Localization is one of the fundamental problems in wireless sensor networks (WSNs), since locations of the sensor nodes are critical to both network operations and most application level tasks. Although the GPS base...Localization is one of the fundamental problems in wireless sensor networks (WSNs), since locations of the sensor nodes are critical to both network operations and most application level tasks. Although the GPS based localization schemes can be used to determine node locations within a few meters, the cost of GPS devices and non-availability of GPS signals in confined environments prevent their use in large scale sensor networks. There exists an extensive body of research that aims at obtaining locations as well as spatial relations of nodes in WSNs without requiring specialized hardware and/or employing only a limited number of anchors that are aware of their own locations. In this paper, we present a comprehensive survey on sensor localization in WSNs covering motivations, problem formulations, solution approaches and performance summary. Future research issues will also be discussed.展开更多
This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle (UCAV) performing an air-to-surface multi-target attack (A/SMTA) mission using satellite-gu...This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle (UCAV) performing an air-to-surface multi-target attack (A/SMTA) mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem (TSP), called the dynamic-constrained TSP with neighborhoods (DCT- SPN). Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap (PRM) is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP (ATSP). Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.展开更多
基金supported by the National Science Foundation (No.CNS-0721951,IIS-0326505)the Air Force Office of Scientific Research(AFOSR) (No.FA9550-08-1-0260)+1 种基金the Texas Advanced Research Program (ARP) (No.14-748779)the Research I Foundation grant of IIT-Kanpur,and Department of Science and Technology,Government of India under Indo-Trento Program for Advanced Research
文摘Localization is one of the fundamental problems in wireless sensor networks (WSNs), since locations of the sensor nodes are critical to both network operations and most application level tasks. Although the GPS based localization schemes can be used to determine node locations within a few meters, the cost of GPS devices and non-availability of GPS signals in confined environments prevent their use in large scale sensor networks. There exists an extensive body of research that aims at obtaining locations as well as spatial relations of nodes in WSNs without requiring specialized hardware and/or employing only a limited number of anchors that are aware of their own locations. In this paper, we present a comprehensive survey on sensor localization in WSNs covering motivations, problem formulations, solution approaches and performance summary. Future research issues will also be discussed.
文摘This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle (UCAV) performing an air-to-surface multi-target attack (A/SMTA) mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem (TSP), called the dynamic-constrained TSP with neighborhoods (DCT- SPN). Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap (PRM) is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP (ATSP). Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.