室内障碍环境下空气质量监测异构WSN部署

针对室内空气质量中污染性气体众多、浓度分布不均，单一传感器无法有效监测，而且室内障碍物会对传感器部署位置造成影响的问题，通过改进北方苍鹰优化算法（improved northern goshawk optimization, INGO）对障碍下异构传感器进行部署研究。首先，采用SPM混沌映射对种群进行初始化，以解决原始北方苍鹰算法初始化种群多样性不高、覆盖率低、冗余度高的问题；其次，使用非线性步长权重改进Lévy飞行策略，对种群位置进行更新；最后，融合柯西变异和反向学习，解决算法后期种群易陷入局部最优的问题。结果表明，改进的优化算法在无障碍和障碍环境下覆盖率分别达到了94.2%和93.0%，与其他学者在无障碍环境下提出的算法进行对比，覆盖率分别提高了0.8%，1.2%，2.8%，7.1%。INGO算法能够对室内障碍环境下的空气质量监测传感器进行最优部署，为室内空气质量监测等复杂环境异构传感器的部署问题提供科学依据。

Heterogeneous WSN deployment for air quality monitoring in indoor barrier environments

To solve the problem of ineffective monitoring indoor air quality in the environment of numerous and uneven distributed polluting gases with a single sensor, and the issue of indoor obstacles affecting the sensor deployment, the improved Northern Goshawk optimization (INGO) algorithm was used to study the deployment of heterogeneous sensor networks. Firstly, the SPM chaotic mapping was used to initialize the population to solve the problems of low diversity, low coverage, and high redundancy in the initialized population of the original Northern Goshawk algorithm. Secondly, the Lévy flight strategy was improved by using non-linear step weights to update the population location. Finally, the problem that the population tends to fall into local optimum at the later stage of the algorithm was solved by fusing Cauchy variation and backward learning. The results show that the proposed optimization algorithm achieves coverage rates of 942% and 930% in barrier-free and obstructed environments, respectively, and the coverage is improved by 08%, 12%, 28%, and 71%, respectively, compared to algorithms proposed by other scholars in barrier-free environments. Therefore, the INGO algorithm can optimally deploy air quality monitoring sensors in indoor obstacle environments, providing a scientific basis for heterogeneous sensor deployment in complex environments such as indoor air quality detection.