Radio Frequency Identification Network Research Articles

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.

A beacon analysis-based RFID reader anti-collision protocol for dense reader environments

Radio-frequency identification (RFID) is one of the major technologies for auto-identification in many applications. When multiple readers work together to improve the read rate and reliability, a dense RFID network is created. One of the most important issues to be considered in such environments is reader collision, which leads to reduced throughput. Although different schemes have been proposed to reduce reader collision, they often either require additional hardware or do not use network resources efficiently, or are not consistent with international laws and standards. In this paper, by managing and analyzing beacon messages, a new centralized scheme, which is consistent with European standards and laws, is proposed to reduce collisions and increase throughput. In the proposed scheme, by sending a priority code via a beacon message and making decisions based on the beacon messages received by readers, the available resources are used optimally, and the maximum number of readers is activated in one round. Additionally, in the proposed scheme, the priority code is initialized in such a manner that the readers access the channel fairly. The simulation results and comparison of the proposed scheme with other protocols indicate that throughput increased. Because of the increased throughput and fairness of the proposed scheme, the delay reduced compared with other schemes, and overall, the proposed scheme was superior to other centralized schemes.

Two-Level Master–Slave RFID Networks Planning via Hybrid Multiobjective Artificial Bee Colony Optimizer

Radio frequency identification (RFID) networks planning (RNP) is a challenging task on how to deploy RFID readers under certain constraints. Existing RNP models are usually derived from the flat and centralized-processing framework identified by vertical integration within a set of objectives which couple different types of control variables. This paper proposes a two-level RNP model based on the hierarchical decoupling principle to reduce computational complexity, in which the costefficient planning at the top levels is modeled with a set of discrete control variables (i.e., switch states of readers), and the quality of service objectives at the bottom level are modeled with a set of continuous control variables (i.e., physical coordinate and radiate power). The model of the objectives at the two levels is essentially a multiobjective problem. In order to optimize this model, this paper proposes a specific multiobjective artificial bee colony optimizer called H-MOABC, which is based on performance indicators with reinforcement learning and orthogonal Latin squares approach. The proposed algorithm proves to be competitive in dealing with two-objective and three-objective optimization problems in comparison with state-of-the-art algorithms. In the experiments, H-MOABC is employed to solve the two scalable real-world RNP instances in the hierarchical decoupling manner. Computational results shows that the proposed H-MOABC is very effective and efficient in RFID networks optimization.

Optimal solution for data collision avoidance in radio frequency identification networks

When data collisions happen in radio frequency identification (RFID) networks, inaccurate tag's information or the communication between the RFID reader and the RFID tag occur. Although recent technological advances have improved the inquiry capacity performance of RFID readers, the performance of RFID networks can still be affected by collisions. We propose a novel solution that helps select RFID readers which have the appropriate interrogation range to enable the highest number of tags to be read without collisions (often referred to as the reader‐tag‐data collision avoidance [RTDCA] problem). To solve the RTDCA problem, we introduce a relative collision graph to transform the original problem into one that involves finding the maximum weight independent set. We then formulate the RTDCA problem using a mathematical model. To solve the RTDCA problem by using a linear programming model requires an exponential number of variables which cannot be solved in polynomial time. Our proposed solution makes use of dynamic programming with path decomposition to determine the optimal number of RFID tags that can be read without data collisions.

Redundant Reader Elimination in Large-Scale Distributed RFID Networks

Radio frequency identification (RFID) is a key enabling technology for large-scale Internet of Things (IoT). Its deployment and management impact significantly on the operational effectiveness and scalability of IoT. Redundant reader elimination is of great importance to reduce the system’s overhead and prolong the lifetime of RFID networks. It helps to reduce unnecessary reader-tag interactions and the cost of collision avoidance algorithm in distributed data collection of RFID network. In large-scale distributed RFID networks, one of the most challenging tasks for redundant reader elimination is to improve the performance of distributed algorithms. This paper proposes a novel distributed redundant reader elimination algorithm based on the threshold selection process, named threshold selection algorithm (TSA), for RFID networks. TSA algorithm selects effective reader iteratively based on the threshold sequence determined by expected tag coverage. This paper also introduces an optimization mechanism into TSA based on detected movement, named TSA with movement detection algorithm. By preliminary simulation, we determine the suggested parameter of linear multiplier for TSA. Our experiments show that TSA algorithm can provide 30%–60% better performance than other major distributed algorithms and also multiphase schemes in both dense and sparse environments. The overhead of TSA algorithm is 30%–50% lower than other selected algorithms, especially in tag-write operations.

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.

