RFID Network Planning Research Articles

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

The Radio Frequency IDentification (RFID) technology is widely used in our life and the RFID Network Planning (RNP) problem has gained increasing attentions. Many algorithms have been proposed to solve this problem, such as genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. However, these algorithms need to know the accurate number of readers before the searching process. In some improved algorithms, the reader elimination operator was designed, such as the Tentative Reader Elimination (TRE) operator, which can adjust the number of readers during the searching process. But these operators have difficult in dealing with large scale problems because of the high computational cost. In this paper, we propose an RFID network planning algorithm with an elimination operator based on kinematics, which is named as curling algorithm for RNP (CA-RNP). We consider each reader as a curling which can slide on the working space. Every tag on the working space generates friction to stop readers. Moreover, we design the reader movement operator and the reader collision operator for searching operation from the viewpoint of kinematics. In the experiments, eight RNP instances are used to verify the performance of CA-RNP. The experimental results show that CA-RNP outperforms GA and PSO, and can find good solutions in a short time.

Biomimicry of symbiotic multi-species coevolution for discrete and continuous optimization in RFID networks.

In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS2Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS2O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS2O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm’s performance. Then PS2O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID) system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP) has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI) techniques. This effort has led the author to propose a novel AI algorithm, which has been named “hybrid AI optimization technique,” to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE) and Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS), and results have been compared with Genetic Algorithm (GA) that demonstrates the feasibility of the proposed architecture successfully.

Radio Frequency Identification Network Planning for Indoor Localization Based on Enhanced Artificial Fish Swarm Algorithm

Radio Frequency Identification Network Planning for Indoor Localization Based on Enhanced Artificial Fish Swarm Algorithm

Optimizing radio frequency identification network planning through ring probabilistic logic neurons

Radio frequency identification is a developing technology that has recently been adopted in industrial applications for identification and tracking operations. The radio frequency identification network planning problem deals with many criteria like number and positions of the deployed antennas in the networks, transmitted power of antennas, and coverage of network. All these criteria must satisfy a set of objectives, such as load balance, economic efficiency, and interference, in order to obtain accurate and reliable network planning. Achieving the best solution for radio frequency identification network planning has been an area of great interest for many scientists. This article introduces the Ring Probabilistic Logic Neuron as a time-efficient and accurate algorithm to deal with the radio frequency identification network planning problem. To achieve the best results, redundant antenna elimination algorithm is used in addition to the proposed optimization techniques. The aim of proposed algorithm is to solve the radio frequency identification network planning problem and to design a cost-effective radio frequency identification network by minimizing the number of embedded radio frequency identification antennas in the network, minimizing collision of antennas, and maximizing coverage area of the objects. The proposed solution is compared with the evolutionary algorithms, namely genetic algorithm and particle swarm optimization. The simulation results show that the Ring Probabilistic Logic Neuron algorithm obtains a far more superior solution for radio frequency identification network planning problem when compared to genetic algorithm and particle swarm optimization.

Optimal layout and deployment for RFID system using a novel hybrid artificial bee colony optimizer based on bee life-cycle model

Large-scale radio frequency identification (RFID) network planning (RNP) problem has been proven to be a NP-hard issue, which can be formulated as a high-dimensional nonlinear optimization problem with a mixture of discrete and continuous variables and uncertain parameters. First, a two-level optimization model for RFID network planning based on distributed decision making (DDM) is presented in this paper. In this model, the mixed discrete and continuous planning variables, namely the number, location, and radiate power of RFID readers are optimized. In each level of the optimization model, the different objectives to determine optimal values for these planning variables are as follows: (i) minimization of total installation cost of RFID network in the top-level; (ii) maximization of tag coverage and network reliability, and minimization of reader interference in the lower-level. In order to solve the proposed model effectively, this work proposes an efficient approach for RNP problem, namely the hybrid artificial bee colony optimizer (HABC), which employs the natural life-cycle mechanism to cast the original ABC framework to a cooperative and population varying fashion. In the proposed HABC, individuals can dynamically shift their survival states and population size varies dynamically according to the local fitness landscape during the executions of algorithm. These new characteristics of HABC help to avoid redundant search and maintain diversity of population in complex environments. Experiments are conducted on a set of CEC2005 and discrete benchmarks for evaluating the proposed algorithm. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

A Novel Optimal RFID Network Planning by MC-GPSO

The fast development of RFID technology having challenging issues of the optimal deployment of RFID network are tags coverage, interference, economic efficiency and load balance. In this paper the novel approach of Multi-Colony Global Particle Swarm Optimization (MC-GPSO) algorithm was used to deploy minimum number of reader which covers all tags with minimum interference effect in large scale basis. The main aim of this algorithm is to divide the swarm in to multi-colony for achieving the optimal results as compared to the basic PSO. Simulation results show the optimal solution of RFID Network Planning (RNP).

An improved multi-objective genetic algorithm for heterogeneous coverage RFID network planning

Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions between the RFID devices. Thus, through RFID network planning (RNP) to achieve the desired coverage within the budget becomes a key factor for success. In this study, we establish a novel and generic multi-objective RNP model by simultaneously optimising two conflicted objectives with satisfying the heterogeneous coverage requirements. Then, we design an improved multi-objective genetic algorithm (IMOGA) integrating a divide-and-conquer greedy heuristic algorithm to solve the model. We further construct a number of computational cases abstracted from an automobile mixed-model assembly line to illustrate how the proposed model and algorithm are applied in a real RNP application. The results show that the proposed IMOGA achieves highly competitive solutions compared with Pareto optimal solutions and the solutions given by four recently developed well-known multi-objective evolutionary and swarm-based optimisers (SPEA2, NSGA-II, MOPSO and MOPS2O) in terms of solution quality and computational robustness.

