This paper addresses multiple target tracking (MTT) in wireless sensor networks (WSN) where the nonlinear observed system is assumed to progress according to a probabilistic state space model. In this paper, we propose to improve the use of the quantized variational filtering (QVF) by optimally quantizing the data collected by the sensors and estimating the channel attenuation between sensors. Our proposed technique is intended to jointly estimate the multiple target positions by using the Hybrid QVF and Sequential Monte Carlo-based approach to data association (SMCDA) algorithm, optimize the number of quantization bits per observation and estimate the fading channel coefficient. The adaptive quantization is achieved by maximizing the predicted Fisher information and the fading channel coefficient is estimated by maximizing the a posteriori distribution. The simulation results show that the adaptive quantization algorithm, outperforms both the centralized quantized particle filter (QPF) and the VF algorithm based on binary sensors (BVF).