Pollination Inspired Clustering Model for Wireless Sensor Network Optimization

S. Shakya
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Abstract

Remote and dangerous fields that are expensive, complex, and unreachable to reach human insights are examined with ease using the Wireless Sensor Network (WSN) applications. Due to the use of non-renewable sources of energy, challenges with respect to the network lifetime, fault tolerance and energy consumption are faced by the self-managed networks. An efficient fault tolerance technique has been provided in this paper as an effective management strategy. Using the network and communication nodes, revitalization and fault recognition techniques are used for handling diverse levels of faults in this framework. At the network nodes, the fault tolerance capability is increased by the proposed protocol model and management strategy. This enhances the corresponding data transmission in the network. When compared to the conventional techniques, the proposed model increases the network lifetime by five times. It is observed from the validation results that, with a 10% increase in the network lifetime, there is a 2% decrease in the fault tolerance proficiency of the network. The network lifetime and data transmission rate are improved while the network energy consumption is reduced significantly. The MATLAB environment is used for simulation purpose. In terms of energy consumption, network lifetime and fault tolerance, the proposed model offers optimal results.
基于授粉启发的无线传感器网络聚类优化模型
使用无线传感器网络(WSN)应用程序,可以轻松检查昂贵、复杂且无法到达的偏远和危险领域。由于不可再生能源的使用,自管理网络在网络寿命、容错能力和能耗方面面临挑战。本文提出了一种高效的容错技术作为一种有效的管理策略。该框架利用网络和通信节点,采用恢复和故障识别技术来处理不同级别的故障。提出的协议模型和管理策略提高了网络节点的容错能力。这增强了网络中相应的数据传输。与传统技术相比,该模型的网络寿命提高了5倍。从验证结果中可以观察到,网络寿命每增加10%,网络的容错能力就会降低2%。提高了网络寿命和数据传输速率,同时显著降低了网络能耗。采用MATLAB环境进行仿真。在能量消耗、网络寿命和容错性方面,该模型具有最优的结果。
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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