摘要： 农田无线传感器网络（WSN）应用环境复杂，影响网络传输的因素包括环境变化、作物生长等。路由协议作为网络数据采集过程中的重要环节，其能耗优化是近年来农田WSN领域的研究热点。传统的能耗优化路由算法多数只针对静态网络环境，难以适用于动态变化的农田监测场景。为此，提出一种基于改进粒子群（PSO）的路由优化算法RD-PSO。将不同的路由传输路径抽象为粒子，根据农田网络能耗、剩余能量、网络传输跳数、链路质量等关键因子构建适应度函数，以提高路径寻优的环境适应性。同时，针对PSO路由随机初始化时迭代效率低的问题，采用反向探测方法确定网络节点的初始化拓扑位置，缩短初始位置与最优解的距离，从而提高算法的收敛速度。实验结果表明，相较ELMR、EEABR和MR-PSO路由算法，RD-PSO算法具有更快的收敛速度，在网络生命周期、能耗均衡效果以及平均传输跳数等方面性能较优，其能提高路由算法在农田动态场景中的适配性。

关键词: 农田无线传感器网络, 路由算法, 路径动态选择, 粒子群算法, 能耗优化

Abstract: The application environment of a farmland Wireless Sensor Network(WSN) is complex.The factors affecting network transmission include environmental variables, crop growth, etc.The routing protocol is an important link in the network data collection process.Therefore, research activities focused on energy consumption optimization for farmland WSN has garnered increased attention recently.Most traditional energy consumption optimization routing algorithms are designed for static network environments, which are difficult to apply to dynamic farmland monitoring scenarios.Therefore, we propose a routing optimization algorithm, namely, RD-PSO, based on improved Particle Swarm Optimization(PSO) in this study.Different routing transmission paths are abstracted as particles, and the fitness function is constructed according to the key factors, such as farmland network energy consumption, residual energy, network transmission hops, and link quality, to improve the environmental adaptability of path optimization.Furthermore, aiming to improve the low iterative efficiency of PSO routing during random initialization, a reverse detection method is used to determine the initialization topology position of the network nodes, shorten the distance between the initial position and optimal solution, and improve the convergence speed of the algorithm.The experimental results demonstrate that compared with ELMR, EEABR, and MR-PSO routing algorithms, RD-PSO attains a faster convergence speed and better performance in network life cycle, energy consumption balance effect, and average transmission hops.These developments ensure that the adaptability of our routing algorithm is superior in the dynamic environment of farmland compared with the existing methods.

Key words: farmland Wireless Sensor Network(WSN), routing algorithm, path dynamic selection, Particle Swarm Optimization(PSO) algorithm, energy consumption optimization

中图分类号: 

• TP391