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Dense RFID environments pose critical challenges such as Reader-to-Reader Interference (RRI), Reader-to-Tag Collisions (RTC), and inefficient resource utilization, which degrade system performance and scalability. Traditional Media Access Control (MAC) protocols, including CSMA and TDMA, struggle to address these issues effectively, particularly in dynamic and large-scale deployments. This paper introduces MCSMARA (Markov Decision Process (MDP)-based Carrier Sense Multiple Access with Reader Arbitration), a novel MAC protocol designed to optimize reader coordination in dense RFID networks. By leveraging an MDP framework, MCSMARA models reader state transitions and employs a utility-based arbitration mechanism to dynamically allocate frequencies and time slots. The protocol incorporates adaptive backoff and decentralized neighborhood discovery for efficient resource management without centralized control. Simulation results demonstrate that MCSMARA reduces collisions by up to 30%, improves throughput by 25%, and ensures superior scalability, supporting a large amount of readers with minimal computational overhead. These findings establish MCSMARA as a transformative solution for RFID networks in logistics, retail, and industrial IoT, with potential for extension to mobile and heterogeneous environments. 

In dense RFID systems, power control provides an effective means for maintaining communication efficiency and preventing reader-to-reader and reader-to-tag interference. Traditional RFID systems often operate at fixed power levels, which can lead to communication bottlenecks and inefficient tag reads in dynamic environments. This paper proposes an adaptive power control technique to improve the system performance by dynamically adjusting the transmit power based on environmental conditions, tag distance, and network congestion. Simulations and experimental results demonstrate that the proposed approach improves tag read rates, reduces interference, and enhances system robustness in dense environments. 

In this work, we integrate digital twin technology with RFID localization to achieve real-time monitoring of physical items in a large-scale complex environment, such as warehouses and retail stores. To map the item-level realities into a digital environment, we proposed a sensor fusion technique that merges a 3D map created by RGB-D and tracking cameras with real-time RFID tag location estimation derived from our novel Bayesian filter approach. Unlike mainstream localization methods, which rely on phase or RSSI measurements, our proposed method leverages a fixed RF transmission power model. This approach extends localization capabilities to all existing RFID devices, offering a significant advancement over conventional techniques. As a result, the proposed method transforms any RFID device into a digital twin scanner with the support of RGB-D cameras. To evaluate the performance of the proposed method, we prototype the system with commercial off-the-shelf (COTS) equipment in two representative retail scenarios. The overall performance of the system is demonstrated in a mock retail apparel store covering an area of 207 m2, while the quantitative experimental results are examined in a small-scale testbed to showcase the accuracy of item-level tag localization.