Comparison of energy consumption for reader anti-collision protocols in dense RFID networks

Radio-frequency identification (RFID) is known as a universal technology and has been in the center of attention in the past few years. Readers and tags constitute the main parts of an RFID system. Similar to other wireless devices, RFID readers are confronted with the critical problem of collision. This is because in some applications, numerous readers are spread out densely and are attempting to attain the same tags simultaneously. Collisions are classified to reader-to-reader and reader-to-tag collisions. Both of these collisions may decrease the system’s throughput and performance, or even result in the consumption of a considerable amount of energy. Many various anti-collision protocols have been proposed to avoid these collisions, but there are only a few researches concerning their energy efficiency. While this paper will look at the main reader anti-collision protocols, it will also demonstrate the evaluation results of their energy efficiency via experimental simulations in similar circumstances. The obtained results can help in choosing the most efficient anti-collision protocol in applications like using mobile readers or combining RFIDs with WSN networks which have energy-consuming constraints.

RFID Integration with Wireless Sensor Networks

Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two important wireless technologies that have wide variety of applications and provide limitless future potentials. However, RFID and sensor networks almost are under development in parallel way. Integration of RFID and wireless sensor networks attracts little attention from research community. This paper first presents a brief introduction on RFID, and then investigates recent research works, new products/patents and applications that integrate RFID with sensor networks. Four types of integration are discussed. They are integrating tags with sensors, integrating tags with wireless sensor nodes, integrating readers with wireless sensor nodes and wireless devices, and mix of RFID and sensors. New challenges and future works are discussed in the end.

Managing Crowds with Wireless and Mobile Technologies

Thousands of people have lost their lives in stampedes and other crowd related disasters in recent years. Most of these fatalities seem to have been caused by poor control and management of crowds, which is discussed in this article. An efficient and effective crowd management system must also have a plan to deal with the ongoing threat of terrorism and outbreak of various kinds of communicable diseases. In this article, we present a framework of a Crowd Control and Health Management System specially designed to prevent and manage stampedes and other disasters. The system has two subsystems; one for dealing with the management of stampedes and other disasters and the other with healthcare management. As part of the proposed system, an algorithm for an early detection of stampedes, with proof and simulation of implementation, is provided. As part of the healthcare management subsystem, we integrate several mobile applications and develop four of them dealing with relief issues, blood donations, complaints and alerts, and utilizing mobile phones as a sensor device. Our system makes use of various kinds of wireless, mobile, and other technologies and tools including Fog Computing, Smart Phones, Smart Digital Street, IP‐Cameras, Radio Frequency Identification (RFID), Voice Alarm, Light Alarm, and Global Positioning System (GPS). We compare merits and effectiveness of RFID and Wireless Sensor Networks (WSNs), as well as those of Cloud and Fog with a view of using them as part of the proposed framework. We also discuss applications of our systems in real‐life cases of Hajj, an annual pilgrimage of millions of people to Mecca, and Kumbh Mela, a periodic gathering of tens of millions of people in India, both of which have accounted for the majority of fatalities in stampedes and other disasters.

Centralized Connectivity for Multiwireless Edge Computing and Cellular Platform: A Smart Vehicle Parking System

This study takes an intuitive step to develop the user‐convenient smart vehicle parking system (SVPS), a smart system able to manage the massive crowd of vehicles during parking searching and do the better jobs of parking reservation and management, with the shorter‐path processing tactics. For that, this study inclusively employed the mapping strategy, where the system parking points are prevalent, to assist the users to get the parking information fast and conveniently. This study is comprised of the several parking points systematically spread over the several locations and traceable over the available graphical map, and the overall information is easily accessible using smart devices. For parking information, a smart web application which is another important module of this study is designed, with which the SVPS system’s registered users are able to access all the services provided for smart vehicle parking searching and reservation in efficient and reliable ways. An integrated network approach, RFID (radio frequency identification) and wireless sensors network (WSN), called RF‐WSN, is employed to retrieve the real‐time information from the installed and configured sensor devices in RFID‐WSN network.

Forecasting of Radio Frequency Identification Entropy Viscosity Parking and Forwarding Algorithm Flow Risks and Costs: Integrated Supply Chain Health Manufacturing System (ISCHMS) Approach

Our research hypothesis is that we could improve traditional supply chain management (SCM) health by designing a new integrated manufacturing enterprise system based on parking and forwarding algorithms, radio frequency identification (RFID) feedback and a Markov decision process. The goal of our research was to optimize a cost function that depends on RFID system viscosity. The viscosity of the RFID can be defined as the flow of data through the RFID network. This process can be measured by the entropy of the parking and forwarding of the signal, leading to a sustainable supply chain system. The dynamics of this system are governed by differential equations. Using this entropy model, we verified the integrated supply chain health manufacturing system risk failure reduction via simulation. Our method employs a fuzzy Kalman filter, improves the system, reduces backorders and demonstrates that RFID viscosity is an effective means to this end. A Markov blanket entropy approach demonstrated that it can capture and provide a theory of RFID viscosity parking and forwarding algorithms as a networking solution to lowering SCM costs, reducing waste, and improving sustainability.

