RFID Network Planning Research Articles

An Approach to Hopfield Network-Based Energy-Efficient RFID Network Planning

Abstract Radio Frequency IDentification (RFID) Network Planning (RNP) is the problem of placing RFID readers in a working area where a tag is interrogated by at least one reader and at the same time satisfies some constraints such as minimum number of placed readers, minimal interference, and minimal outside coverage. The RNP optimization has been proven NP-hard; thus, natural-inspired approaches are often used to find the optimal solution. The paper proposes an energy-efficient RNP approach in which the positions of placed readers are optimized by a Hopfield network, and the energy efficiency is achieved through a placement area restriction technique. A mechanism of redundant reader elimination is also added to minimize the number of placed readers. Simulation results show that the Hopfield network-based energy-efficient RNP approach achieves the maximum tag coverage and energy efficiency by reducing interference, outside coverage, and the number of placed readers.

An Optimal Model for Allocation Readers with Grid Cell Size and Arbitrary Workspace Shapes in RFID Network Planning

RFID Network Planning (RNP) is the problem of deploying RFID readers within a workspace so that each tag can be covered by at least one reader. The objective of RNP is to determine the optimal positions of readers while satisfying certain constraints, such as maximum coverage, minimal interference, load balance among readers, etc. However, most previous studies considered the workspace rectangular or square and assumed a fixed number of readers. They then employed some heuristic methods to find the optimal reader positions. This approach is not practical because the workspace can have any shape, and an approach adaptable to the actual shape of the workspace is needed. This paper proposed an improved adaptive model considering the workspace shape, called RNP-3P. The objectives of RNP-3P are to minimize the number of readers, maximize coverage area, minimize interference, and achieve load balance. RNP-3P optimizes the problem in three phases: Phase 1 involves modeling the workspace with grid cell size, Phase 2 determines the objective function, and Phase 3 proposes the iGAPO algorithm to optimize the number and positions of readers within the workspace. Simulation results demonstrate that the proposed model is more effective compared to other heuristic methods.

Radio Frequency Identification Network Planning Using Ascendant Hierarchical Clustering Algorithm

Radio Frequency Identification (RFID) is a suitable technology for several application scenarios. However, it faces many issues that need to be addressed. RFID Network Planning (RNP) is one of these challenges, which is studied in this paper. It consists in defining the optimal placement of RFID readers and their communication range. In this study, the RNP issue is considered as a clustering problem. Therefore, it is solved using Ascendant Hierarchical Clustering algorithm (AHC) such that each cluster head represents a reader. The advantage of our proposed approach is that, in addition to optimizing the RNP, it is capable to determine the optimal number of readers needed to get optimal performances. Simulation results demonstrate that the proposed AHC algorithm performs better than two efficient RNP algorithms from the literature: Particle Swarm Optimization (PSO) and Leaders and Followers (LaF) algorithms. Hence, it is observed that AHC algorithm is more efficient, scalable and runs faster than PSO and LaF.

RFID Network Planning of Smart Factory Based on Swarm Intelligent Optimization Algorithm: A Review

RFID Network Planning of Smart Factory Based on Swarm Intelligent Optimization Algorithm: A Review

Improved multi-objective brain storm optimization algorithm for RFID network planning

Improved multi-objective brain storm optimization algorithm for RFID network planning

One decade of radio frequency identification (RFID) Network Planning (RFID-NP)

The rapid expansion of industrial technology necessitates the widespread use of radio frequency identification networks (RFID). Multiple (RFID) components must be installed in order to do this (readers, tags, computer systems, and so on). Networks are growing more expensive and sophisticated as a result of the requirement to add more readers. To supply strong service for (RFID) systems, figuring out the optimal quantity, position, and reader settings is a considerable task. Good planning ensures that (RFID) networks have (the coverage, load balancing, and reader overlapping they need). This problem is well-studied in literary works as a radio frequency identification network planning problem. In this paper, a number of algorithms that were used to improve the RFID Network Planning (RNP) model.

