In this paper, we propose two soft computing localization techniques for wireless sensor\nnetworks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural\nNetwork (ANN), focus on a range-based localization method which relies on the measurement of the\nreceived signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout\nthe track cycling field. The soft computing techniques aim to estimate the distance between bicycles\nmoving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS\nwas considered, whereas in the second approach the ANN was hybridized individually with three\noptimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm\n(GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN\noutperforms the other methods adopted in this paper in terms of accuracy localization and distance\nestimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of\n0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.
Loading....