Situation Awareness Inferred From Posture Transition and Location: Derived From Smartphone and Smart home Sensors

Situation awareness may be inferred from user context such as body posture transition and location data. Smartphones and smart homes incorporate sensors that can record this information without significant inconvenience to the user. Algorithms were developed to classify activity postures to infer current situations; and to measure user's physical location, in order to provide context that assists such interpretation. Location was detected using a subarea-mapping algorithm; activity classification was performed using a hierarchical algorithm with backward reasoning; and falls were detected using fused multiple contexts (current posture, posture transition, location, and heart rate) based on two models: “certain fall” and “possible fall.” The approaches were evaluated on nine volunteers using a smartphone, which provided accelerometer and orientation data, and a radio frequency identification network deployed at an indoor environment. Experimental results illustrated falls detection sensitivity of 94.7% and specificity of 85.7%. By providing appropriate context the robustness of situation recognition algorithms can be enhanced.

A new variant of cuckoo search algorithm with self adaptive parameters to solve complex RFID network planning problem

With the paradigm of the Internet of things, each object in the physical world can be remotely identified, controlled, and located through networks. Thanks to their low cost and their small form, the Radio frequency identification (RFID) tags are frequently used to tag objects . The tags or objects are often distributed in large geographic areas. Due to the limit of the interrogation range of RFID readers, multiple readers should be deployed to read the information stored on all tags. The major challenge in an RFID network design is to find the optimal placement and parameters of readers in order to meet the essential requirements of an RFID system such as coverage, load balance and interference between readers. This challenge has led to a new research area known in the literature as the RFID network planning problem. This problem is characterized by a large number of constraints as well as numerous objectives and it proves to be NP-hard. In this paper, we develop a novel optimization algorithm, namely the self adaptive cuckoo search (SACS) algorithm, to solve this complex problem. In the SACS algorithm, the control parameters of the cuckoo search (CS) algorithm are adjusted dynamically in real time. The self-adaptation phenomenon allows the evolutionary algorithm to be more flexible and closer to natural evolution. The experimental results on 13 standard benchmark functions demonstrate that the proposed algorithm is more efficient than five adaptive variants of the CS algorithm. In the second part of the paper, the SACS algorithm is also used to solve three difficult RFID network planning instances. The simulation studies show that the SACS algorithm obtains better solutions for the RFID network planning problem than the original CS, four adaptive CS variants, the GA and the PSO in terms of optimization and robustness. To test the effectiveness of the SACS algorithm on a real problem, a case study is carried out.

A Power Control and Anticolision Mechanism for RFID Systems

RFID (Radio Frequency IDentification) applications require identification of large number of tags, which results in increased collisions and degradation in the performance of traditional DFSA (Dynamic Frame slot ALOHA) algorithms. This paper proposes a power control mechanism to estimate the amount of tags in RFID networks. The mechanism is based on the division of the interrogation zone in sub-areas called clusters. This division is used to interrogate in a single slot all tags of a cluster, perform measurements of RSSI and, with it, estimate the amount of tags per cluster. The mechanism is simulated and evaluated using our own simulator developed in C/C ++ programming language. We compared the results of number of slots, identification time and energy consumption with that obtained from the use of the optimal DFSA algorithm and Q algorithm of the EPCglobal standard. From the simulation results, one can see that the proposed mechanism provides performance 99% close to the ideal DFSA in dense networks, where there are large amount of tags.

SwitchTable: An efficient anti‐collision algorithm for RFID networks

Radio frequency IDentification (RFID) applications in the Internet of Things usually use multiple readers to read multiple tags. In such a practice, reader collisions may critically affect the performance. To obtain better reading schedules for the readers in order to enhance the RFID performance, the authors introduce a new and efficient anti-collision algorithm – the SwitchTable algorithm – in this study. The authors build the new algorithm over the concepts of the four-colour theorem and also the switch tables to fix the loopholes in existing algorithms – mainly the practice of variable MaxColor and the broadcast of kick signals. The authors’ SwitchTable algorithm sets MaxColor to four according to the four-colour theorem, to avoid possible collisions and solve the problem of previous dynamic MaxColor change. It meanwhile uses a colour switch mechanism which works by switch tables and unicasting switch signals (not broadcasting kick signals) to remove the impact of sending kick signals and attain more efficient planned colour change. Extended simulation runs are carried out to evaluate the performance of various anti-collision algorithms, including DCS, ColorWave, ColorTable and the authors’ SwitchTable. The results show that, with the new features, the authors’ new algorithm produces constantly better transmission success ratios and throughput, particularly in more complex RFID networks.

