Localization accuracy of farmland wireless sensor network localization algorithm based on received signal strength indicator

Localization accuracy of farmland wireless sensor network localization algorithm based on received signal strength indicator

Yisheng Miao Huarui Wu  Huaji Zhu  Yuling Song 

National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, China

Corresponding Author Email: 
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This paper attempts to improve the localization accuracy under the farmland environment. The accuracy of the localization algorithms based on received signal strength indicator (RSSI) hinges on the working environment. However, there are many disturbances during the wireless signal propagation in farmland wireless sensor network (WSN), such as fading, shielding, reflection and scattering. The impacts of these disturbances vary with the plant growth. Considering these, the author adopted an improved RSSI-based localization method, which divides the target area into multiple small triangles and let each node decides its local triangle. Then, a global path loss exponent was calculated for the entire localization area, and local exponents were also computed for each small triangle. Through example verification, the proposed algorithm was proved to have good localization accuracy and good adaptability to the time-varying environment in farmland. The research findings provide a reference for location estimation in large-scale farmland and real-time channel modeling.


farmland wireless sensor network (WSN), localization methods, received signal strength indicator (RSSI), range based localization, path loss exponent

1. Introduction
2. Network model and assumptions
3. Evaluation of Path Loss Exponent
4. Localization Based on Rssi
5. Experiment and analysis
6. Conclusions

This work was financially supported by Beijing Natural Science Foundation (4172024), Natural Science Foundation of China (61871041, 61571051) and Opening Foundation of Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture (2017AIOT-08).


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