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Algorithms for energy efficient mobile object tracking in wireless sensor networks

机译:无线传感器网络中高效节能的移动物体跟踪算法

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Wireless sensor networks have found more and more applications in a variety of pervasive computing environments, in their functions as data acquisition in pervasive applications. However, how to get better performance to support data acquisition of pervasive applications over WSNs remains to be a nontrivial and challenging task. The network lifetime and application requirement are two fundamental, yet conflicting, design objectives in wireless sensor networks for tracking mobile objects. The application requirement is often correlated to the delay time within which the application can send its sensing data back to the users in tracking networks. In this paper we study the network lifetime maximization problem and the delay time minimization problem together. To make both problems tractable, we have the assumption that each sensor node keeps working since it turns on. And we formulate the network lifetime maximization problem as maximizing the number of sensor nodes who don't turn on, and the delay time minimization problem as minimizing the routing path length, after achieving the required tracking tasks. Since we prove the problems are NP-complete and APX-complete, we propose three heuristic algorithms to solve them. And we present several experiments to show the advantages and disadvantages referring to the network lifetime and the delay time among these three algorithms on three models, random graphs, grids and hypercubes. Furthermore, we implement the distributed version of these algorithms.
机译:无线传感器网络已经在各种普适计算环境中找到了越来越多的应用,它们的功能是在普适应用中获取数据。但是,如何获得更好的性能来支持WSN上普及应用程序的数据采集仍然是一项艰巨而艰巨的任务。网络寿命和应用要求是用于跟踪移动对象的无线传感器网络中的两个基本但有冲突的设计目标。应用程序需求通常与延迟时间相关,在该延迟时间内应用程序可以将其感测数据发送回跟踪网络中的用户。在本文中,我们一起研究了网络寿命最大化问题和延迟时间最小化问题。为了使这两个问题都易于解决,我们假设每个传感器节点自打开以来一直保持工作状态。在完成所需的跟踪任务之后,我们将网络寿命最大化问题表示为最大化未打开的传感器节点数量,将延迟时间最小化问题表示为最小化路由路径长度。由于我们证明问题是NP完全问题和APX完全问题,因此我们提出了三种启发式算法来解决它们。并且,我们提出了一些实验,以说明在三种模型(随机图,网格和超立方体)上这三种算法在网络寿命和延迟时间方面的优缺点。此外,我们实现了这些算法的分布式版本。

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