RFID network planning using a new hybrid ANNs-based approach

Radio frequency identification (RFID) is a wireless technology that uses radio waves to automatically identify and track objects within a certain range. Due to the benefits of the technology, RFID technology has been adopted as a reliable and efficient solution and is an important technology used in various IoT applications. The deployment of the RFID system has caused an RFID network planning (RNP) problem. However, the RNP problem can be defined as an NP-hard combinatorial optimisation problem that requires the examination of a large number of combinations to obtain the best solution. The goal of this research is to introduce and describe a new hybrid approach to solve the problem of RNP optimisation that is based on artificial intelligence algorithms that require a minimum number of initial parameters. Based on widely used benchmarks, our approach is compared against a hybrid approach based on the genetic algorithm, and the results are efficient, cost-effective and assure a good deployment of the RFID system.

Mayfly Sparrow Search Hybrid Algorithm for RFID Network Planning

The radio frequency identification (RFID) technology is frequently used for large-scale RFID network planning, which brings thought-provoking RFID network planning (RNP) problems such as the uncertain number of readers, high interference rate of readers, poor economic benefits, and weak robustness. In this paper, we propose a mayfly sparrow search hybrid algorithm with Circle chaotic map and Lévy flight, which is named MASSA. MASSA maps out the position of the readers by adding the idea of the mayfly algorithm (MA) into the optimization process of the sparrow search algorithm (SSA). Circle chaos mapping and Lévy flight are added in the SSA optimization process and the nonlinear inertia coefficients to balance the relationship between global search and local search. The MASSA is compared with SSA, Improved sparrow search algorithm (ISSA), MA, particle swarm optimization algorithm (PSO), and gray wolf algorithm (GWO). The first part of the experiment uses the CEC2017 test suite to evaluate algorithm performance and analysis strategy effectiveness. The results encourage the application of MASSA to solve RNP problems. In the second part, experiments were conducted with four objective functions of coverage, interference, power, and load balance. Two different experiments were carried out with fixed number of readers and flexible number of readers under random label conditions. Experimental results show that MASSA has good planning performance, better economy, and robustness.

Swarm intelligence RFID network planning using multi-antenna readers for asset tracking in hospital environments

Radio frequency identification (RFID) systems have been widely used for the inventory management and asset tracking in hospital industry. Although the total cost of an RFID system relies mostly on the number of readers, existing RFID network planning (RNP) techniques focused on single-port readers. This paper introduces a swarm intelligence RFID network planning model with multi-antenna readers (named RNP-MAR) to efficiently track medical assets in hospital environments. Our motivation is to utilize multi-port RFID readers, in order to minimize the total cost of the RFID network, and simultaneously maximizing the network coverage. To achieve this purpose, we consider three reader types with one, two, and four antenna ports. Moreover, different antenna models with different coverage characteristics are utilized to be allocated on the different ports of RFID readers. The aim of the RNP-MAR is not only to determine the optimal placements of the RFID readers in the hospital plan, but also to design the multi-antenna readers and their antennas. To efficiently solve the RNP-MAR model, a combined global-local linear-nonlinear metaheuristic based on whale optimization algorithm and simulated annealing (named WOA-SA) is presented. The multi-objective function of the WOA-SA is to maximize the network coverage, while minimizing the total cost, interference, collision, and power consumption. Simulation results show that the proposed method provides 39.57% saving (on average) in the total cost of the RFID network through efficiently utilizing multi-antenna RFID readers.

Optimal Deployment of Phased Array Antennas for RFID Network Planning Based on an Improved Chicken Swarm Optimization

Effective network planning improves performance in the radio-frequency identification (RFID) system. This article proposes an optimal deployment of phased array reader antennas for RFID network planning (RNP). For practical considerations, the RNP problem is analyzed and formulated based on a multipath propagation model, where each reader is equipped with a phased array antenna. The gains and radiation directions of the antennas are adjusted by voltage states instead of gestures, reducing the cumbersome antennas redeployment. An indicator called amplitude fluctuation under narrowband (AFN) is proposed to reflect the disturbance of frequency selective fading caused by the multipath effect. To effectively address the RNP problem, an improved chicken swarm optimization algorithm with two targeted strategies is developed. Simulation and experiment comparisons with the existing algorithms demonstrate the superiority of the proposed approach.