SA-PSO based optimizing reader deployment in large-scale RFID Systems

The RFID technologies are expected to revolutionize various industrial areas and the application of large-scale RFID systems has been a trend. Satisfying a set of imperative constrains and optimizing a set of objectives, the reader deployment is the primary problem for the most challenging RFID network planning (RNP), and needs to be reasonably solved to operate the large-scale RFID systems in an optimal fashion. By taking the coverage, signal interference and load balance as the optimization objectives and deducing the objective ranges, the reader deployment is conducted as a problem of multi-objectives combination optimization. And then, by introducing the Metropolis rule of Simulated Annealing Algorithm, an improved Particle Swarm Algorithm is proposed (SA-PSO) to solve this problem, which can restrict the position change of original and new particles in the iteration process and accelerate the convergence speed of the algorithm. The simulation results show that the addressed SA-PSO algorithm is superior to the compared algorithms in coverage optimization, and the whole system performance can be enhanced by optimizing the signal interference and load balance among the deployed readers.

Hybrid Intelligent Optimization Approach for RFID Network Planning

Radio Frequency Identification (RFID) is a kind of short-range radio technology for data acquisition and automatic identification. In large scale application of RFID system, RFID network planning (RNP) is one of the fundamental tasks need to be fulfilled. This paper proposed an efficient hybrid intelligent optimization approach for RNP. A RNP optimization model is designed in considering of coverage, cost, and the radiated power of readers. A hybrid intelligent optimization approach is proposed to optimize the model in a hierarchical fashion. Firstly, we used the intelligent optimization algorithm to regulate the number and position of the RFID readers for the coverage and cost objective. Then, the optimal radiate power of readers is computed by analytic calculation. Experimental results show that this hybrid intelligent optimization approach has the advantage in terms of the quality of RFID network deployment and computation efficiency comparing with the existing approaches.

Cooperative artificial bee colony algorithm for multi-objective RFID network planning

Radio frequency identification (RFID) is rapidly growing into an important technology for object identification and tracking applications. This gives rise to the most challenging RFID network planning (RNP) problem in the large-scale RFID deployment environment. RNP has been proven to be an NP-hard problem that involves many objectives and constraints. The application of evolutionary and swarm intelligence algorithms for solving multi-objective RNP (MORNP) has gained significant attention in the literature, while these proposed methods always transform multi-objective RNP into single-objective problem by the weighted coefficient approach. In this work, we propose a cooperative multi-objective artificial colony algorithm called CMOABC to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP. The experiment presents an exhaustive comparison of the proposed CMOABC and two successful multi-objective techniques, namely the recently developed multi-objective artificial bee colony algorithm (MOABC) and nondominated sorting genetic algorithm II (NSGA-II), on instances of different nature, namely the two-objective and three-objective MORNP in the large-scale RFID scenario. Simulation results show that CMOABC proves to be superior for planning RFID networks compared to NSGA-II and MOABC in terms of optimization accuracy and computation robustness.

Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches

The development of radio frequency identification (RFID) technology generates the most challenging RFID network planning (RNP) problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA) and swarm intelligence (SI) for solving multiobjective RNP (MORNP) has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC), the nondominated sorting genetic algorithm II (NSGA-II), and the multiobjective particle swarm optimization (MOPSO), on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

Correlation between RFID Network Planning (RNP) Parameters and Particle Swarm Optimization (PSO) Solutions

Particle Swarm Optimization (PSO) algorithm is often used for solving RFID Network Planning (RNP) problems. However, the direct correlation between RNP parameters (coordinates and power settings of RFID readers) and PSO solutions is rarely shown. This is due to the fact that most researches done in this field focus more on the development of new variants of PSO and the optimization result. For that reason, this paper tends to investigate the correlation between RNP parameters and PSO solutions. One of RNP objectives (Optimal Tag Coverage) is taken as an example. The formula of optimal tag coverage is elaborated in order to expose the allocation of RNP parameters in the formula. In addition, a representation system for embedding RNP parameters in PSO solution is explained. This paper can also serves as an early guideline for solving RNP problems using PSO algorithm.

Optimizing RFID Network Planning by Using a Particle Swarm Optimization Algorithm With Redundant Reader Elimination

The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms.

Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning

In order to obtain accurate and reliable network planning in the Radio Frequency Identification (RFID) communication system, the locations of readers and the associated values for each of the reader parameters have to be determined. All these choices must optimize a set of objectives, such as tag coverage, economic efficiency, load balance, and interference level between readers. This paper proposes a novel optimization algorithm, namely the multi-colony bacteria foraging optimization (MC-BFO), to solve complex RFID network planning problem. The main idea of MC-BFO is to extend the single population bacterial foraging algorithm to the interacting multi-colony model by relating the chemotactic behavior of single bacterial cell to the cell-to-cell communication of bacterial community. With this multi-colony cooperative approach, a suitable diversity in the whole bacterial community can be maintained. At the same time, the cell-to-cell communication mechanism significantly speeds up the bacterial community to converge to the global optimum. Then a mathematical model for planning RFID networks is developed based on the proposed MC-BFO. The performance of MC-BFO is compared to both GA and PSO on RFID network planning problem, demonstrating its superiority.