Pathloss Prediction Model of a Wireless Sensor Network in an Indoor Environment
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The propagation of radio signals is affected by several factors that contribute to the degradation of its quality and these are more significant on the propagation of wireless signals with low-power radios, typically used in Wireless Sensor Networks (WSNs). This makes radio links in WSNs to be unpredictable. This paper presents the development of an indoor channel prediction model of Wireless Sensor Network (WSN). A WSN testbed comprising of four TelosB sensor nodes was built at the corridor of first floor of Goddian Ezekwem building, faculty of Engineering, Nnamdi Azikiwe University, Awka. Three of the sensor nodes were placed at angles and the remaining one was attached to a laptop and used as the sink. Extensive measurements of RSSI were taken at distances of 1m to 7m at the interval of 1m. Channel prediction model of the environment was developed from the measured data using Linear Regression Analysis. The developed model was tested and the regression data analysis gives the R Square (R2) as 0.86, Multiple R as 0.93 and Adjusted R Square as 0.70.