下,本文首先梳理集中化计算路径在...面向大规模感知与智能应用场景,集中式计算在时延、带宽、能耗与隐私保护的多重约束下逐渐呈现边际效益递减,计算范式因此由单一的“万物上云”模式,逐步转向“就地计算与云边协同”的新形态。在此背景下,本文首先梳理集中化计算路径在不同发展阶段所具备的优势及其适用边界,进而界定边缘计算在端-云之间所扮演的关键角色。在此基础上,进一步概述“传感云-边缘-端”协同计算框架,重点分析其中的核心机制,包括数据“必要即上行”的传输原则、面向服务级别协议(SLA)感知的任务分配与双层调度策略,以及边侧即时闭环执行与云侧全局策略治理之间的分工与协同关系。随着计算与智能能力向边缘侧持续下沉,本文进一步讨论边缘智能的发展方向,涵盖模型轻量化与本地学习机制、联邦学习与知识蒸馏的协同范式,以及面向边缘环境的智能运维(AIOps for Edge)与多级降级机制所支撑的自治能力。同时,强调构建以端到端闭环效率、系统韧性与可追责性为导向的综合评价体系的重要性。最后,结合教育等典型应用场景以及产业实践,论证就地计算与云边协同在保障确定性时延、提升系统整体韧性以及实现跨域一致性方面的现实有效性,并据此指出计算范式由边缘计算向云边智能协同演进的必然趋势与发展方向。展开更多
The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) g...The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.展开更多
For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by th...For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.展开更多
The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quant...The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quantify hemiplegic gait in consideration of the hemiplegic affected leg and unaffected leg. The recorded inertial sensor data, which is inclusive of the gyroscope signal, can be readily transmitted by wireless means to a secure Cloud. Incorporating Python to automate the post-processing of the gyroscope signal data can enable the development of a feature set suitable for a machine learning platform, such as the Waikato Environment for Knowledge Analysis (WEKA). An assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes, were evaluated in terms of classification accuracy and time to develop the machine learning model. The K-nearest neighbors achieved optimal performance based on classification accuracy achieved for differentiating between the hemiplegic affected leg and unaffected leg for gait and the time to establish the machine learning model. The achievements of this research endeavor demonstrate the utility of amalgamating the conformal wearable and wireless inertial sensor with machine learning algorithms for distinguishing the hemiplegic affected leg and unaffected leg during gait.展开更多
It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon O...It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon Observation-Cloud and Aerosol Imager （TANSO-CAI） cloud screening are necessary for the retrieval of CO2 and CH4 gas concentrations for GOSAT TANSO-Fourier Transform Spectrometer （FTS） observations. In this study, TANSO-CAI cloud flag data were compared with ground-based cloud data collected by an all-sky imager （ASI） over Beijing from June 2009 to May 2012 to examine the data quality. The results showed that the CAI has an obvious cloudy tendency bias over Beijing, especially in winter. The main reason might be that heavy aerosols in the sky are incorrectly determined as cloudy pixels by the CAI algorithm. Results also showed that the CAI algorithm sometimes neglects some high thin cirrus cloud over this area.展开更多
基金supported by Science and Technology Rising Star of Shaanxi Youth (No.2021KJXX-61)The Open Project Program of the State Key Lab of CAD&CG,Zhejiang University (No.A2206).
文摘Star sensors are an important means of autonomous navigation and access to space information for satellites.They have been widely deployed in the aerospace field.To satisfy the requirements for high resolution,timeliness,and confidentiality of star images,we propose an edge computing algorithm based on the star sensor cloud.Multiple sensors cooperate with each other to forma sensor cloud,which in turn extends the performance of a single sensor.The research on the data obtained by the star sensor has very important research and application values.First,a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition,which can reduce the amount of data computation.Then,a mappingmodel between content and space is constructed to achieve low-rank image representation and efficient computation.Finally,the data collected by the wireless sensor is delivered to the edge server,and a differentmethod is used to achieve privacy protection.Only a small amount of core data is stored in edge servers and local servers,and other data is transmitted to the cloud.Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage,and has strong privacy.
