Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system

Kuo, R. J.; Hung, Shih-Hsuan; Cheng, W. T. K.

doi:10.1016/j.ins.2013.10.035

Cited by 31 publications

(13 citation statements)

References 29 publications

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“…In [24], they are indicating that the proposed system performs better than other algorithms. In addition, the evaluation results produced in [25] shows that it can predict the picking cart's position more accurately. The authors of [26] introduced a new sub-pixel mapping strategy based on the immune system, and is used for the sub-pixel mapping in remote sensing imagery system.…”

Section: The Artificial Immune Systemmentioning

confidence: 99%

“…The proposed work has a unique feature of classifier for embedded property with local feature selection. In [25], the authors proposed an optimization technique for the RIFD-based position system and they produced several results for the three-benchmark functions. In [24], they are indicating that the proposed system performs better than other algorithms.…”

Section: The Artificial Immune Systemmentioning

confidence: 99%

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Artificial immune system (AIS)-based location management scheme in mobile cellular networks

Biswash

Sarkar

Sharma³

2018

Iran J Comput Sci

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In this paper, we are proposing a bio-inspired location management (LM) technique for personal communication system (PSC). It is based on artificial immune system (AIS), with self-adaptation and self-update attributes to perform the location management, and helps to achieve better quality of service (QoS) and quality of experience (QoE) for the mobile users. Here, we are suggesting a modified mobile switching center (MSC) architecture, and an adaptive self-modified location management procedure. The proposed mobile switching center architecture has an advantage of rule-based and fact-based system to store the rules and facts related to location management procedure, and it shows the intelligent behavior of system. The mobile switching center calculates the best method for location management and the rule-based system trigged the rules to perform the techniques. The system stores the result (techniques for location management) in fact-based system for future use. The efficiency and effectiveness of the proposed techniques have been analyzed, and it observed that the proposed system has 45-50% improvement in performance over the current location management techniques. Here, we are using performance parameters such as signaling cost, database update cost, overhead measurement, and mobility management cost.

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Section: The Artificial Immune Systemmentioning

confidence: 99%

Section: The Artificial Immune Systemmentioning

confidence: 99%

Artificial immune system (AIS)-based location management scheme in mobile cellular networks

Biswash

Sarkar

Sharma³

2018

Iran J Comput Sci

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“…Meanwhile, because of the threshold setting of VIRE algorithm also can lead to errors while solving the neighbor label [9] . So this paper presents an improved VIRE positioning algorithm.…”

Section: Improved Vibe Algorithmmentioning

confidence: 99%

Application and Research of Active RFID-Based Positioning System in Sport Competition

Jing¹

2016

IJFGCN

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In this paper, it has made a deep research to the application of RFID positioning in sports competitions. It also researched the application of VIRE algorithm in positioning system, and proposed an improved VIRE algorithm, also chose the appropriate interpolation method. Through the improved location algorithm, it builds a relationship between the distance and time in finish line of the athlete, and solves the function to make sure the time that athletes reach the finish line, so that it can achieve the purpose of timing.

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“…The multi-layer perceptron (MLP) with back-propagation learning is the most popular and commonly used neural network structure due to its simplicity, effectiveness and excellent performance in many applications that require to learn complex patterns [60,61]. Multi Layer perceptron (MLP) is a feedforward neural network with one or more hidden layers between input and output layer.…”

Section: Mlp Neural Networkmentioning

confidence: 99%

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Karami

2015

Journal of Network and Computer Applications

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Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NDN (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase -adaptive trainingwe propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance-we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and highutilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics.

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Application of an optimization artificial immune network and particle swarm optimization-based fuzzy neural network to an RFID-based positioning system

Cited by 31 publications

References 29 publications

Artificial immune system (AIS)-based location management scheme in mobile cellular networks

Artificial immune system (AIS)-based location management scheme in mobile cellular networks

Application and Research of Active RFID-Based Positioning System in Sport Competition

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

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