The IoT aims at creating a world that connects physical and virtual objects and integrates them. The IoT generates a range of large, multi-sourcing, heterogeneous, and sparse data sets with the participation of numerous wireless sensor devices. Fusion of data is mainly used to minimize data size and to optimize data traffic volume and extract significant raw data from which IoT services can be improved and intelligent services delivered. The Internet of Things (IoT) is a type of multi-source data preparation that seeks to provide a complete, perfect, and accurate inspection and input comparing actual data. Usually, a combination of multi-sensor information can manage a similar type. Nonetheless, as new qualities arise in IoT, interoperable innovations arranged to assist in the division and combination of certified information between heterogeneous IoT-related gadgets will be necessary. A design which can provide guidance to improve the IoT data combination is required to solve these problems. There is a lot of information in the literature, as well as IoT data fusion studies. This paper aims to provide certain possible aspects to address the above-mentioned challenges. This paper aims at data fusion techniques applied to overcome problems faced by IoT enabled autonomous systems and also addresses traditional data fusion algorithms and advanced data fusion algorithms. Two models were presented. One is a single filter and the second one is a multi-filer model. In the IMM combination stage, a model's weight is proportional to its likelihood, and as projected in results, there is a variation in error rate. With the single object detection method, the error rate generated is 30%, but with the use of the multi filter model, the error rate generated is reduced to 25% and became 5%.

物联网旨在创建一个连接物理和虚拟对象并将它们集成在一起的世界。物联网在众多无线传感器设备的参与下生成了一系列大型、多源、异构和稀疏的数据集。数据融合主要用于最小化数据规模和优化数据流量，并提取重要的原始数据，从中可以改进物联网服务并提供智能服务。物联网 (IoT) 是一种多源数据准备，旨在提供完整、完美、准确的检查和输入对比实际数据。通常，多传感器信息的组合可以管理类似的类型。尽管如此，随着物联网新特性的出现，有助于在异构物联网相关小工具之间划分和组合认证信息的互操作创新将是必要的。解决这些问题需要一种可以为改进物联网数据组合提供指导的设计。文献中有很多信息，以及物联网数据融合研究。本文旨在提供某些可能的方面来解决上述挑战。本文旨在解决应用于克服物联网自主系统面临的问题的数据融合技术，并解决传统数据融合算法和高级数据融合算法。介绍了两个模型。一个是单过滤器，第二个是多过滤器模型。在 IMM 组合阶段，模型的权重与其似然成正比，正如结果所预测的那样，错误率存在变化。使用单物体检测方法，产生的错误率为30%，但使用多滤波器模型，产生的错误率降低到25%，变为5%。