Radio Frequency Identification Network Research Articles

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.

Evaluation Software and IT Integration for IoT-based Healthcare Radio Frequency Identification Network Planning

For Internet of Things (IoT)-based on healthcare systems to autonomously monitor patients, radio frequency identification, or RFID, its essential. It is difficult to guarantee complete coverage throughout sizable healthcare facilities with a small number of RFID readers, though Software for RFID network planning must be optimized. The purpose of this paper is about optimizing related software and suggest a topological RFID network planning strategy that will minimize reader interference while deploying the fewest possible readers. The best location for RFID tags on patients as well as readers depends on the layout of the institution and how the patients move. To dependably scan tags across a variety of locations, RFID network design software precisely calculates the number and positions of readers using algorithms. Software features and network planning goals are developed to efficiently track patient status by automating the gathering of medical data. in this paper to find the optimal number of RFID readers required and their location in the system. After the algorithm was tested, it was found that the algorithm can determine the true effectiveness of the coverage and reduce the area of interference between the areas of coverage of RFID readers. PSO is a superior algorithm for solving difficult problems (NP). The PSO algorithm has shown a high efficiency in finding the optimal solution, with some of weakness in the performance of the algorithm, represented in finding functional boundaries that serve the research problem. By providing constant access to health information, this plan raises the standard of care.

Study of Security Threats, Authentication Protocols and RFID Networks

In this paper, we studied about security threats, authentication protocols and RFID networks. Wireless sensor networks (WSN) and radio frequency identification (RFID) are two important wireless technologies that have a wide variety of applications and provide limitless future potentials. RFID facilitates detection and identification of objects that are not easily detectable or distinguishable by using current sensor technologies.

A Review and Testing of Fault Tolerance Levels of Anti-Poaching Cybersecurity System

The development of anti-poaching networks and systems has created a new environment for animals in game reserves all over the world. Advanced technologies such as heat sensors, drones, and trip wires help prevent poachers from entering the property and therefore, creating a safer environment for animals to roam freely. Radio frequency identification (RFID) systems are used to track the location of animals. These networks are, however, susceptible to being hacked if not properly protected with cybersecurity tools, resulting in cyber-criminals gaining access into the network. Many attacks or threats can be executed on the RFID network due to some exposed vulnerabilities of elements within the anti-poaching network. The purpose of this paper is to explore the empirical methods of common attacks, used by cyber-criminals, to attack the anti-poaching network, and whether these methods used are effective in identifying weaknesses within the network. This will be executed by creating an experimental structure of the anti-poaching system with a specialised focus on the RFID elements, using quantitative research methods to produce findings. GNS3, an open-source software application that has specifically been chosen to conduct this research, is used to build the network simulation in order to analyse the weaknesses of the network. Cybersecurity protocols are implemented to protect the network and aim to protect the animals. The attacks performed, such as Flood and Scapy attacks, have shown that the anti-poaching network is vulnerable to penetration from cyber-criminals. A hypothesis test was conducted to determine whether the attacks had a significant effect on the network, by using the average ping time from specific nodes to Google. It was found that the average ping time increased by 2.0020 units, therefore stating that the elements of the network were successfully attacked. The fault tolerance test shows that the availability of the anti-poaching network is roughly 90 percent which concludes that the network is configured to deliver quality performance and handle failures, should there be any intervention. This will allow game reserves to implement and have information on a better and safer RFID system for the animals.

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.

Optimization of the RFID network in IoT through Hybrid Genetic Algorithmz

Several cutting-edge technologies, like as Radio Frequency Identification (RFID) optimization and sensors, are enabling the Internet of Things (IoT). Using a unique code connected with tags, this radio frequency identification technology allows the sensor to read from a distance without requiring sight contact. Optimal tag coverage, cost efficiency, and quality of service needs are all important considerations when deploying RFID networks. We investigated optimization approaches for RFID in Internet of Things (IoT) devices in this paper. RFID optimization saves processing time, hardware costs, and improves efficiency. Random search strategies based on Artificial Intelligence (AI) are more suited than deterministic methods that are difficult to solve in polynomial time. In this paper, we present a hybrid algorithm that combines PSO and GA features to improve the efficiency of an IoT network system.

