Signalling Cost Analysis of Community Model

摘要

Data fusion is generally defined as the application of methods that combines data from multiple sources and collect that information in order to get conclusions. This paper analyzes the signalling cost of different data fusion filter models available in the literature with the new community model. The signalling cost of the Community Model has been mathematically formulated by incorporating the normalized signalling cost for each transmission. This process reduces the signalling burden on master fusion filter and improves throughput. A comparison of signalling cost 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 cost.