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Performance analysis of DSDV and OLSR wireless sensor network routing protocols using FPGA hardware and machine learning

机译：DSDV和OLSR无线传感器网络路由协议的性能分析使用FPGA硬件和机器学习

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Wireless Sensor Network (WSN) is a self-organized network, contains sensor nodes deployed in particular regions to gather the environmental parameters and communicate the information to the base station directly through intermediate nodes. In recent times, WSN has gained attention from wireless device manufacturers, researchers, and users for remotely accessing and monitoring the information in diverse environments. The scalability and routing are the major concerns of the network. Apart from that, the performance of WSN depends on network simulation parameters such as delay, throughput, packet delivery ratio (PDR), and control overhead. The research paper focused on the DSDV and OLSR routing protocol realization on the new hardware platform. The hardware chip of these protocols is designed in Xilinx ISE 14.7 software using VHDL, targeted on Virtex-5 FPGA. The node communication is verified on Modelsim 10.0 simulation software. The FPGA hardware and timing parameters are analyzed for different node clusters (N = 10, 20 horizontal ellipsis 150) configuration. The OLSR routing protocol network performance parameters are used to build the machine learning prediction model using cluster tree regression, random forest regression, multiple regression, and K-means clustering. The K-means clustering predicted 99.12% and 98.50% accuracy in terms of the packet delivery ratio and throughput respectively.
机译：无线传感器网络（WSN）是一个自组织网络，包含部署在特定区域的传感器节点，以收集环境参数，并通过中间节点直接将信息传送到基站。最近，WSN从无线设备制造商，研究人员和用户掌握了远程访问和监控各种环境中信息的关注。可扩展性和路由是网络的主要问题。除此之外，WSN的性能取决于网络仿真参数，如延迟，吞吐量，数据包传递比（PDR）和控制开销。研究文件集中在新硬件平台上的DSDV和OLSR路由协议实现。这些协议的硬件芯片在Xilinx ISE 14.7软件中设计了VHDL，针对Virtex-5 FPGA。节点通信在ModelSIM 10.0仿真软件上验证。分析FPGA硬件和定时参数，用于不同的节点集群（n = 10,20级水平省略的150）配置。 OLSR路由协议网络性能参数用于使用群集树回归，随机林回归，多元回归和K-means群集构建机器学习预测模型。 K-Means聚类分别在分组传递比率和吞吐量方面预测了99.12％和98.50％的精度。

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