摘要
为提高无线传感器网络的能量效率,提出一种基于随机概率统计模型分析的能量优化算法。根据伽玛和修正贝塞尔函数,给出能够反映信道衰落严重程度的概率模型,采用训练序列分组进行信道估计和同步,分析2种信道数据传输机制的能量级,依据当前信道衰落和阴影的严重程度选择最佳发送能量级,以提高每比特能量的利用效率。实验结果表明,与基于节点感知机制的能量优化模型算法和基于IEEE 802.15.4移动传感器网络的能量高效算法相比,该算法的平均每比特错误率分别降低276.3%和147.6%,网络总能量效率分提高26.7%和29.2%,在减少数据传输的比特错误率、提高网络能量效率上具有更好的效果。
To improve the energy efficiency of Wireless Sensor Network（WSN）,an energy utility optimization algorithm based on random probability statistics model analysis is proposed in this paper.The algorithm based on Gamma and the modified Bessel function proposes a way to reflect the fading severity probability model.Then,it uses the training sequence packet to estimate and synchronize the channel,and analyzes the energy level of the two-channel data transmission mechanism.Finally,according to the severity of the current channel fading and shadowing,this paper selects the best transmit energy level to improve the efficiency of the energy per bit.Experimental results show that,compared with the algorithm of energy optimization based on nodes awareness mechanism and the algorithm of energy efficiency based on IEEE 802.15.4 mobile sensor network,the average bit error rate is decreased by 276.3% and 147.6%,and the total network energy efficiency is increased by 26.7% and 29.2%.Therefore,it has a better effect on reducing the bit error rate of data transmission and improving network energy efficiency.
出处
《计算机工程》
CAS
CSCD
北大核心
2016年第6期108-112,共5页
Computer Engineering
基金
河南省科技厅发展计划基金资助项目(142102110088
152102110039)
关键词
无线网络
能量优化
阴影衰落信道建模
训练序列分组
伽玛和修正贝塞尔函数
数据传输效率
wireless network
energy optimization
shadow fading channel modeling
training sequence grouping
Gamma and modified Bessel function
data transmission efficiency