A hybrid particle swarm optimization algorithm for RFID network planning

The radio frequency identification (RFID) technology is widely used for object identification and tracking applications, which brings the most challenging RFID network planning (RNP) problem. However, existing RNP methods have some defects, such as the number of readers is uncertain and objectives conflict each other. In this paper, we propose a hybrid particle swarm optimization algorithm with K-means clustering and virtual forces for RNP, which is named as HPSO-RNP. HPSO-RNP can search the number of readers automatically and initialize the coordinates of readers through the K-means algorithms. Virtual force is integrated into the random movement to adjust the location of readers during the search process of PSO. Moreover, we consider four objective functions in a hierarchical manner. To compare HPSO-RNP with the existing method, extensive experiments are conducted on eight RNP benchmark datasets and the results validate that the performance of the proposed method is superior for planning RFID networks in terms of the number of readers, interference, power and load balance.

Robust multi-objective optimization for solving the RFID network planning problem

Radio-frequency identification (RFID) is a new technology used for identifying and tracking objects or people by radio-frequency waves to facilitate automated traceability and data collection. The RFID system consists of an electronic tag attached to an object, readers, and a middleware. In the latest real applications based on the RFID technology, the deployment of readers has become a central issue for RFID network planning by means of optimizing several objectives such as the coverage of tags, the number of readers, and the readers/tags interferences. In practice, the system is affected by uncertainty and uncontrollable environmental parameters. Therefore, the optimal solutions to the RFID network planning problem can be significantly reduced with uncontrollable variations in some parameters, such as the reader's transmitted power. In this work, we propose a robust multi-objective optimization approach to solve the deployment of RFID readers. In this way, we achieve robust optimal solutions that are insensitive to uncertainties in the optimization parameters.

Passive UHF RFID Network Planning for Accurate 3-D Location via Restricted Genetic Algorithm

Indoor localization via radio-frequency identification (RFID) carries critical importance due to its high accuracy and low hardware requirement. The positions of reader antennas can affect the positioning accuracy and coverage in RFID network. In this letter, we present a novel RFID network planning (RNP) approach to optimize the deployment of reader antennas for accurate 3-D location. First, the 3-D antenna radiation mode of a passive UHF RFID system is set up. Then, the received signal strength (RSS) and path loss characteristics (PLC) are analyzed and a restricted genetic algorithm (RGA) is developed to obtain the optimal solution of the RNP via maximizing the total reward function with constraints. Finally, the convolutional neural network (CNN) and weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (WKNN) algorithms are respectively used to verify the localization effect. For comparison, the Cramér-Rao lower bound (CRLB) is also derived. The experimental results show that the proposed approach can improve the positioning accuracy as well as the coverage, and enhance the anti-noise capability of the location system.

A new algorithm based CSP framework for RFID network planning

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 $$PS^2O$$ , GPSO and VNPSO–RNP.

A self learned invasive weed-mixed biogeography based optimization algorithm for RFID network planning

The optimal placement of the RFID readers inaugurates an ongoing research field, namely the RFID network planning (RNP). The main issue in the RNP is to know how many readers have to be used and what is their best distribution that guarantees fulfillment of multiple objectives. The common RNP objectives are the optimal coverage, readers’ interference avoidance, redundant reader elimination, load balance among deployed readers and minimum power losses, which are considered as conflicting objectives that leads the RNP to be an NP-hard problem need to be solved. The contributions in this paper are: firstly, utilizing both the Biogeography based optimization (BBO) and the Hybrid Invasive Weed-Biogeography based optimization (HIW-BBO) as new algorithms have not used before for solving the RNP. Secondly, we proposed a Self Learning (SL) strategy with a mixed BBO Migration (MBBOM) operation to modify the HIW-BBO algorithm in an algorithm called Self Learned Invasive Weed-Mixed Biogeography based optimization (SLIWMBBO). Thirdly, the performance of the proposed SLIWMBBO algorithm is compared to both the HIW-BBO and the Self Adaptive Cuckoo Search (SACS) optimization algorithms according to a set of 13 benchmark functions. The results of this comparison encourage the application of the SLIWMBBO as an optimization algorithm for solving the complex problems. Lastly, the BBO, HIW-BBO and SLIWMBBO optimization algorithms are used for solving three complex RNP instances and compared to the SACS algorithm. Simulation results of the SLIWMBBO are outstanding and demonstrate its superiority over the compared algorithms.

