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

2015 ◽  
Vol 30 ◽  
pp. 249-264 ◽  
Author(s):  
Zhonghua Li ◽  
Jianming Li ◽  
Chunhui He ◽  
Chengpei Tang ◽  
Jieying Zhou
Keyword(s):  
Collision Avoidance ◽  
Network Optimization ◽  
Immune Network ◽  
Artificial Immune ◽  
Rfid Reader ◽  
Artificial Immune Network

scholarly journals An Effective Scheduling-Based RFID Reader Collision Avoidance Model and Its Resource Allocation via Artificial Immune Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Shanjin Wang ◽  
Zhonghua Li ◽  
Chunhui He ◽  
Jianming Li
Keyword(s):  
Resource Allocation ◽  
Collision Avoidance ◽  
Radio Frequency Identification ◽  
Immune Network ◽  
Artificial Immune ◽  
Tag Identification ◽  
Coverage Area ◽  
Identification Rate ◽  
Rfid Reader ◽  
Artificial Immune Network

Radio frequency identification, that is, RFID, is one of important technologies in Internet of Things. Reader collision does impair the tag identification efficiency of an RFID system. Many developed methods, for example, the scheduling-based series, that are used to avoid RFID reader collision, have been developed. For scheduling-based methods, communication resources, that is, time slots, channels, and power, are optimally assigned to readers. In this case, reader collision avoidance is equivalent to an optimization problem related to resource allocation. However, the existing methods neglect the overlap between the interrogation regions of readers, which reduces the tag identification rate (TIR). To resolve this shortage, this paper attempts to build a reader-to-reader collision avoidance model considering the interrogation region overlaps (R2RCAM-IRO). In addition, an artificial immune network for resource allocation (RA-IRO-aiNet) is designed to optimize the proposed model. For comparison, some comparative numerical simulations are arranged. The simulation results show that the proposed R2RCAM-IRO is an effective model where TIR is improved significantly. And especially in the application of reader-to-reader collision avoidance, the proposed RA-IRO-aiNet outperforms GA, opt-aiNet, and PSO in the total coverage area of readers.

scholarly journals GM-NAINO: Global Mutation based Novel Artificial Immune Network Optimization for Enhancing Data Security in Public Cloud Storage System

2021 ◽  
Author(s):  
Priyadharshini Kaliyamoorthy ◽  
Aroul Canessane Ramalingam
Keyword(s):  
Network Optimization ◽  
Cloud Storage ◽  
Storage System ◽  
Data Integrity ◽  
Cloud Services ◽  
Immune Network ◽  
Public Cloud ◽  
Artificial Immune ◽  
Artificial Immune Network ◽  
Secure Data

Abstract In recent years, numerous research works have been established to obtain secure data in the cloud storage system. But the data privacy regarding information outsourcing on cloud services is considered a crucial problem. In order to provide secure data, it is necessary to encrypt the information before storing it in the public cloud storage system. To provide security and data integrity during encryption and decryption, this paper proposes a global mutation-based novel artificial immune network optimization algorithm for RSA cryptosystem. Here, the Global Mutation Based Novel Artificial Immune Network Optimization (GM-NAINO) Algorithm is employed to attain optimal generation of keys thereby enhancing safe and secure data transmission and improving the data integrity during the transmission of data. Thus, the proposed GM-NAINO based RSA framework provides an effective system in improving data integrity. In addition to this, to determine the effectiveness of the proposed GM-NAINO algorithm seven benchmark functions are utilized in this paper. The performance evaluation and the comparative analysis are carried out and the proposed GM-NAINO based RSA framework outperforms other approaches.

Identification of cavitation in centrifugal pump by artificial immune network

2021 ◽  
pp. 095440892110284
Author(s):  
Seyed M Matloobi ◽  
Mohammad Riahi
Keyword(s):  
Immune System ◽  
Production Systems ◽  
Principal Component ◽  
Immune Network ◽  
Centrifugal Pumps ◽  
Artificial Immune ◽  
Human Immune System ◽  
Time Frequency ◽  
Artificial Immune Network ◽  
Damage Condition

Reducing the cost of unscheduled shutdowns and enhancing the reliability of production systems is an important goal for various industries; this could be achieved by condition monitoring and artificial intelligence. Cavitation is a common undesired phenomenon in centrifugal pumps, which causes damage and its detection in the preliminary stage is very important. In this paper, cavitation is identified by use of vibration and current signal and artificial immune network that is modeled on the base of the human immune system. For this purpose, first data collection were done by a laboratory setup in health and five stages damage condition; then various features in time, frequency, and time–frequency were extracted from vibration and current signals in addition to pressure and flow rate; next feature selection and dimensions reduction were done by artificial immune method to use for classification; finally, they were used by artificial immune network and some other methods to identify the system condition and classification. The results of this study showed that this method is more accurate in the detection of cavitation in the initial stage compared to methods such as non-linear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means with the same data. Also, selected features with artificial immune system were better than principal component analysis results.

scholarly journals An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Mengling Zhao ◽  
Hongwei Liu
Keyword(s):  
Association Rules ◽  
Large Scale ◽  
Classification Algorithm ◽  
Data Sets ◽  
Immune Network ◽  
Artificial Immune ◽  
Mining Algorithm ◽  
Sar Image Segmentation ◽  
Artificial Immune Network ◽  
Adaptive Pso

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

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