Viewshed analysis is of great interest to location optimization, environmental planning, ecology and tourism. There have been plenty of viewshed analysis methods which are generally time-consuming and among these methods, the XDraw algorithm is one of the fastest algorithms and has been widely adopted in various applications. Unfortunately, XDraw suffers from chunk distortion which greatly lowers the accuracy, which limits the application of XDraw to a certain extent. Previous works failed to remove chunk distortion because they are unaware of the underlying contribution relationship. In this paper, we propose HiXDraw—an improved XDraw algorithm free of chunk distortion. We first uncover the causation of chunk distortion from an innovative contributing perspective. Instead of recording LOS (line-of-sight) height, we use a new auxiliary grid to preserve contributing points. By preventing improper terrain data from contributing to determining the visibility, we significantly improve the accuracy of the outcome viewshed. The experimental results reveal that the error rate largely decreases by 65%. Given the same computing time, HiXDraw is more accurate than previous improvements in XDraw. To validate the removal of chunk distortion, we also present a pillar experiment.

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We present a distributed spatial index called CANQTree based on a Content-Addressable Network (CAN) andQuadTree-alike structures. In our system, both spatial objects and their corresponding nodes in a QuadTree are identified by some points and mapped into CAN zones of peers. For a given spatial range query, CAN-QTree can provide O(n 1/2 ) search performance. With a uniform distribution of spatial objects, given ε > 0, CAN-QTree can provably maintain a load imbalance of at most 2 + ε between a highest loaded peer and a lightest loaded peer. Simulations show that our CAN-QTree is an effective spatial index with a guarantee of good load-balancing.

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Ant colony algorithm (ACA) is employed and improved in routing protocol of information acquisition of manufacturing process based on wireless networks. Weighting factor for paths selection is defined as an exponential variable in adaptive ACA. In this way, it prevents the weighting factor from excess increasing or rapid reducing to 0 which results in local optimum. This approach can dynamically adjust paths selection and improve global search ability by optimizing global policy. Adaptive ACA consumes the least time in the process of searching the most optimized paths and searches the shortest paths under the same of iterative loops. Under the same condition of the information heuristic factor and the expected heuristic factor, the algorithm shows good adaptation, realizes the load balancing between paths and resolves the dynamic adjustment problem.