Large-Scale Multiantenna Multisine Wireless Power Transfer

Wireless Power Transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things, Radio Frequency identification (RFID) networks, etc. Although there has been some progress towards multi-antenna multi-sine WPT design, the large-scale design of WPT, reminiscent of massive MIMO in communications, remains an open challenge. In this paper, we derive efficient multiuser algorithms based on a generalizable optimization framework, in order to design transmit sinewaves that maximize the weighted-sum/minimum rectenna output DC voltage. The study highlights the significant effect of the nonlinearity introduced by the rectification process on the design of waveforms in multiuser systems. Interestingly, in the single-user case, the optimal spatial domain beamforming, obtained prior to the frequency domain power allocation optimization, turns out to be Maximum Ratio Transmission (MRT). In contrast, in the general weighted sum criterion maximization problem, the spatial domain beamforming optimization and the frequency domain power allocation optimization are coupled. Assuming channel hardening, low-complexity algorithms are proposed based on asymptotic analysis, to maximize the two criteria. The structure of the asymptotically optimal spatial domain precoder can be found prior to the optimization. The performance of the proposed algorithms is evaluated. Numerical results confirm the inefficiency of the linear model-based design for the single and multi-user scenarios. It is also shown that as nonlinear model-based designs, the proposed algorithms can benefit from an increasing number of sinewaves.

Optimizing Radio Frequency Identification Networks Planning by using Particle Swarm Optimization Algorithm with Fuzzy Logic Controller and Mutation

ABSTRACTOne of the main problems for radio frequency identification (RFID) networks planning is to optimize readers’ position. The problem of RFID networks planning (RNP) includes many goals like coverage, interference, load balancing, and economic efficiency. In this paper, we try to introduce a particle swarm optimization (PSO) algorithm-based method to improve RFID networks planning in which a fuzzy logic controller and a mutation operator have been used to prevent quick algorithm convergence to local optimum. According to the findings, the recommended algorithm comparing other evolutionary algorithms locates readers in places where the goals of RFID networks planning could be achieved at the lowest cost.

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.

Cost and Lightweight Modeling Analysis of RFID Authentication Protocols in Resource Constraint Internet of Things

Internet of Things (IoT) is a pervasive environment to interconnect the things like: smart objects, devices etc. in a structure like internet. Things can be interconnected in IoT if these are uniquely addressable and identifiable. Radio Frequency Identification (RFID) is one the important radio frequency based addressing scheme in IoT. Major security challenge in resource constraint RFID networks is how to achieve traditional CIA security i.e. Confidentiality, Integrity and Authentication. Computational and communication costs for Lightweight Mutual Authentication Protocol (LMAP), RFID mutual Authentication Protocol with Permutation (RAPP) and kazahaya authentication protocols are analyzed. These authentication protocols are modeled to analyze the delays using lightweight modeling language. Delay analysis is performed using alloy model over LMAP, RAPP and kazahaya authentication protocols where one datacenter (DC) is connected to different number of readers (1,5 or 10) with connectivity to 1, 5 or 25 tags associated with reader and its results show that for LMAP delay varies from 30-156 msec, for RAPP from 31-188 while for kazahaya from 61-374 msec. Further, performance of RFID authentication protocols is analyzed for group construction through more than one DC (1,5 or 10) with different number of readers (10, 50 or 100) and tags associated with these readers (50, 500, 1000) and results show that DC based binary tree topology with LMAP authentication protocol is having a minimum delay for 50 or 100 readers. Other authentication protocols fail to give authentication results because of large delays in the network. Thus, RAPP and Kazahaya are not suitable for scenarios where there is large amount of increase in number of tags or readers.

Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty

Radio frequency identification (RFID) is widely used for item identification and tracking. Due to the limited communication range between readers and tags, how to configure a RFID system in a large area is important but challenging. To configure a RFID system, most existing results are based on cost minimization through using 0/1 identification model. In practice, the system is interfered by environment and probabilistic model would be more reliable. To make sure the quality of the system, more objectives, such as interference and coverage, should be considered in addition to cost. In this paper, we propose a probabilistic-based multi-objective optimization model to address these challenges. The objectives to be optimized include number of readers, interference level and coverage of tags. A decomposition-based firefly algorithm is designed to solve this multi-objective optimization problem. Virtual force is integrated into random walk to guide readers moving in order to enhance exploitation. Numerical simulations are introduced to demonstrate and validate our proposed method. Comparing with existing methods, such as Non-dominated Sorting Genetic Algorithm-II and Multi-objective Particle Swarm Optimization approaches, our proposed method can achieve better performance in terms of quality metric and generational distance under the same computational environment. However, the spacing metric of the proposed method is slightly inferior to those compared methods.