Space diversity model for field monitoring systems deployed in agriculture

Abstract

With the rapid technical development, advance technical concepts are applied to enhance the efficient of agriculture sector. One of the key technical concepts is field monitoring networks. Today wired & wireless networks are mainly deployed on agriculture fields. Different kind of information such as temperature, relative humidity, wind speed, wind direction, soil moisture, CO2 concentration, illumination, leaf wetness, PH indication of soil, light intensity and rainfall volume has been collected and sent to analyzing central office over this network. There is huge barrier to increase reliability of this network due to lack of power on these fields. To improve reliability of network, it needs some time complex operations, high end error control coding mechanisms. It will lead uncertainty on node, since node is running with low power. Improve the field monitoring network reliability without exceeding exciting power consumption of sensor nodes. Space diversity model based on multiple data collection points instead of single data collection point has been introduced to proposed network model. The new algorithms have been introduced under the thesis on data link layer, network layer and application layer. Rest of the things, which are not defined under this thesis, is followed IEEE 802.15.4 specifications. Different data combining techniques such as selective gain combining, equal gain combining & maximal ratio combining have been analyzed over different fading mechanisms such as Rayleigh, Rician & Gaussian on top MathLab 7.0 environment under different routing strategies on field monitoring network. For each simulation, BER vs. EblNo has been taken as output and completed the comparison. Finally it is proofed that, the performance on EblNo & reliability of field monitoring network can be increased with using this space diversity model based on two data collection points with maximal ratio combining on data collection server. Further, this model can be increased up