基于改进差分的多障碍视觉传感器网络部署优化

Multi-obstacle visual sensor network deployment optimization based on improved difference algorithm

与传统无线传感器网络(WSN)不同,视觉传感器对障碍物敏感度远高于其他传感器,障碍物直接影响视觉传感器的有效感知范围.受实际场景模型仿真的局限,目前视觉传感器网络的优化部署研究大多忽略实际多障碍情形,集中于无障碍范围内情形.建筑信息模型(Build Information Model,BIM)技术可以用来自动提供实际场景要素的几何和非几何属性,可为优化算法提供可靠的场景模型输入数据.本文提出一种BIM和分段自适应迁移差分算法(Segment Adaptive Migration Difference Evolution,SAMDE)融合框架BIM-SAMDE,并提出一种多节点有效感知区域分析算法辅助计算有效感知率,对协同视觉传感器网络在多障碍情况下的感知覆盖进行优化部署.仿真实验表明,所提出的BIM-SAMDE框架能够自动获取多障碍场景数据,模型构建更为智能化;所提出的SAMDE算法在多障碍情形的部署优化问题中寻优性能优越,收敛速度快,与其他部署优化算法相比具有一定的优势.

The VSN is much more sensitive to the obstacle than other sensors,which means the obstacle can affect the visual sensor perception range,different from the traditional wireless sensor networks(WSNs).Nowadays,most research on optimal VSN deployment mainly focuses on the scenes without obstacles,neglecting multi-obstacle scenes due to the limitation of real scene model simulation.However,Building Information Model(BIM)technology can provide reliable real scene data as input for the optimal algorithm due to the features that it can give the geometric and non-geometric properties of actual scene elements automatically.This paper proposes a novel BIM-SAMDE framework that combines BIM and Segment-Adaptive-Migration Differential Evolution algorithm.This paper also presents a multi-nodes effective perception coverage analysis algorithm to calculate the optimal effective coverage rate for collaborative visual sensor network deployment in multi-obstacle cases.The simulation result shows that the proposed BIM-SAMDE framework can automatically obtain the multi-obstacle scenes data.The proposed SAMDE algorithm has the superior optimizing performance in multi-obstacle scenes,with a fast convergence rate compared with other optimal algorithms.