Nowadays transceiver-free (also referred to as device-free) localization using Received Signal Strength (RSS) is a hot topic for researchers due to its widespread applicability. However, RSS is easily affected by the indoor environment, resulting in a dense deployment of reference nodes. Some hybrid systems have already been proposed to help RSS localization, but most of them require additional hardware support. In order to solve this problem, in this paper, we propose two algorithms, which leverage the Packet Received Rate (PRR) to help RSS localization without additional hardware support. Moreover, we take the environment noise information into consideration by utilizing the Signal-to-Noise Ratio (SNR) which is based on the RSS and Noise Floor (NF) information instead of pure RSS. Thus, we can alleviate the noise effect in the environment and make our system more sensitive to the target. Specifically, when reference nodes are sparsely deployed and RSS is very weak, PRR and SNR can help in performing localization more accurately. Our BEYOND RSS system is based on sparse wireless sensor networks, wherein the experimental results show that the average localization error of our approach outperforms the pure RSS based approach by about 15.19%.

Nowadays transceiver-free (also referred as Device- free) localization by using Radio Signal Strength (RSS) is a hot topic for researchers due to its wide applicability. But RSS is easily affected by indoor environment, resulting in dense deployment of reference nodes. Some hybrid systems have already been proposed to help RSS localization, but most of them require additional hardware support. In order to solve this problem, in this paper, we propose two algorithms, which leverage Packet Received Rate (PRR) to help RSS localization without additional hardware support. Specifically, when reference nodes are sparsely deployed and RSS is very weak, our approach is able to accurately localize the target. Based on sparsely wireless sensor network, the experiment results show that the localization accuracy of our approach outperforms the pure RSS based approach by about 12.8%.