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Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization

机译:使用游戏理论和增强的蚁群优化,通过WSN的移动水槽收集数据

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Optimal performance and improved lifetime are the most desirable design benchmarks for WSNs and the mechanism for data gathering is a major constituent influencing these standards. Several researchers have provided significant evidence on the advantage of mobile sink (MS) in performing effective gathering of relevant data. However, determining the trajectory for MS is an NP-hard-problem. Especially in delay-inevitable applications, it is challenging to select the best-stops or rendezvous points (RPs) for MS and also to design an efficient route for MS to gather data. To provide a suitable solution to these challenges, we propose in this paper, a game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique. This is a distributed method of data gathering using MS, combining the optimal decision making skill of game theory in selecting the best RPs and computational swarm intelligence of enhanced ant colony optimization in choosing the best path for MS. GTAC-DG helps to reduce data transfer and management, energy consumption and delay in data delivery. The MS moves in a reliable and intelligent trajectory, extending the lifetime and conserving the energy of WSN. The simulation results prove the effectiveness of GTAC-DG in terms of metrics such as energy and network lifetime.
机译:最佳性能和改进的寿命是WSN的最理想的设计基准,数据收集机制是影响这些标准的主要组成部分。一些研究人员提供了关于移动水槽(MS)的优势在执行相关数据的有效收集方面的重要证据。但是,确定MS的轨迹是NP-HARD问题。特别是在延迟不可避免的应用程序中,选择用于MS的最佳停止或结论点(RPS)并设计有效的MS用于收集数据的有效路由有挑战性。为了为这些挑战提供合适的解决方案,我们提出了本文,是一种博弈论和基于增强的基于蚁群的MS路由选择和数据收集(GTAC-DG)技术。这是使用MS的数据收集的分布式方法,结合了游戏理论的最佳决策技巧在选择最佳RPS和计算蚁群优化中的最佳RPS和计算群智能时选择MS的最佳路径。 GTAC-DG有助于降低数据传输的数据传输和管理,能耗和延迟。 MS在可靠而智能的轨迹中移动,延伸寿命并节省WSN的能量。模拟结果证明了GTAC-DG在能量和网络寿命等度量方面的有效性。

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