Exploration of Parameter Factor in the RSSI Model

Abstract:

In order to increasing accuracy of the RSSI model and reduce error of parameter factors, the paper analyses and optimizes empirical models through the experimental calculation. It simply fits and compares different regression analysis of the RSSI model firstly, and then explores the rule of environmental factor n and shadowing factor η of RSSI experience model with average error respectively, based on the objective of the experimental data. Using the rule and experimental data, it can optimize RSSI model and improve precision. Empirical results show that optimization theory demonstrates variation tendency of environmental factor n, shadowing factor η with distance, signal intensity in the optimization of RSSI model. The contradiction between errors and parameter factors can be solved effectively by the function-based RSSI model throughout simple measures.

• Periodical: Applied Mechanics and Materials (Volumes 268-270)
• Pages: 1822-1831
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.268-270.1822
• Online since: December 2012
• Authors: Zhi Chao Cao, De Wei Li
• Keywords: Environmental Factor N, RSSI Model, Shadowing Factor η

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