RFID reader-to-reader collision avoidance model with multiple-density tag distribution solved by artificial immune network optimization

Li, Zhonghua; Li, Jianming; He, Chunhui; Tang, Chengpei; Zhou, Jing

doi:10.1016/j.asoc.2015.01.056

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…PSO was selected due to its almost similar characteristics with GSA and its application in the similar type of sputtering process [23][24][25], while GA was selected due to its adaptation in other type of sputtering processes [26][27][28]. As for AIS and ACO, these algorithms have been adapted in various engineering parameter optimization problems [29][30][31][32]. All of the algorithms were executed 30 times, with their average fitness values and processing times recorded as shown in Table 6.…”

Section: Resultsmentioning

confidence: 99%

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

et al. 2016

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This research is focusing on the radio frequency (RF) magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA) technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Immune System (AIS) and Ant Colony Optimization (ACO). Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.

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Section: Resultsmentioning

confidence: 99%

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

et al. 2016

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“…In most literatures [5,14,15,16,17,19], the interrogation range is defined as a perfect circumstance where the radius only depends on the reader’s output power. In this paper, we also follow this assumption as UHF RFID readers with known positions and passive RFID tags with fixed positions in centralized MRE:

Readers are randomly distributed over an area in two dimensions.

Any reader has only two states, active and inactive, where only the readers in active operate and interfere with other active ones.

The interference power from multiple interfering readers seen by the desired reader is additive.

…”

Section: Resource Allocation Model In Centralized Mre Under Epcglomentioning

confidence: 99%

“…Based on this, the number of time slots, the number of frequency channels and the position of readers are optimally allocated by using some heuristic algorithms such as genetic algorithm [12], swarm optimization algorithm [13] and artificial immune algorithms [14,15,16,17]. Among these, the artificial immune algorithms output the best performance when compared with other heuristic ones, the advantage of which is mainly embodied in outputting a larger interrogation range.…”

Section: Introductionmentioning

confidence: 99%

“…Among these, the artificial immune algorithms output the best performance when compared with other heuristic ones, the advantage of which is mainly embodied in outputting a larger interrogation range. In [14,15,16,17], the author introduced a reader-to-reader avoidance model formulizing from the signal-to-interference-plus-noise (SINR) view, which could perfectly describe the interference process. However, the author did not consider the non-uniformity distribution of multiple readers deployment in MRE, resulting in the unfairness of scheduling schemes since the readers with no interferences are always operating while the ones dramatically affected by interferences are almost never operating.…”

Section: Introductionmentioning

confidence: 99%

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Geometric Distribution-Based Readers Scheduling Optimization Algorithm Using Artificial Immune System

Duan

Wang

2016

Sensors

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In the multiple-reader environment (MRE) of radio frequency identification (RFID) system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS) is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression) quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range.

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“…In 2013, localization precision of a passive RFID localization system has been improved by using sparsely distributed RFID tags (Yang et al, 2013). In 2014, Li and his group established an RFID reader-to-reader collision avoidance model with multiple-density tag distribution (R2RCAM-MTD) (Li et al, 2015). In Yu et al (2016b), Yu and his team used the Fisher matrix to theoretically prove that the distribution of the multi-tag does affect its reading performance.…”

Section: Introductionmentioning

confidence: 99%

An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO

Zhou

Liu

et al. 2019

Transactions of the Institute of Measurement and Control

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In this paper, a multi-tag optimization method based on image analysis and particle swarm optimization (PSO) neural network is proposed to verify the effect of radio frequency identification (RFID) multi-tag distribution on the performance of the system. A RFID tag detection system is proposed with two charge coupled device (CCD). This system can automatically focus on the tag according to its position, so it can obtain the image information more accurately by template matching and edge detection method. Therefore, the spatial structure of multi-tag and the corresponding reading distance can be obtained for training. Because of its excellent performance in multi-objective optimization, the PSO neural network is used to train and predict multi-tag distribution at the maximum reading distance. Compared with other neural networks, PSO is more accurate and its uptime is shorter for RFID multi-tag analysis.

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RFID reader-to-reader collision avoidance model with multiple-density tag distribution solved by artificial immune network optimization

Cited by 9 publications

References 26 publications

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

Optimization of Nano-Process Deposition Parameters Based on Gravitational Search Algorithm

Geometric Distribution-Based Readers Scheduling Optimization Algorithm Using Artificial Immune System

An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO

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