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Handling large volume of data seems to have attracted by number of IoT researchers due to its increased number and volume of data generation. Growing size of data is more complex to handle which might consists of both relevant and irrelevant data even though they are collected from the same kind of IoT devices. This large volume of data handling can be improvised by integrating them together instead of keeping and maintaining multiple copies of same data. This can be performed by introducing the data fusion technique which allows IoT researchers to combine the data’s from multiple sources together. There are various research techniques has been introduced earlier for performing the data fusion each follows different procedures. In the existing work, Distributed Hierarchical Data Fusion Architecture (DHDFA) is introduced for the data fusion process where data’s collected from the multiple devices will be fused together in multiple levels. However accuracy of this existing research technique is lesser where meaning of data is not considered. This issue is rectified in the proposed research work by introducing the method called Semantic based Hierarchical Data Fusion Technique (SHDFT). In the proposed research work, data fusion is performed in hierarchical manner where data fusion is performed in three levels namely low level, middle level and high level. Here accuracy of the data fusion is improvised by performing the data fusion in the higher level of data fusion process by considering the semantic meaning of the data. Finally performance of the data fusion outcome is tested and analysed by introducing the Convolutional neural network based prediction framework which will learn and analyse the data fusion outcome in terms of error rate. Based on this outcome, data fusion performance can be analysed accurately. The overall evaluation of the research work is done in the matlab in terms of accuracy, error rate and memory consumption against the existing research technique to prove the proposed method effectiveness.