Under-Determined Blind Source Separation Anti-collision Algorithm for RFID Based on Adaptive Tree Grouping

Anti-collision protocols for radio frequency identification (RFID) tag reading processes are designed to reduce the frequency of collisions and to quickly identify all RFID tags during a tag reading collision process. In this paper, we propose a tree-based anti-collision algorithm using a bit change sensing unit (TABCS) based on the bi-slotted collision tracking tree algorithm (BSCTTA) algorithm. The proposed algorithm can identify bits returned from tags using a bit change sensing unit, even in the event of multiple collisions. The algorithm rapidly generates a unique prefix that identifies each tag and reduces the total number of bits. Simulation results show that the proposed algorithm reduces the cost of tag identification compared with legacy algorithms, such as the query tree algorithm with a bit change sensing unit (QT-BCS) or BSCTTA. When the number of tags is 10000, the average number of tags identified per second in TABCS is improved by 4%, and TABCS requires 28% fewer steps to successfully identify one tag compared to BSCTTA.

The most important research in the world in these days, research that looking at the internet of thing's (IoT) topics and their applications. Most of these applications depend on RFID system, which includes RFID readers and tags. The important issues in RFID system or network are how we can reduce anti-collision between readers to identify and read tags data. In these paper, we suggest an Improved anti-collision protocol, which can be used to connect RFID readers with RFID tags and reduce the number of RFID tag's collisions. The simulation shows that an Improved Class-1 Generation 2 algorithm is better than Slotted Aloha, Class-1 Generation-2 (Number of Tags Known), Class-1 Generation-2 (Number of Tags Unknown) algorithms.

The role of the intelligent optimization algorithm in the blind separation algorithm is to make the independence decision function enter the global optimal position, but the performance of many optimization algorithms depends on the choice of control parameters. In this paper, we use the simplex neighborhood convex set approximation idea, combined with the full random search and multi-role state of the search operator, we use a single-parameter surface-simplex swarm evolution (SSSE), which has a single parameter and pure randomness. The characteristics of the search can overcome the shortcomings of the algorithm’s dependence on the control parameters and easily fall into the local optimum, and improve the application effectiveness of the blind separation algorithm. In the experiment, the fourth-order cumulant (kurtness) is used as the criterion function in ICA, and the improved blind separation algorithm is applied to the blind separation of speech and background music signals. Experimental results show that the improved algorithm effectively separates speech components from the background. The music component and the algorithm have better performance in terms of convergence speed, algorithm stability, and separation effect.

Most of the existing algorithms for blind sources separation have a limitation that sources are statistically independent. However, in many practical applications, the source signals are nonnegative and mutual statistically dependent signals. When the observations are nonnegative linear combinations of nonnegative sources, the correlation coefficients of the observations are larger than these of source signals. In this letter, a novel Nonnegative Matrix Factorization (NMF) algorithm with least correlated component constraints to blind separation of convolutive mixed sources is proposed. The algorithm relaxes the source independence assumption and has low-complexity algebraic computations. Simulation results on blind source separation including real face image data indicate that the sources can be successfully recovered with the algorithm.

In this paper, we propose a novel anti-collision algorithm for generation 2 RFID system to estimate the number of tags and to use the optimal frame size. Depending on the number of tags, there exists the optimal frame size in the sense that the system efficiency is maximized. The number of tags can be estimated from the collision ratio in terms of collided slots per frame. In addition, we compare our proposed algorithm with other existing schemes by simulation. The proposed scheme is easy to implement, and the simulation results show that its performance improves significantly compared to other schemes in terms of the identification time and the number of consumed slots.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

The dynamic frame slot ALOHA (DFSA) algorithm is a typical anti-collision. For the shortcomings of DFSA algorithm, this paper presents an improved algorithm - IDFSA. On the one hand, IDFSA algorithm to improve the label estimation algorithm, so as to achieve dynamic adjustment. On the other hand, through the analysis of mathematics, the new grouping of labels is improved, and the phenomenon of landslide is improved. The simulation results show that the throughput of IDFSA algorithm is higher, and the total operations time is less. RFID (radio frequency identification, RFID) is widely used in logistics, identification, anti-theft and other fields. A traditional RFID system mainly includes two parts: the reader and the tag. In the case of the number of tags more dense, the reader must use the anti-collision to identify the individual tags. So, anti collision algorithm is very important in multi label application field. Tag anti-collision protocols (1) can be categorized into two groups: probabilistic anti-collision methods and deterministic anti-collision methods. The probabilistic anti-collision methods are Aloha-based protocols (2) such as pure Aloha protocol, Slotted ALOHA (SA) protocol, Framed Slotted ALOHA (FSA) protocol and Dynamic Framed Slotted ALOHA (DFSA) (3, 4) protocol. In this paper, an improved dynamic frame slot ALOHA (IDFSA) algorithm is proposed. 2. Related work Frame slotted ALOHA algorithm will be a plurality of time slots set as a frame, for a fixed frame length, when the tag number is less than the length of the frame appears waste the time slot, when the frame length is less than or equal to the number of tags conflict too frequent . In view of this situation, according to the number of tags in the reader range, dynamic adjustment of the next frame length, by a decrease or increase the length of the frame, you can solve the slot waste and tag identification time increased, this method is called dynamic frame slotted ALOHA (DFSA). The analysis of DFSA algorithm shows that the throughput of the system is mainly determined by the estimation of the number of tags and the adjustment of the frame length. The estimation of the number of tags plays a key role in the performance of the algorithm. At present, there are three kinds of label estimation algorithms, Vogt, Schoute and Lowbound