文摘The current IT cloud computing is playing a vital role in most of the areas such as Education, Research, Health care, etc. The cloud computing technology involving in sensor networks embedded system and IOT (Internet of Things). At present scenario, the sensors collected the information from the particular environment, where the sensors are fixed and transfer the collected information to cloud storage, here the challenge is the data transmission i.e. data that traverse from sensor to cloud environment are the big issue and maximum number of data loss is very high especially in dynamic routing environment. If data loss is identified in any routing path then automatically the information will transfer to alternate routing path. In this paper, we introduce a new algorithm for automatic routing path selection that can be integrated with cloud technology. This algorithm supports when data loss is found in the particular path of a network, then it selects an alternate route to transfer the data. The proposed model is comparatively more efficient than the prior methodologies. The implementation of the proposed work is done on NS3 simulator, and the performance metric is analyzed.
基金National Natural Science Foundation of China under Grant No.62032020Hunan Science and Technology Plan⁃ning Project under Grant No.2019RS3019the National Key Research and Development Program of China under Grant 2018YFB1003702.
文摘Mobile edge users(MEUs)collect data from sensor devices and report to cloud systems,which can facilitate numerous applications in sensor‑cloud systems(SCS).However,because there is no effective way to access the ground truth to verify the quality of sensing devices’data or MEUs’reports,malicious sensing devices or MEUs may report false data and cause damage to the platform.It is critical for selecting sensing devices and MEUs to report truthful data.To tackle this challenge,a novel scheme that uses unmanned aerial vehicles(UAV)to detect the truth of sensing devices and MEUs(UAV‑DT)is proposed to construct a clean data collection platform for SCS.In the UAV‑DT scheme,the UAV delivers check codes to sensor devices and requires them to provide routes to the specified destination node.Then,the UAV flies along the path that enables maximal truth detection and collects the information of the sensing devices forwarding data packets to the cloud during this period.The information collected by the UAV will be checked in two aspects to verify the credibility of the sensor devices.The first is to check whether there is an abnormality in the received and sent data packets of the sensing devices and an evaluation of the degree of trust is given;the second is to compare the data packets submitted by the sensing devices to MEUs with the data packets submitted by the MEUs to the platform to verify the credibility of MEUs.Then,based on the verified trust value,an incentive mechanism is proposed to select credible MEUs for data collection,so as to create a clean data collection sensor‑cloud network.The simulation results show that the proposed UAV‑DT scheme can identify the trust of sensing devices and MEUs well.As a result,the proportion of clean data collected is greatly improved.
文摘This paper presents a prototype of an Integrated Cloud-Based Wireless Sensor Network (WSN) developed to monitor pH, conductivity and dissolved oxygen parameters from wastewater discharged into water sources. To provide realtime online monitoring and Internet of Things (IoT) capability, the system collects and uploads sensor data to ThingSpeak cloud via GPRS internet connectivity with the help of AT commands in combination with HTTP GET method. Moreover, the system sends message alert to the responsible organ through GSM/GPRS network and an SMS gateway service implemented by Telerivet mobile messaging platform. In this prototype, Telerivet messaging platform gives surrounding communities a means of reporting observed or identified water pollution events via SMS notifications.
