Energy efficient and event driven mobility model in mobile WSN

Tathagata Das, Sarbani Roy
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引用次数: 5

Abstract

Nowadays wireless sensor network (WSN) of mobile nodes is used in most of the sensor applications especially in event-driven applications. The choice of mobile nodes are obvious as they are much more versatile than static sensors. Mobile nodes can be deployed in any scenario and cope with rapid changes in the environment. One of the fundamental problem of mobile WSN is energy consumption and this is a more serious issue than in static WSN because all sensors are moving continuously and as a result they are prone to consume more energy. Network lifetime is inversely dependent on the overall energy consumption of nodes in the network. In this paper, we present an energy efficient and event driven mobility model (E3DM) where mobile sensors inform each other about a static event and adjust their route to reach to the nearest point of that event. Sensors exchange some useful information between themselves by packet transmissions. Through this information exchange, movement of one sensor influences the movements of its neighbors. The objective is to minimize the energy consumption and thus increase the network lifetime. Although some overhead involved due to packet exchange between nodes, an enhanced version of energy efficient and event driven mobility model (E4DM) is also introduced to reduce this complexity. The effectiveness of the proposed approaches are evaluated and compared with random way-point mobility model (RWP), one variant of RWP where constant velocity (RWPCV) and Gauss Markov mobility model (GMKV) using NS-3 simulation.
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移动WSN中节能和事件驱动的移动性模型
目前,移动节点的无线传感器网络(WSN)在大多数传感器应用中都得到了应用,特别是在事件驱动的应用中。移动节点的选择是显而易见的,因为它们比静态传感器更通用。移动节点可以部署在任何场景中,应对环境的快速变化。移动无线传感器网络的一个基本问题是能量消耗,这是一个比静态无线传感器网络更严重的问题,因为所有的传感器都是连续移动的,因此它们容易消耗更多的能量。网络生命周期与网络中节点的总能耗成反比。在本文中,我们提出了一种节能和事件驱动的移动模型(E3DM),其中移动传感器相互通知静态事件并调整其路线以到达该事件的最近点。传感器之间通过分组传输交换一些有用的信息。通过这种信息交换,一个传感器的运动影响它的邻居的运动。目标是最小化能源消耗,从而增加网络生命周期。尽管节点之间的数据包交换会带来一些开销,但还引入了增强版本的能效和事件驱动移动模型(E4DM)来降低这种复杂性。通过NS-3仿真,对所提方法的有效性进行了评估,并与随机路径点迁移模型(RWP)、随机路径点迁移模型的一种变体(RWPCV)和高斯马尔可夫迁移模型(GMKV)进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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