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Energy efficient and trustable routing protocol for Wireless Sensor Networks based on Genetic Algorithm (E2TRP)

机译:基于遗传算法的无线传感器网络(E2TRP)节能可信赖的路由协议

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In today's world Wireless Sensor Networks (WSNs) has gained a lot of recognition because of its wide-ranging areas of applications. Sensor nodes in WSNare connected to each other by networks, mainly powered by a battery source. These sensor nodes have lesser amount of power and computational capabilities. Typically, sensor nodes are deployed in remote areas where replacement of their batteries once exhausted becomes extremely difficult and cumbersome. Battery power is crucial aspect of the network (while designing a protocol). To maximize the lifespan of sensor nodes we recommend a genetic algorithm-based model. In this model, energy is distributed among all the sensor nodes and network performance is enriched by choosing the cluster head and clustering on the basis of 3 important factors: a. residual energy, b. distance from the sink and c. trust of the node. The trust of the node helps to detect malicious (abnormal) nodes in the neighborhood. To further maximize the network lifetime, the proposed model implements a multihop routing mechanism for data dissemination from source to the sink. To prove the real-time effectiveness of the proposed model, we simulated it using Matlab and compared with “Design and Implementation of a New Energy Efficient Clustering Algorithm using Genetic Algorithm for Wireless Sensor Networks (DINEECAGA)”[11]. The algorithm was evaluated in the range of 20 to 60 sensor nodes. Our results prove that the proposed model is far better in terms of maximizing the network lifetime than the DINEECAGA.
机译:在当今世界无线传感器网络(WSNS)中由于其广泛的应用领域而获得了很多识别。通过网络互相连接的WSNARE中的​​传感器节点,主要由电池源供电。这些传感器节点具有较小的功率和计算能力。通常,传感器节点部署在偏远地区,其中一旦耗尽的电池替换变得极其困难和繁琐。电池电量是网络的关键方面(在设计协议时)。为了最大化传感器节点的寿命,我们推荐基于遗传算法的模型。在该模型中,能量分布在所有传感器节点中,通过在3个重要因素的基础上选择群集头和聚类来丰富网络性能:a。剩余能量,b。距离水槽和c的距离。信任节点。节点的信任有助于检测邻域中的恶意(异常)节点。为了进一步最大化网络生命周期,所提出的模型实现了一种多频道路由机制,用于从源到沉没的数据传播。为了证明所提出的模型的实时有效性,我们使用MATLAB模拟它,并与“使用无线传感器网络(Dineecaga)”的遗传算法(Dineecaga)的遗传算法进行比较“设计与实现”的设计与实现。[11]。该算法在20到60个传感器节点的范围内进行评估。我们的结果证明,拟议的模型在最大化网络寿命比Dineecaga最大限度地更好。

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