RFID Network Research Articles

Dairy cows' localisation and feeding behaviour monitoring using a combination of IMU and RFID network

Dairy cows' localisation and feeding behaviour monitoring using a combination of IMU and RFID network

Dairy cows' localisation and feeding behaviour monitoring using a combination of IMU and RFID network

Localisation systems have become recently important for the monitoring of cow's health. However, the main limitations of these systems lie in the lack of information about cow's activities such as ...

Optimization of Intelligent Logistics Supply Chain Management System Based on Wireless Sensor Network and RFID Technology

Based on the in‐depth study of two technologies, wireless sensor network, and RFID technology, this paper proposes a corresponding solution to the urgent storage positioning problem and the safety of goods in transit in the logistics supply chain process. And on this basis, it broadens the whole logistics process of warehousing and distribution and designs a framework of intelligent logistics supply chain management system with wireless sensor network and RFID technology to realize the management process of logistics main business. For the problems of complicated hardware wiring, the excessive number of positioning nodes and low effective coverage rate of traditional RFID‐based positioning methods in large‐scale warehousing environment, the integrated RFID and wireless sensor network fusion technology constructs a new movable anchor node, divides the nodes in the traditional positioning process into fixed anchors and mobile anchors, proposes a secondary positioning method, improves positioning accuracy and effective coverage rate, and provides a new positioning method for large‐scale storage environment. For the transportation of goods in transit, especially precious and dangerous goods, a six‐axis acceleration sensor‐based method for detecting the behavioral attitude of goods in transit is proposed. Time window slicing of cargo motion data stream is performed to reduce data processing and based on the statistical features of a large amount of experimental data, and feature vectors and classifiers are analyzed and designed to achieve behavioral attitude classification of goods in transit. The experiment proves that this method has high recognition accuracy and has certain practical application value. Finally, the whole storage and distribution logistics process is integrated, the framework of intelligent logistics supply chain management system combining WSN and RFID is built based on wireless sensor network software and hardware platform, and the detailed design of logistics supply chain management system business process is mainly carried out. Combined with the two key methods proposed in this paper, the information management process of traditional logistics business such as access, storage positioning and monitoring, and distribution monitoring and management is realized, and the management system is tested on this basis. The testing effect proves that this system has certain deployment reference significance and practical application value and to a certain extent can help improve the intelligence level of logistics supply chain management and achieve the expected goal of this study.

The Supply Chain Financial Supervision Mechanism of the Internet of Things Based on the Integration of RFID and Wireless Sensor Network

Based on the fusion of wireless sensor modules and UHF RFID readers, this paper develops a UHF RFID reading and writing system based on wireless sensing technology and designs a communication interface circuit that integrates wireless sensing and RFID technology. The financial supervision mechanism of the Internet of Things compatible with these two technologies improves the communication distance limitation of the RFID system and improves the flexibility of the RFID reading and writing system. Secondly, in view of the security issues of WSN small and medium data distribution, this paper proposes a lightweight small data supply chain financial supervision program. The solution adopts two‐way authentication technology based on the improvement of the Internet of Things supply chain, the negotiation of the Internet of Things supply chain, and a lightweight encryption algorithm, which realizes the secure communication between ordinary nodes and gateway nodes, and can more effectively solve the problem of small and medium data distribution in WSN to resist the deficiencies of the financial supervision mechanism; compared with existing solutions of the same magnitude, it has higher security and efficiency. Experimental simulation shows that an important problem in the converged network is the imbalance of node energy consumption. In order to reduce the negative impact of the imbalance of energy consumption on the network, this article compares the two for direct transmission protocol and minimum energy multihop routing protocol. From the perspective of supply chain process performance, based on the proposed technical impact framework, it analyzes how companies actually carry out ID application work, making the research from general to individual, from theory to practice. The network energy consumption calculation method under this protocol improves an improved network architecture model that increases the communication scale. The test results show that the technical impact analysis framework established in this article based on the theory of the Internet of Things and the advantages of RFID technology can analyze the automation benefits, information benefits, and transformative benefits brought by automatic identification technology to the supply chain and introduce complementary resources and potential factors. It provides a theoretical basis and operation method for measuring the impact of RFID on the performance of the financial supervision process based on the Internet of Things supply chain in practical applications.