Health Monitoring of Conveyor Belt Using UHF RFID and Multi-Class Neural Networks

Conveyor belts in mining sites are prone to cracks, which leads to dramatic degradation of overall system performance and the breakdown of operation. Crack detection using radio frequency identification (RFID) sensing technology is recently proposed to provide robust and low-cost health monitoring systems for conveyor belts. The intelligent machine learning (ML) technique is one of the most promising solutions for crack detection and successful implementation within the IoT paradigm. This paper presents a conveyor belt structural health monitoring (SHM) model using ML and Internet of Things (IoT) connectivity. The model is extensively tested, and the classification is conducted based on simulated data obtained from an Ultra High Frequency (UHF) RFID sensor. Here, the sensor is laid on a belt, and the data are obtained at different crack orientations of vertical, horizontal, and diagonal cracks, for varying crack widths of 0.5 to 5 mm at 10 different locations on the sensor. The ML model is tested with different input features and training algorithms, and their performances are compared and analysed to identify the superior input feature and training algorithm. This method produces high accuracy in determining crack width, orientation, and location. The findings show that the proposed detection system based on ML modelling could detect cracks with 100% accuracy. The proposed system can also distinguish between vertical, horizontal, and diagonal cracks with an accuracy of 83.9%, and has a significant identification rate of 84.4% accuracy for detecting crack-width as narrow as 0.5 mm. Moreover, the model can predict the region of the crack with an accuracy of 95.5%. Overall, the results show that the proposed model is very robust and can perform SHM of conveyor belts with high accuracy for a range of parameters and classification scenarios. The method has huge industrial significance in coal mines.

Improving Efficiency of Large RFID Networks Using a Clustered Method: A Comparative Analysis

Radio Frequency Identification (RFID) is primarily used to resolve the problems of taking care of the majority of nodes perceived and tracking tags related to the items. Utilizing contactless radio frequency identification data can be communicated distantly using electromagnetic fields. In this paper, the comparison and analysis made between the Clustered RFID with existing protocols Ad hoc On-demand Multicast Distance Vector Secure Adjacent Position Trust Verification (AOMDV_SAPTV) and Optimal Distance-Based Clustering (ODBC) protocols based on the network attributes of accuracy, vulnerability and success rate, delay and throughput while handling the huge nodes of communication. In the RFID Network, the clustering mechanism was implemented to enhance the performance of the network when scaling nodes. Multicast routing was used to handle the large number of nodes involved in the transmission of particular network communication. While scaling up the network, existing methods may be compromised with their efficiency. However, the Clustered RFID method will give better performance without compromising efficiency. Here, Clustered RFID gives 93% performance, AOMDV_SAPTV can achieve 79%, and ODBC can reach 85% of performance. Clustered RFID gives 14% better performance than AOMDV_SAPTV and 8% better performance than ODBC for handling a huge range of nodes.

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.

DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things

Radio Frequency Identification (RFID) is considered as one of the most widely used wireless identification technologies in the Internet of Things. Many application areas require a dense RFID network for efficient deployment and coverage, which causes interference between RFID tags and readers, and reduces the performance of the RFID system. Therefore, communication resource management is required to avoid such problems. In this paper, we propose an anti-collision protocol based on feed-forward Artificial Neural Network methodology for distributed learning between RFID readers to predict collisions and ensure efficient resource allocation (DMLAR) by considering the mobility of tags and readers. The evaluation of our anti-collision protocol is performed for different mobility scenarios in healthcare where the collected data are critical and must respect the terms of throughput, delay, overload, integrity and energy. The dataset created and distributed by the readers allows an efficient learning process and, therefore, a high collision detection to increase throughput and minimize data loss. In the application phase, the readers do not need to exchange control packets with each other to control the resource allocation, which avoids network overload and communication delay. Simulation results show the robustness and effectiveness of the anti-collision protocol by the number of readers and resources used. The model used allows a large number of readers to use the most suitable frequency and time resources for simultaneous and successful tag interrogation.

A Review Paper on “IoT” and It’s Smart Applications

The Web of Things is ushering in a new era of registering innovation (IoT). IOT is a cloud-based "all-encompassing worldwide neural system" that connects various devices. The Internet of Things (IoT) is a collection of cleverly connected devices and frameworks that include brilliant machines cooperating and communicating with other machines, conditions, items, and foundations, and Radio Frequency Identification (RFID) and sensor network innovations will rise to meet this new challenge. As a result, a vast amount of data is being generated, stored, and processed into useful activities that can "summon and control" the things that will make our lives far less demanding and secure— as well as reduce our impact on the environment. Every organisation, such as corporations and public institutions, requires cutting-edge data about individuals. Most foundations use websites, bulletins, or notice sheets in this way. However, in a huge number of countries, web access is available to people on computers and mobile phones, making data exchange much easier and less expensive.

