Dynamic rectangle

Abstract: Packing orthogonal unequal rectangles in a circle with a mass balance (BCOURP) is a typical combinational optimization problem with the NP-hard nature. This paper proposes an effective quasiphysical and dynamic adjustment approach (QPDAA). Two embedded degree functions between two orthogonal rectangles and between an orthogonal rectangle and the container are defined, respectively, and the extruded potential energy function and extruded resultant force formula are constructed based on them. By an elimination of the extruded resultant force, the dynamic rectangle adjustment, and an iteration of the translation, the potential energy and static imbalance of the system can be quickly decreased to minima. The continuity and monotony of two embedded degree functions are proved to ensure the compactness of the optimal solution. Numerical experiments show that the proposed QPDAA is superior to existing approaches in performance.

Abstract: Most existing routing protocols on the object detection and tracking in sensor networks concentrates on finding ways to detect and track one or more individual objects, such as people, animals, and vehicles, and not many protocols have been done on detecting and tracking continuous objects, such as poison gas, biochemical, and chemical liquid. These continuous objects are quite different from the individual objects in that they continuously distributed across a region and usually occupy a large area. Accordingly, they are detected and sensed by many sensor nodes, and their sensing data are redundant and highly correlated. Hence, there need any efficient scheme on collecting and aggregating locally their sensing data and generating the data report. The continuous objects also tend to diffuse, changes in shape, increases the size, even splits into multiple smaller continuous objects, or join together one continuous object. Accordingly, there also need any efficient scheme to manage efficiently the dynamic change of shape of the continuous objects. Therefore, we introduce dynamic rectangle zone-based collaboration mechanism for detecting, tracking, and monitoring the continuous objects taking into account their properties. One center node in the zone collects and aggregates the sensing data from sensor nodes which detect the continuous object. The dynamic rectangle zone change newly according to dynamic change of the continuous object and the center node is also altered by another node to minimize the energy consumption for collecting the sensing data.

Abstract: This paper addresses issues for tracking and monitoring continuous objects, such as poison gas, biochemical, and chemical liquid in wireless sensor networks. These continuous objects are quite different from the individual objects, such as people, animals, and vehicles in that they are continuously distributed across a region and usually occupy a large area. Accordingly, they are detected and sensed by many sensor nodes, and their sensing data are redundant and highly correlated. Hence, there needs any efficient scheme on collecting and aggregating locally their sensing data and generating the data report. The continuous objects also tend to diffuse, changes in shape, increases the size, even splits into multiple smaller continuous objects, or join together one continuous object. Accordingly, there also need any efficient scheme to manage efficiently the dynamic change of shape of the continuous objects. Therefore, we introduce Dynamic Rectangle Zone-based Collaboration Mechanism for detecting, tracking, and monitoring the continuous objects taking into account the dynamic change of their shape. The proposed mechanism constructs a dynamic rectangle zone included the area occupied by one continuous object. One center node in the zone collects and aggregates the sensing data from sensor nodes which detect the continuous object. The dynamic rectangle zone change newly according to dynamic change of the continuous object and the center node is also altered by another node to minimize the energy consumption for collecting the sensing data. Through simulation results, we also evaluate how environmental factors and control parameters affect the performance of the proposed mechanism.

Abstract: The paths obtained by conventional A* algorithm are usually not the optimal solutions, which have many turning nodes and spend more time. To solve these problems, an improved A* algorithm is presented. First, the proposed algorithm introduced Chebyshev distance into the formula and parent heuristic function; secondly, using a method of bidirectional search paths with dynamic rectangle. The simulation results show that time is reduced by 85.9% and 58.4% comparing with traditional A* algorithm and the other improved A* algorithm. It has been demonstrated that the proposed improved A* algorithm can efficiently solve the shortest path planning problem especially on large grid maps.