Dynamic Sensor Speed Measurement Algorithm and Influencing Factors of Traffic Safety With Wireless Sensor Network Nodes and RFID

The proportion of traffic accidents caused by speeding is high, so the safety and regulatory issues of urban roads are particularly important. Existing road speed detection can only be detected on specific road sections due to cost and technical issues, such as radar or video image speed measurement. Although GPS-based speed detection is convenient for the driver, its speed measurement accuracy is not ideal. It cannot be used as a speed measurement certificate for the traffic supervision department. The vehicle speed detection based on the RFID road system can effectively solve the above problems. It can not only realize the speed detection of the whole road section, but also feedback the relevant information of over speed vehicles to the driver and the road supervision department in time. The Internet of Vehicles provides real-time early warning of vehicles around speeding vehicles to minimize the probability of traffic accidents. This article first introduces the basic principles, classification, and main features of RFID technology and wireless sensor networks. On this basis, the system architecture of the vehicle speed detection based on RFID-based wireless sensor network nodes is established. Secondly, the vehicle speed detection method based on the RFID road system is studied in different road scenarios, and a dynamic vehicle speed measurement algorithm based on RFID is proposed. Simulation experiment results show that the dynamic speed measurement algorithm proposed in this article can detect the instantaneous speed of the vehicle passing more accurately.

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.

RFID network scheduling using an improved bat algorithm

The performance of RFID networks can be optimized by RFID reader scheduling. This paper proposed a novel approach using an improved bat algorithm (IBA), which can optimize RFID networks. The IBA algorithm includes 2 improved mechanisms. In the proposed approach, all RFID readers are scheduled to work in appropriate sequence, which can greatly reduce RFID collisions. Experiments on two different RFID networks have been carried out to evaluate the effectiveness. Simulation results show that the proposed approach using IBA has better optimization precision than control algorithms.

Multi objective optimization of Indoor UHF RFID Network Based on Gradient - Cuckoo search

Network design, in general is a critical concept due to its effect on efficiency, cost and other significant factors. In recent years, RFID is widely applied for RNP Network design. The large -area network design process requires a significant number of interrogating antennas based on the reader-tag range communication. RFID technology uses a huge number of tags communicate with a small number of the reader, from thus point of view, the challenges of large scale RNP problems include high computational cost due to the time consumption of RFID readers placement error, as a result, these challenges reduce the effectiveness of the RFID system In this study, a model of multi-objective function for RFID reader placement was conducted on various large -area condition to evaluate the impact of network design expansion. A comparative analysis was performed with (GBCS algorithm) Gradient-Based Cuckoo Search. The dataset was performed for the area of 80m x 80m and 150m x 150m. Simulation results exhibited that the performance of (GBCS) Gradient-Based Cuckoo presented a minimum number of deployed readers with maximum RFID tags coverage and was superior in solving large scale RFID-NP problems. Simulation results not only explained that the present algorithm is strong and workable but also showed excellent approximation abilities even in the high scale-area. Consequently, the authors recommend if the area bigger than (80m2), there is a need to divide the area into separate regions and deal with each region separately to be sure that the objective function can work efficiently.

Interconnected IoT Architectural Platforms and Applications

Future technology totally based on smart automation and smart information transfer. Internet of Things makes a bridge communication from appliances and industry to smart automation. Sensors of Internet of Things records the physical data from the appliances and industry and transfer accordingly for analytic and decision processing. Next in line to advancements, for example, RFID and Wireless Sensor Networks, the Internet Of Things has unearthed bolt upright interconnection of restrictive frameworks. IoT depicts the future condition of the interconnected network between these storehouses, empowering the revelation of tangible sensors and elucidation of data transfer amidst the things. This paper comes up with a schematic path and network empowerment to give the interconnections among different frameworks and platform based data transmission technology and information management.

