Localization technique of IoT nodes using artificial neural networks (ANN)

Beata Krupanek, Ryszard Bogacz


One of the ways to improve calculations related to determining the position of a node in the Iot measurement system is to use artificial neural networks (ANN) to calculate coordinates. The method described in the article is based on the measurement of the RSSI (Received Signal Strenght Indicator), whitch value is then processed by the neural network. Hence, the proposed system works in two stages. In the first stage, RSSI coefficient samples are taken, and then the node location is determined on an ongoing basis. Coordinates anchor nodes (i.e. sensors with fixed and previously known positions) and the matrix of RSSI coefficients are used in the learning process of the neural network. Then the RSSI matrix determined for the system in which the nodes with unknown positions are located is fed into the neural network inputs. The result of the work is a system and algorithm that allows determining the location of the object without processing data separately in nodes with low computational performance.

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K. Anup, S. Takuro: Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges, J. Sens. Actuator Netw., 2017, vol. 6.

E. Figueiredo, P. Silva, V. Kaseva, E.S. Lohan: Wireless Positioning in IoT: A Look at Current and Future Trends, Sensors, 2018, 18, pp. 2470.

B. Wu, C. Jen, and K. Chang, Neural fuzzy based indoor localization by Kalman filtering with propagation channel modeling,in: Proceedings IEEE International Conference on Systems, Man and Cybernetics, 2007,pp. 812–817

R. Battiti, T.L. Nhat, A. Villani, Location-aware computing: a neural network model for determining location in wireless LANs, Technical Report DIT-02-0083, University of Trento, Department of Information & Communication Technology (2002)

J.P Tian, and H.C Shi, Study of localization scheme base on neural network for wireless sensor networks’ in the Proceedings of IET Conference on Wireless, Mobile and Sensor Networks , pp.64-67

W. H. Kuo, Y. S. Chen, K. T. Cheng, T. W Lu.: Signal Strength Based Indoor and Outdoor Localization Scheme in Zigbee Sensor Networks, IAENG International Journal of Computer Science, vol. 43, 2016.

J. Luo: Range Error Correction In RSSI-based Wireless Sensor Node Localization, Proceedings of 2014 IEEE International Conference on Mechatronics and Automation, August 3 - 6, Tianjin, China.

S. H. Chagas, L. de Oliveira, J. Baptista, S. Martins: Node localization in Wireless Sensor Networks Using Artificial Neural Networks and Optimization Based on Simulated Annealing Algorithm. XXVII SIM - South Symposium on Microelectronics.

B. Krupanek, R. Bogacz: Zwiększenie dokładności lokalizacji węzłów systemów bezprzewodowych. Pomiary w nauce i technice. 2019

S. Kumar, S. Lee: Localization with RSSI values for Wireless Sensor Networks: An Artificial Neural Network Approach. Conference Proceedings Paper – Sensors and Application. International Electronics Conference on Sensors and Aplications. 2014


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