Radio Frequency IDentification (RFID) systems have rich applications in daily life. A crucial problem in RFID systems is to estimate the cardinality of RFID tags. Most exiting probabilistic RFID cardinality estimation algorithms utilize a certain pattern hidden in the response vector formed by the tag responses to make estimators. In this paper, we argue that finding the patterns is actually not necessary! In this paper, we present a novel approach to RFID cardinality estimation by making the full use of the whole response vector. We characterize the critical relationship between the number of RFID tags and the specific distribution of the bits in the response vector. In this paper, we consider two kinds of responses, i.e., empty / non-empty responses and empty / singleton / collision responses. To cater for RFID systems with a very large number of tags, we also generalize our estimation approach to handle geometrically distributed response vectors. Both rigid theoretical analysis and extensive simulations have been conducted and the conclusive results demonstrate that our approach is more accurate than existing state-of-the-art RFID estimation approaches.

Dynamic time slot frame ALOHA algorithm is currently the most widely used anti-collision technology in radio frequency identification (RFID) system. Based on the traditional ALOHA algorithm analysis, we propose an improved algorithm. The algorithm is based on a packet adaptive ALOHA anti-collision algorithm (PA-ALOHA). First, the reader scans and counts the time slots which are randomly selected by tags, and sends it to each tag. The tags, then, adjust accordingly the time slot that enable the reader to skip idle slots and collision slots, adaptively allocate effective slots, and then identify tags rapidly. The algorithm employs packet and dynamically adjusts the frame size and other strategies, in order to reduce the time of processing slots. Simulation results show that PA-ALOHA algorithm improves the efficiency and stability of the system and reduce the transmission overhead. Especially, when the number of tags is over 1000, the algorithm throughput is still above 70% that has been greatly improved system efficiency than conventional ALOHA algorithm.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

In this paper, we present a dynamic power adjustment (DPA) scheme for a reader to speed up the tag identification (ID) time in a RFID system. By using TDMA frames, the proposed DPA can adaptively increase or decrease the transmission power of a reader. A factor is designed to adjust the transmission power of a reader by considering two aspects. The first aspect increases the power to read more tags, if the number of idle state far exceeds the number of collided state. The second aspect cut power to reduce the number of tag readings, if the number of idle state is much smaller than the number of collided state. The proposed DPA is simulated using NS-2. In the simulation, we design three different topologies which place tags in three distributions, uniform, random, and hotspot. From the simulation results, we demonstrate that the proposed DPA can speed up the tag identification time, particularly when a large number of tags are placed in hotspot distribution.

This paper discusses the separation of signals in multiple-tag radio frequency identification (RFID) systems. First, a model for the RFID system in both single and multiple tag environments is presented. Then, an analytical constant modulus algorithm (ACMA) for the blind source separation problem is reviewed. An alternative approach to the traditional ACMA using joint diagonalization is considered. Finally, both the ACMAs are applied to the multiple-tag RFID environment and performance of the system is studied. Simulations are carried out for 4-QAM and 16-QAM modulations at tag and analyses of the simulation results reveal that the ACMA with joint diagonalization takes lesser CPU time to execute compared to the traditional ACMA, but with higher average modulus error (AME). The variation of system performance with the number of measurements, SNR (signal to noise ratio), number of tags and number of antennas at the reader is also studied. Based on these, some design guidelines are presented. Interestingly both the ACMA algorithms work for the 16-QAM case and yield trends similar to those of 8-PSK and 4-QAM.

Collision is a familiar problem in a RFID (radio frequency identification) system. How to solve this problem effectively is very important to the whole RFID system. A RFID system is always made up of two indispensable components: one is the tag, the other is the reader. Collisions occur when there are so many tags within the interrogation zone of a reader communicating with the reader synchronously. Binary search algorithm is an important anti-collision algorithm to solve the collision problems. But the main disadvantage of any existing algorithm based on binary search algorithm is the rate of identifying the identification codes (IDs) of tags is slow. In this paper, an improved anti-collision algorithm is proposed. Using this algorithm will recognize more tags in less time compared with other existing algorithms

Anti-collision algorithm is very important in the RFID system, because it decides tag identification time and tag identification accuracy. We propose improved anti-collision algorithms using Bin slot in RFID system. In the proposed algorithms, if the reader memorizes the Bin slot information, it can reduce the repetition of unnecessary PingID command and the time to identify tags. If we also use ScrollAllID command in the proposed algorithm, the reader knows the sequence of collided ID bits. Using this sequence, the reader can reduce the repetition of PingID command and tag identification time. We analyze the performance of the proposed anti-collision algorithms and compare the performance of the proposed algorithms with that of the conventional algorithm. We also validate analytic results using simulation. According to the analysis, for the random tag ID, comparing the proposed algorithms with the conventional algorithm, the performance of the proposed algorithms is about 130% higher when the number of the tags is 200. And for the sequential tag ID, the performance of the conventional algorithm decreases. On the contrary, the performance of the proposed algorithm using ScrollAUID command is about 16% higher than the case of using random tag ID.

The problem of noise amplification in the multichannel blind separation of communication signals is researched. A new algorithm and its improved algorithm to seek the separation matrix is proposed based on the idea of maximizing the signal-to-interference-plus-noise power ratio (SINR). When the condition number of the separation matrix is large, the separation matrix sought by this algorithm and its improved algorithm can beautifully solve the problem of separation performance degradation caused by noise amplification. Simulation results show that this algorithm and its improved algorithm can reduce the bit error rate effectively and improve the performance of blind separation, exhibiting practical application value.