Enhanced RFID tag detection in IoT environments using improved FCM–DFSA approach

The rapid growth of information technology has integrated the Internet of Things (IoT) into everyday life, with Radio Frequency Identification (RFID) serving an essential role in this evolution. RFID technology has been critical across multiple industries, particularly in optimizing airport baggage handling procedures, thereby enhancing efficiency and safety of the process. A significant challenge in RFID systems is tag collision, which occurs when numerous tags simultaneously respond to a reader, leading to signal interference and increasing the likelihood of undetected tags in the reader range. This necessitates the use of anti-collision algorithms that facilitate the sequential identification of tags, reduce data loss, and improve tracking precision. Detection of missing tags and anti-collision algorithms are crucial in RFID systems, which ensure precise tracking and inventory management by preventing data loss during consecutive tag responses. These methods optimize system efficiency by reducing collisions, thereby enhancing identification rates in high-density settings. Existing anti-collision techniques, such as tree-based and Aloha-based algorithms, demonstrate deficiencies in high-density settings, particularly for dynamic tag populations and mobile readers. These methods frequently encounter challenges regarding scalability and efficiency, resulting in increased latencies and operational costs. This framework presents a new anti-collision algorithm: the Improved FCM–DFSA-Based Anti-Collision Algorithm. The proposed method effectively clusters tags according to the signal properties by integrating enhanced Fuzzy C-Means clustering with the Dynamic Frame Slotted Aloha protocol (DFSA), thereby minimizing collision occurrences. It dynamically modifies the frame dimensions based on real-time assessments, enhancing the communication intervals for faster and more dependable tag readings. The enhanced method exhibits substantial advancements in throughput and energy efficiency, making it suitable for resource-limited IoT applications. The experimental findings demonstrate that the proposed method significantly reduces collisions, resulting in enhanced identification rates and a lower likelihood of tag misses. This work provides significant insights into the formulation of effective anti-collision solutions essential for advancing the realm of RFID applications within the IoT framework.