在密集的物联网环境中通过应用神经网络配置来优化传感器拓.
George Papastergiou1, Apostolos Xenakis1, Costas Chaikalis1
1Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究使用神经网络在无线传感器网络 (WSN) 中进行最佳传感器放置,显著延长网络寿命,同时平衡可扩展部署的覆盖范围和能源限制.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 无线传感器网络 (WSN) 面临着密集部署的挑战,原因是传感器放置,覆盖范围,连接和能源等相互矛盾的限制.
- 扩展大型WSN是困难的,因为维持这些约束之间的权衡是复杂的.
- 现有的解决方案通常依赖于启发式,以在多项式时间内实现接近最佳的网络行为.
研究的目的:
- 为WSNs制定拓控制和寿命延长问题,考虑传感器放置,覆盖范围和能量限制.
- 为了研究神经网络配置的应用来解决这个问题.
- 动态提出和管理传感器放置坐标,以最大限度地提高网络寿命.
主要方法:
- 为WSNs制定拓控制和寿命延长问题.
- 应用和测试各种神经网络配置.
- 神经网络通过动态提议和处理2D平面中的传感器位置坐标.
主要成果:
- 拟议的神经网络算法在模拟中成功提高了网络寿命.
- 算法保持了基本的通信和能源限制.
- 对于中大型和大规模的WSN部署,已经证明了性能改进.
结论:
- 神经网络提供了一种可行的方法来优化WSN中的传感器放置.
- 拟议的方法有效地延长了网络寿命,同时遵守了关键的操作约束.
- 这种方法解决了密集的WSN部署中的可扩展性挑战.
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