Renovated XTEA Encoder Architecture-Based Lightweight Mutual Authentication Protocol for RFID and Green Wireless Sensor Network Applications

Wireless sensor networks find applications everywhere in day to day activities right from attendance entry systems to healthcare monitoring systems. The evolution of the Internet of Things (IoT) as the Internet of Everything (IoET) makes the wireless sensor network omnipresent and increases the use of Radio Frequency Identification (RFID) for the proper identification of devices and sensor nodes which are mostly battery operated. As technology evolves, security threats also increase rapidly. This mandates a strong and energy-efficient green solution. This work attempted to address these issues by effectively deploying the lightweight encryption scheme called Extended Tiny Encryption Algorithm (XTEA). Though the XTEA is lightweight and famous, it is commonly known for various attacks. Our work patches the security threats in the XTEA by applying domain-specific customization, random number utilization, and undisclosed key renewal techniques. Two custom Renovated XTEA Mutual Authentication Protocol (RXMAP) encoder architectures, namely, RXMAP-1 and RXMAP-2, are proposed based on the replacement of accurate computational blocks with approximate blocks. The proposed RXMAP protocol is evaluated for its computational and storage overhead and verified against various security threats using BAN logic formal verification and informal verification. The proposed encoder architectures are simulated for functional verification, and ASIC implementation is done with a 132 nm process node. ASIC implementation results show that the proposed designs RXMAP-1 and RXMAP-2 occupy 53.11% and 53.31% lesser area compared to XTEA I and 52.97% and 53.18% lesser area compared to XTEA II implementation. The total power consumed by the proposed encoder architectures RXMAP-1 and RXMAP-2 is 68.76% and 71.64% lesser than XTEA II implementation, respectively, while maintaining the equal throughput.

Hybrid GA –SA Multiobjective Optimization and Simulation for RFID Network Planning Problem

Optimized asset tracking with radio frequency identification (RFID) as a complicated innovation that requires much money to be implemented has become more popular in the healthcare industry. Considering the use of more antennas in each reader, we present a modern heuristic methods, hybrid of genetic algorithms (GA) and simulated annealing (SA) for the purpose of placing readers in an emergency department of a hospital with an RFID network. In this study, a multi-objective function is developed for the network coverage maximization and the minimization of total cost, tag reader collision, interference, energy consumption, and path loss in a simultaneous way. The proposed algorithm provides savings (on average) in the total cost of the RFID network through the efficient use of three types of readers with one, two and four antenna ports. Additionally, by testing three scenarios, the effect of algorithms in achieving the optimal solution is indicated by the simulated results. Besides, the results of GA-SA is compared to the results of GA and other existing models in the relevant literature. It is shown that its main advantage is the use of multi-antenna RFID readers, which reduces the total cost of the RFID network and also increase network coverage with fewer readers and antennas. In other words, contributions for the research are proposing a hybrid GA-SA algorithm, developing a multi-objective function, testing the algorithm in a hospital setting, and comparing the results of GA-SA with GA.

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.

Multi-objective optimal RFID reader deployment using a Leaders and Followers algorithm

The Radio Frequency Identification (RFID) reader deployment problem (RDP) is a key challenge in the planning of RFID networks. The majority of the current research uses metaheuristic algorithms to solve the multi-objective RDP for unconstrained deployment of readers possessing omnidirectional antennae. The increasing adoption of the Internet-of-Things (IoT) technology and smart directional antennae will make the RDP even more challenging. The contributions of this work are: firstly, modelling a novel RDP formulation as a mixed-integer nonlinear program (MINLP); secondly, incorporating both directional and omnidirectional antenna models into the RDP; and, lastly, introducing an adapted Leaders and Followers (LaF) solution algorithm for the RDP. The proposed LaF algorithm results in reduced energy use and interference reduction in comparison to other popular metaheuristic algorithms. The proposed MINLP model is also used to demonstrate the advantage of deploying readers possessing diverse antennae coverage compared to the exclusive use of omnidirectional antennae.

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.

Model of dynamic clustering‐based energy‐efficient data filtering for mobile RFID networks

Data filtering is an essential task for improving the energy efficiency of radio-frequency identification (RFID) networks. Among various energy-efficient approaches, clustering-based data filtering is considered to be the most effective solution because data from cluster members can be filtered at cluster heads before being sent to base stations. However, this approach quickly depletes the energy of cluster heads. Furthermore, most previous studies have assumed that readers are fixed and interrogate mobile tags in a workspace. However, there are several applications in which readers are mobile and interrogate fixed tags in a specific area. This article proposes a model for dynamic clustering-based data filtering (DCDF) in mobile RFID networks, where mobile readers are re-clustered periodically and the cluster head role is rotated among the members of each cluster. Simulation results show that DCDF is effective in terms of balancing energy consumption among readers and prolonging the lifetime of the mobile RFID networks.

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.

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.