| 摘要: |
| 为了改善LoRa传输过程中的干扰冲突问题，提出了一种基于烟花爆炸式混合蛙跳算法的LoRa网络参数分配策略。首先，针对混合蛙跳算法存在易早熟、易陷入局部最优等不足，改变分配种群方式，同时引入反向学习、自适应烟花爆炸机制和高斯变异算子提高算法的搜索性能。其次，以最大化节点平均传输成功率为优化目标，并将接收灵敏度作为约束系数，保证信息能够被接收的前提下分配最佳参数。仿真结果表明，所提的分配策略优于其他分配方案，能显著降低节点碰撞概率，提高节点信息接收率。 |
| 关键词: 物联网 LoRa网络参数分配 混合蛙跳算法 反向学习 烟花爆炸 |
| DOI： |
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| 基金项目:国家自然科学基金资助项目(61671254,61871239)；国家电网科技项目(KJ20-1-32) |
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| LoRa network parameter allocation strategy based on firework explosive shuffled frog leaping algorithm |
| ZHOU Chao,ZHANG Hui,YANG Maoheng,ZHENG Tianyu,JIANG Meijun |
| (Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology,Nankai University,Tianjin 300350,China) |
| Abstract: |
| In order to improve the interference and conflict problem in the LoRa transmission process,a LoRa network parameter allocation strategy based on a firework explosive shuffled frog leaping algorithm is proposed.First of all,to address the shortcomings that the shuffled frog leaping algorithm is easy to mature and be falling into local optimality,the allocation method is changed,and reverse learning,adaptive firework explosion mechanism and Gaussian mutation operator are introduced to improve the search performance of the algorithm.Secondly,maximizing the average node transmission success rate is set as the optimization target,and the receiving sensitivity is used as the constraint coefficient to allocate the best parameters under the premise that the information can be received.The simulation results show that the allocation strategy based on the firework explosive shuffled frog leaping algorithm is better than other schemes,which can significantly reduce the probability of node collision and increase the rate of node information extraction. |
| Key words: Internet of Things LoRa network parameter allocation shuffled frog leaping algorithm reverse learning firework explosion |
