Abstract:

In order to improve the locating accuracy in Wireless Senor Network (WSN) node localization, an algorithm based on Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA) was proposed, namely Bi-system Cooperative Optimization (BCO) algorithm. With the advantages of fast convergence in PSO and high optimization precision in SFLA, the proposed algorithm was easier to converge through less iterations and achieve higher accuracy of depth search. The simulation experiments indicate that the BCO algorithm is effective. First, the BCO algorithm can be very close to the optimal solution when it is used for solving the test target functions with better locating accuracy and higher convergence speed. Meanwhile, when the proposed algorithm is used for node localization based on Received Signal Strength Indicator (RSSI), the absolute distance error of the prediction location and the actual location is less than 0.05 meters. Compared with the Improved Particle Swarm Optimization algorithm based on RSSI (IPSO-RSSI), the locating accuracy of the proposed algorithm can be increased 10 times at least.

Key words: node localization, Particle Swarm Optimization (PSO) algorithm, Shuffled Frog Leaping Algorithm (SFLA), parallel system, cooperative optimization, positioning accuracy

摘要：

为进一步提高无线传感器网络(WSN)中节点的定位精度,提出了一种双系统协同进化(BCO)算法。改进算法利用粒子群优化(PSO)算法快速收敛的特性和混合蛙跳算法(SFLA)较高的寻优精度的特性,在较少的迭代次数内快速收敛且实现深度搜索达到较高的精度。仿真实验结果表明:在应用双系统协同进化算法对测试目标函数进行求解时,能非常接近最优解;同时将该算法应用到基于接收信号强度值(RSSI)测距的节点定位中,预测位置与实际位置的绝对误差在0.05 m范围内;相比基于RSSI的分步粒子群算法(IPSO-RSSI),其定位精度至少提高了10倍。

关键词: 节点定位, 粒子群算法, 蛙跳算法, 并行系统, 协同进化, 定位精度