Anti-Collision Algorithm for Identification in Precision Agriculture Applications

Precision Agriculture (PA) techniques employing Internet of Things (IoT) can significantly improve crop yields and enhance productivity. Radio frequency identification (RFID) based IoT systems can enhance precision livestock farming by enabling livestock traceability and identification in large farms, but congestion is a major challenge. In such conditions, livestock estimation using RFID is a complex task because of high chances of tag collision due to significant increase in interference at the gates. Several anti-collision algorithms have been explored in recent past to address this issue with the aid of a single antenna. However, the throughput gains from a single antenna are limited particularly for dense scenarios like gates at a small ruminant farm. Therefore, we propose a multi-antenna RFID reader system with a dynamic frame slotted ALOHA (DFSA) anti-collision algorithm to address the tag collision problem by exploiting the spatial diversity of the multiple inputs and multiple outputs (MIMO) backscattering channel. The proposed technique is simulated in MATLAB for several tag population figures both with and without interference actions of various intensities. Results have shown that significant throughput gains can be achieved, in comparison with gains attained by anti-collision algorithms based on a single antenna. The simulation testbed enables us to evaluate the proposed technique for multiple receive antenna configurations.