With the problem of robot motion control in dynamic environment represented by mobile obstacles,working pieces and external mechanisms considered, a relevant control actions design procedure has been pro-posed to prov...With the problem of robot motion control in dynamic environment represented by mobile obstacles,working pieces and external mechanisms considered, a relevant control actions design procedure has been pro-posed to provide coordination of robot motions with respect to the moving external objects so that an extension ofrobot spatial motion techniques and active robotic strategies based on approaches of nonlinear control theory canbe achieved.展开更多
Performing mobile k nearest neighbor （MkNN） queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried are mobile. The performance of this kind of query re...Performing mobile k nearest neighbor （MkNN） queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried are mobile. The performance of this kind of query relies heav- ily on the maintenance of the current locations of the objects. The index used for mobile objects must support efficient up- date operations and efficient query handling. This study aims to improve the performance of the MkNN queries while re- ducing update costs. Our approach is based on an observa- tion that the frequency of one region changing between being occupied or not by mobile objects is much lower than the frequency of the position changes reported by the mobile ob- jects. We first propose an virtual grid quadtree with Voronoi diagram （VGQ-Vor）, which is a two-layer index structure that indexes regions occupied by mobile objects in a quadtree and builds a Voronoi diagram of the regions. Then we propose a moving k nearest neighbor （kNN） query algorithm on the VGQ-Vor and prove the correctness of the algorithm. The ex- perimental results show that the VGQ-Vor outperforms the existing techniques （Bx-tree, Bdual-tree） by one to three or- ders of magnitude in most cases.展开更多
Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are ene...Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are energy con-strained and not feasible for deploying advanced tracking techniques due to significant computing requirements.To address these issues,in this paper,we develop an edge computing-based multivariate time series(EC-MTS)framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks.Specifically,EC-MTS leverages statistical technique(i.e.,vector auto regression(VAR))to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction.Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure.We have validated the efficacy of EC-MTS and our experimental results demon-strate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects.In addition,we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.展开更多
基金Sponsored by Russian Foundation of Basic Research (Grant No. 97-01-00432)
文摘With the problem of robot motion control in dynamic environment represented by mobile obstacles,working pieces and external mechanisms considered, a relevant control actions design procedure has been pro-posed to provide coordination of robot motions with respect to the moving external objects so that an extension ofrobot spatial motion techniques and active robotic strategies based on approaches of nonlinear control theory canbe achieved.
文摘Performing mobile k nearest neighbor （MkNN） queries whilst also being mobile is a challenging problem. All the mobile objects issuing queries and/or being queried are mobile. The performance of this kind of query relies heav- ily on the maintenance of the current locations of the objects. The index used for mobile objects must support efficient up- date operations and efficient query handling. This study aims to improve the performance of the MkNN queries while re- ducing update costs. Our approach is based on an observa- tion that the frequency of one region changing between being occupied or not by mobile objects is much lower than the frequency of the position changes reported by the mobile ob- jects. We first propose an virtual grid quadtree with Voronoi diagram （VGQ-Vor）, which is a two-layer index structure that indexes regions occupied by mobile objects in a quadtree and builds a Voronoi diagram of the regions. Then we propose a moving k nearest neighbor （kNN） query algorithm on the VGQ-Vor and prove the correctness of the algorithm. The ex- perimental results show that the VGQ-Vor outperforms the existing techniques （Bx-tree, Bdual-tree） by one to three or- ders of magnitude in most cases.
文摘Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are energy con-strained and not feasible for deploying advanced tracking techniques due to significant computing requirements.To address these issues,in this paper,we develop an edge computing-based multivariate time series(EC-MTS)framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks.Specifically,EC-MTS leverages statistical technique(i.e.,vector auto regression(VAR))to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction.Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure.We have validated the efficacy of EC-MTS and our experimental results demon-strate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects.In addition,we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.
