Application of Green IoT in Agriculture 4.0 and Beyond: Requirements, Challenges and Research Trends in the Era of 5G, LPWANs and Internet of UAV Things
“…This vulnerability increases the false code rate, slows the recognition speed, and reduces the effective recognition distance of the RFID system. Therefore, it is also an unsolved challenge to eliminate this co-channel interference, so that a tag is not read by multiple readers of the same frequency at the same time [111][112][113].…”
With radio frequency identification (RFID) becoming a popular wireless technology, more and more relevant applications are emerging. Therefore, anti-collision algorithms, which determine the time to tag identification and the accuracy of identification, have become very important in RFID systems. This paper presents the algorithms of ALOHA for randomness, the binary tree algorithm for determinism, and a hybrid anti-collision algorithm that combines these two algorithms. To compensate for the low throughput of traditional algorithms, RFID anti-collision algorithms based on blind source separation (BSS) are described, as the tag signals of RFID systems conform to the basic assumptions of the independent component analysis (ICA) algorithm. In the determined case, the ICA algorithm-based RFID anti-collision method is described. In the under-determined case, a combination of tag grouping with a blind separation algorithm and constrained non-negative matrix factorization (NMF) is used to separate the multi-tag mixing problem. Since the estimation of tag or frame length is the main step to solve the RFID anti-collision problem, this paper introduces an anti-collision algorithm based on machine learning to estimate the number of tags.
“…This vulnerability increases the false code rate, slows the recognition speed, and reduces the effective recognition distance of the RFID system. Therefore, it is also an unsolved challenge to eliminate this co-channel interference, so that a tag is not read by multiple readers of the same frequency at the same time [111][112][113].…”
With radio frequency identification (RFID) becoming a popular wireless technology, more and more relevant applications are emerging. Therefore, anti-collision algorithms, which determine the time to tag identification and the accuracy of identification, have become very important in RFID systems. This paper presents the algorithms of ALOHA for randomness, the binary tree algorithm for determinism, and a hybrid anti-collision algorithm that combines these two algorithms. To compensate for the low throughput of traditional algorithms, RFID anti-collision algorithms based on blind source separation (BSS) are described, as the tag signals of RFID systems conform to the basic assumptions of the independent component analysis (ICA) algorithm. In the determined case, the ICA algorithm-based RFID anti-collision method is described. In the under-determined case, a combination of tag grouping with a blind separation algorithm and constrained non-negative matrix factorization (NMF) is used to separate the multi-tag mixing problem. Since the estimation of tag or frame length is the main step to solve the RFID anti-collision problem, this paper introduces an anti-collision algorithm based on machine learning to estimate the number of tags.
“…Numerous communication protocols, ranging from well-established ones like ZigBee to more recent ones like 5G (fifth generation) and NB-IoT (NarrowBand Internet of Things) are available in the tech world for smart farming. ZigBee provides balanced power and range, making it perfect for isolated farms [12], while NB-IoT provides extensive coverage with low energy requirements for large-scale agricultural endeavours [13]. With its speed, low latency, and dependability, 5G is poised to redefine data transfer [14].…”
Section: Communication Technologies: Bridging the Gapmentioning
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