An efficient and fast kinematics-based algorithm for RFID network planning

Zhang, Tao; Liu, Jing

doi:10.1016/j.comnet.2017.04.035

Cited by 12 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Zhang and Liu (2017), a Curling Algorithm for RNP (CA-RNP) was designed to solve the RNP problem. CA-RNP would implement the reader-movement and reader-collision operators to determine the optimal number of deployed readers and the best position of readers in the monitoring area.…”

Section: Related Workmentioning

confidence: 99%

A new algorithm based CSP framework for RFID network planning

Jaballah

Meddeb

2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

The huge growth of industrial society requires the deployment of radio frequency identification networks on a large scale. This necessitates the installation of a large number of radio frequency identification components (readers, tags, middleware and others). As a consequence, the cost and complexity of networks are increasing due to the large number of readers to be installed. Finding the optimal number, placement and parameters of readers to provide a high quality of service for radio frequency identification systems is a critical problem. A good planning affords a basic need for radio frequency identification networks, such as coverage, load balance and interference between readers. This problem is famous in the literature as a radio frequency identification network planning problem. All the proposed approaches in the literature have been based on meta-heuristics. In this paper, we design a new algorithm, called the RNP-CSP algorithm based on the constraint satisfaction problem framework to solve the radio frequency identification network planning problem. The performance evaluation shows that the RNP-CSP algorithm is more efficient than P S 2 O, GPSO and VNPSO-RNP.

show abstract

Section: Related Workmentioning

confidence: 99%

A new algorithm based CSP framework for RFID network planning

Jaballah

Meddeb

2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…Tang et al [9] combined the actual situation of the mixed flow assembly line, summarized the requirements including fence coverage [10], full coverage, and distinguished coverage [11], and introduced fuzzy interference value [12] to measure the coverage capability of reader antenna and established a multiobjective nonlinear integer planning model in the twodimensional plane to optimize the layout of the reader antenna with the objectives of cost and coverage performance. Most of the current research about RFID reader antenna deployment mainly simplifies the complex environment into a two-dimensional or three-dimensional environment without obstacles [13][14][15] and applies genetic algorithm (GA), such as particle swarm algorithms, and adaptive bacterial foraging algorithm, etc., to optimize the deployment of spatial RFID reader antennas.…”

Section: Introductionmentioning

confidence: 99%

Optimization Method of RFID Reader Antenna Deployment in Obstacle Environment Based on Improved FA

Hong

Jiang

Wang

2022

International Journal of Antennas and Propagation

View full text Add to dashboard Cite

The aim of this study is to solve the problem of RFID reader antenna deployment when an obstacle in the environment or the material itself is an obstacle. The article establishes a new reader antenna constraint model based on the rectangular obstacle RFID antenna deployment optimization environment model and the reader antenna sensing model containing Gaussian distribution noise probability and applies the improved firefly algorithm to find the optimum with coverage rate, interference degree, and load balance function as multiobjective functions. First, to obtain a uniform traversal of the target space, and a cube mapping is applied for chaos initialization. Second, a weighting model is proposed to control the speed of firefly variation and combine the variable step strategy of elite individuals to expand the movement step of elite individuals and reduce the movement step of nonelite individuals to improve population diversity. Finally, to improve the algorithm’s continuous optimality-seeking ability, a Gaussian variation operation strategy based on elite fireflies is introduced to ensure the algorithm’s optimality-seeking throughout the process.

show abstract