Data fusion is generally defined as the application of methods that combines data from multiple sources and collect information in order to get conclusions. This paper analyzes the signalling time of different data fusion filter models available in the literature with the new community model. The signalling time is calculated based on the data transmission time and processing delay. These parameters reduce the signalling burden on master fusion filter and improves throughput. A comparison of signalling time of the existing data fusion models along with the new community model has also been presented in this paper. The results show that our community model incurs improvement with respect to the existing models in terms of signalling time.
Data fusion is an important area in different fields of applications as it provides a detailed analysis of the data store which helps in analytics and decision support system. Among the numerous Data Fusion models available in literature, the six pioneer pieces of work in terms of
Data Fusion Models are – Waterfall Model, Multisensor Integration Fusion Model, Behavioral Knowledge-based Data Fusion Model, Omnibus Model, Dasarathy Model and JDL Model. In this paper, we have given a comparative study of the above models based on basic features like signalling overhead,
power consumption, fault tolerance, survivability and robustness facilities subsequently. Here, a new improved model, named Community Model has been proposed for better throughput in terms of Data Transmission Time (DTT). This model can be applied to any number of filter levels irrespective
of application areas.
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