Analysis of gradient-based cuckoo search for the large scale optimal rfid network planning

Nature inspired algorithms provide an efficient method to resolve the problems of RFID Network Planning optimization that are not possible with the conventional methods. The gradient of the RFID Network Planning objective function was recently used to improve the precision of global optimal solutions. Therefore, this work presents a comparative study between the Gradient-Based Cuckoo Search (GBCS) and state-of the-art algorithms in a large, complex, and dynamic RNP network. The outcome of this study analyzed the algorithms performance in terms of tag coverage, required number of readers, and interference between reader propagation areas. The present method specifies the combinatorial performance of the reader’s propagation area based on the evaluation of the tag density and location by using the Gradient-Based to manage the input representation of the Cuckoo Search. The results observed high local information for the objective function, which facilitated the choice of complex RFID Network Planning parameters that enabled the algorithm to work with big data in large scale conditions.

Development of a sparse RFID reader deployment algorithm for effective RFID network planning

A well planned Radio Frequency Identification (RFID) network achieves the following twin objectives, (a) reduces the cost of acquiring the readers to be deployed (b) completes coverage of tags deployed in the network. A reader deployment approach that considered the physical topology of passive tags that were sparsely deployed in a RFID network was developed. In the implemented technique, readers could not be deployed outside the work area which implied that tags located outside the work area could not be detected. Bidirectional communication between the readers and tags could only be established if their receiver sensitivity was less than or equal to the incoming signal. The developed algorithm was optimized by logically integrating some sub-algorithms such as Useless Reader Elimination Algorithm (UREA) and Redundant Reader Elimination Algorithm (RREA). The effectiveness of the developed Sparse Reader Deployment Algorithm (SRDA) was demonstrated through simulation of various scenarios. The implemented approach was compared to the Constrained Reader Deployment Approach (CORDA) and the former outperformed the latter in terms of Optimal Power Dissipated (OPD) which was 255mW. The impact of increasing the number of tags and coverage area on the OPD of SRDA was investigated in a bid to ascertain the robustness of the developed algorithm. Keywords : RFID; CORDA; SRDA; OPD.

Robust modeling and planning of radio-frequency identification network in logistics under uncertainties

To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a robust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage is established by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference is calculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. In robust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forward to improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploitation speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; the exploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size. Simulation results show that, compared with the other three methods, the planning solution obtained by this work is more conducive to enhance the coverage rate and reduce interference and cost.

Indicator-based multi-objective adaptive bacterial foraging algorithm for RFID network planning

This work develops a novel indicator-based multi-objective bacterial colony foraging algorithm (I-MOBCA) for complex multi-objective or many-objective optimization problems. The main idea of I-MOBCA is to develop an adaptive and cooperative model by combining bacterial foraging, adaptive searching, cell-to-cell communication and preference indicator-based measure strategies. In this algorithm, each bacterium can adopt its run-length unit to appropriately balance exploitation and exploration states, and the quality of position or solution is calculated on the basis of the binary quality indicator to determine the Pareto dominance relation. Our algorithm uses Pareto concept and preference indicator-based measure to determine the non-dominated solutions in each generation, which can essentially reduce the computation complexity. With several mathematical benchmark functions, I-MOBCA is proved to have significantly better performance over compared algorithms for solving some complex multi-objective optimization problems. Then the proposed I-MOBCA is used to solve three-objective RFID network planning problem. Simulation results show that I-MOBCA proves to be superior for planning RFID networks than compared algorithms in terms of optimization accuracy and computation robustness.

Multi-Objective Decision Aid Application for RFID Network Planning

The Radio Frequency Identification system (RFID) is a generic term for technologies of a type of automatic identification system that use radio-frequency waves to automatically identify people or objects. Multiple RFID readers are required to detect and monitor the tags in the network area due to their limited interrogation range. Therefore, the cost of the RFID System, its efficiency in the communication process, and the number of tags covered directly depend on the number of deployed readers. In this work, we present a new many/multi-objective model with the aim to achieve the optimal solutions of the NP-hard problem of RFID Network Planning (RNP). The model involves many objectives and constraints as determining the minimum number of readers, finding their optimal 3D deployment and localization, guarantying a full coverage of all the tags distributed in the area, and minimizing the energy and the interferences caused by an overlap in the readers signals and the tags responds at the same time. Moreover, we provide a new decision aid application with the aim to access, manage, solve, and optimize the RNP problem involving the needs of researchers and decision makers.