A study on outdoor localization method by recurrent deep learning based on time series of received signal strength from low power wireless tag

Abstract

In order to detect estrus and abnormalities from the interactions of grazing cattle, we are developing a position estimation method using low-power wireless devices. In this paper, in order to obtain a natural trail of cow's locations, we propose a localization method based on long short term memory. Our evaluations show that the proposed method suppresses unnatural trail and achieves the average location error of 5.25 m for the cow that is used for learning and about 6 m for the other cows.

BLE-Based Outdoor Localization With Two-Ray Ground-Reflection Model Using Optimization Algorithms

“…In this study, we consider a 100-meter wide square field surrounded by eight anchor nodes to remain close to the real situation presented in [3]. Considering that the usage environment is outdoor, we choose to use the 2.4 GHz frequency because of its longer transmission distance compared to 5 GHz.…”

“…In a previous study, the location information of Japanese Wagyu cattle was used to predict estrus [3]. Indeed, when cattle are in heat, they tend to chase other cattle and try to mount them.…”