文摘面向大规模感知与智能应用场景,集中式计算在时延、带宽、能耗与隐私保护的多重约束下逐渐呈现边际效益递减,计算范式因此由单一的“万物上云”模式,逐步转向“就地计算与云边协同”的新形态。在此背景下,本文首先梳理集中化计算路径在不同发展阶段所具备的优势及其适用边界,进而界定边缘计算在端-云之间所扮演的关键角色。在此基础上,进一步概述“传感云-边缘-端”协同计算框架,重点分析其中的核心机制,包括数据“必要即上行”的传输原则、面向服务级别协议(SLA)感知的任务分配与双层调度策略,以及边侧即时闭环执行与云侧全局策略治理之间的分工与协同关系。随着计算与智能能力向边缘侧持续下沉,本文进一步讨论边缘智能的发展方向,涵盖模型轻量化与本地学习机制、联邦学习与知识蒸馏的协同范式,以及面向边缘环境的智能运维(AIOps for Edge)与多级降级机制所支撑的自治能力。同时,强调构建以端到端闭环效率、系统韧性与可追责性为导向的综合评价体系的重要性。最后,结合教育等典型应用场景以及产业实践,论证就地计算与云边协同在保障确定性时延、提升系统整体韧性以及实现跨域一致性方面的现实有效性,并据此指出计算范式由边缘计算向云边智能协同演进的必然趋势与发展方向。
基金the National Natural Science Foundation of China (61720106012 and 61403215)the Foundation of State Key Laboratory of Robotics (2006-003)the Fundamental Research Funds for the Central Universities for the financial support of this work.
文摘The autonomous exploration and mapping of an unknown environment is useful in a wide range of applications and thus holds great significance. Existing methods mostly use range sensors to generate twodimensional (2D) grid maps. Red/green/blue-depth (RGB-D) sensors provide both color and depth information on the environment, thereby enabling the generation of a three-dimensional (3D) point cloud map that is intuitive for human perception. In this paper, we present a systematic approach with dual RGB-D sensors to achieve the autonomous exploration and mapping of an unknown indoor environment. With the synchronized and processed RGB-D data, location points were generated and a 3D point cloud map and 2D grid map were incrementally built. Next, the exploration was modeled as a partially observable Markov decision process. Partial map simulation and global frontier search methods were combined for autonomous exploration, and dynamic action constraints were utilized in motion control. In this way, the local optimum can be avoided and the exploration efficacy can be ensured. Experiments with single connected and multi-branched regions demonstrated the high robustness, efficiency, and superiority of the developed system and methods.
文摘For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.
文摘The quantification of gait is uniquely facilitated through the conformal wearable and wireless inertial sensor system, which consists of a profile comparable to a bandage. These attributes advance the ability to quantify hemiplegic gait in consideration of the hemiplegic affected leg and unaffected leg. The recorded inertial sensor data, which is inclusive of the gyroscope signal, can be readily transmitted by wireless means to a secure Cloud. Incorporating Python to automate the post-processing of the gyroscope signal data can enable the development of a feature set suitable for a machine learning platform, such as the Waikato Environment for Knowledge Analysis (WEKA). An assortment of machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes, were evaluated in terms of classification accuracy and time to develop the machine learning model. The K-nearest neighbors achieved optimal performance based on classification accuracy achieved for differentiating between the hemiplegic affected leg and unaffected leg for gait and the time to establish the machine learning model. The achievements of this research endeavor demonstrate the utility of amalgamating the conformal wearable and wireless inertial sensor with machine learning algorithms for distinguishing the hemiplegic affected leg and unaffected leg during gait.
基金support from the Strategic Pilot Science and Technology project of the Chinese Academy of Sciences(Grant No.XDA05040200)the National Natural Science Foundation of China(Grant No.41275040)
文摘It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon Observation-Cloud and Aerosol Imager （TANSO-CAI） cloud screening are necessary for the retrieval of CO2 and CH4 gas concentrations for GOSAT TANSO-Fourier Transform Spectrometer （FTS） observations. In this study, TANSO-CAI cloud flag data were compared with ground-based cloud data collected by an all-sky imager （ASI） over Beijing from June 2009 to May 2012 to examine the data quality. The results showed that the CAI has an obvious cloudy tendency bias over Beijing, especially in winter. The main reason might be that heavy aerosols in the sky are incorrectly determined as cloudy pixels by the CAI algorithm. Results also showed that the CAI algorithm sometimes neglects some high thin cirrus cloud over this area.
