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摘要: 结合危险品运输监测应用, 搭建了基于无线传感器网络的实时监测系统, 并对其MAC层协议和物理层无线数据发送时序进行了改进和优化。改进了MAC层中的原始BEB算法, 引入了支持优先级的GDCF算法。对MAC层中的RTS/CTS方式进行了有效性分析, 并出于节能考虑, 引入了睡眠技术。在物理层数据无线发送过程中, 为缩短发送时间, 减少碰撞可能性, 对其时序进行了优化。在工程车辆上安装基于IRIS无线传感器的节点平台进行实际测试。测试结果表明: 改进后的退避算法节点丢包率随网络节点数目增加变化不明显; 去除RTS/CTS机制后, 在采样间隔时间为50 ms时, 网络丢包率由20%左右下降到了6%以内; 一个工作周期内节省能量达到95%;无线数据发送时序优化达到了设计要求, 满足了实际应用中对实时监测无线传感网络的性能要求。Abstract: A WSNs-based real-time monitoring system was built in view of the application in hazardous materials transportation monitoring.MAC layer protocol and physical layer transmission time sequence of the monitoring system were improved and optimized.Priority supported GDCF algorithm was introduced after improving the original BEB algorithm of MAC layer.Effectiveness analysis was made for the RTS/CTS mechanism of MAC layer.Sleep mode was introduced for energy saving.Time sequence was optimized to reduce data transmission time and collision probability in physical layer data transmission.Practical test was made by IRIS node platform installing on an engineering automobile.Test result shows that the drop rate using improved backoff algorithm has no significant change with the increase of network node number.The drop rate without RTS/CTS mechanism reduces from the original about 20% to less than 6% when the giving sampling interval time is 50 ms, and 95% energy is saved in every working period.Design goal is achieved in improved time sequence of wireless data transmission, and the performance requirements in real-time monitoring system of WSNs are satisfied in practical application.
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表 1 采样时间间隔为50 ms时的丢包率
Table 1. Packet loss rates at giving sampling interval time of 50 ms %
分节点 优化前丢包率(有RTS/CTS) 优化后丢包率(无RTS/CTS) 1 23.0 1.2 2 27.0 0.8 3 19.0 6.2 4 26.0 5.1 5 13.0 3.9 6 9.0 1.2 表 2 各阶段运行时间
Table 2. Running time of each stage
采样发送时间周期/ms 500 最大时钟漂移Ta/ms 0.63 节点采样发送的实际工作时间Tb/ms 20.98 节点休眠唤醒时间Tc/ms 1.10 节点整体工作时间Ta+Tb+Tc/ms 22.71 节点休眠时间/ms 477.295 节点工作时间占空比/% 4.54 表 3 优化后的时间测试结果
Table 3. Time test result after optimization
数据有效载荷长度/Byte 4 28 T2-T1/μs 11.9 11.9 T3-T2/μs 27.1 27.1 T4-T3/μs 33.6 105.3 -
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