A Holistic Methodology for Improved RFID Network Lifetime by Advanced Cluster Head Selection using Dragonfly Algorithm

A Holistic Methodology for Improved RFID Network Lifetime by Advanced Cluster Head Selection using Dragonfly Algorithm

Smart Batch-Processing Protocol for RFID Multiple Tag Identification

The common paradigm of the RFID network is to obtain data from multiple tags through a reader. The tag collision problem seriously affects the efficiency of tag identification. The anti-collision algorithm provides a reliable guarantee to identify multiple tags as fast as possible. Many papers have proposed query tree algorithms based on bit tracking, but these algorithms still have problems such as idle paging and redundant prefixes. In this paper, we start with an analysis of state-of-the-art anti-collision algorithms. Based on summarizing current works, we propose a Smart Batch-Processing protocol (SBP) for RFID multiple tag identification based on M-ary collision bit arbitration mechanism. In the SBP, the reader firstly maps m-bit collision bits into 2 m slots and then predicts the distribution of tags in slots. Finally, the reader allows the tag to return the remaining part of its ID at the mapped slot using a batch-processing command. By using SBP, the reader can simultaneously identify 2 m groups of tags in a slot. Besides, empty queries can be eliminated in the tag identification process. The description of SBP is given first, and then, its performance is theoretical analyzed and simulated. Both theoretical analysis and extensive simulation results show that the SBP algorithm reduces 22.94% and 22.28% of time and energy consumption compared to other M-ary collision bit arbitration based methods.

Modeling and Optimization of RFID Networks Planning Problem

Aimed at solving the RFID networks planning problem, a mathematical model considering tag coverage and reader interference is presented. The DEEPSO algorithm that adds differential evolution and evolutionary strategies to the standard PSO is introduced to the optimization of RFID Networks Planning, which can improve the global convergence ability and particle diversity and can avoid falling into local convergence. According to the simulation results, compared with RFID networks planning by standard PSO, RFID networks planning by DEEPSO is superior.

Structured Deployment using Clustering Mechanism for Improving the Performance of the RFID Network

RFID is one of the most suitable technologies for today’s world. RFID technology is mainly focused for tracking and locating the objects. The objects may be either moving or immovable. Normally RFID communication is based on the RFID Tag and the Reader permutations. The Readers were deployed in different locations for the better communication between the tag and reader. This paper is focused on the structured deployment of RFID readers to form a network using clustering mechanism. The performance of the network will be analyzed by various network attribute such as throughput, Accuracy, Missing tag ratio, Read Rate and Delay. For an efficient communication, the structured deployment of the RFID Reader’s is most important. The clustering method is used for the proper deployment of RFID readers for efficient communication. Cluster is the grouping of RFID readers based on the geographical locations. Every cluster has a cluster head (CH) and these Cluster heads are responsible for every transaction between the cluster to cluster communications. This cluster head have selected according to their energy level and the centrality of the adjunct. This paper detailed how the RFID readers and tags are communicated using the clustering mechanism and performance of the RFID network is analyzed and discuss the results.

Design Training Kits CPI for Vocational Learning in Industry 4.0

Cyber Physical Infrastructure (CPI) is one of eight supporting technology infrastructures of Industry 4.0. CPI is built from several technologies including RFID, RTLS, sensors, wireless sensors, actuators, processors, controllers, and data communication network systems. This paper presents the design of vocational learning training kits for CPI competencies in industry 4.0. CPI training kits for developing industry competency 4.0 are designed using the waterfall life-cycle method. Design analysis, design systems, part production, assembling processing, firmware production, systems evaluation, and test used interactively to produce the final product. Industrial 4.0 CPI training kits design includes: (1) PLC processor systems, mini PC, Arduino mega2560; (2) a timer counter controller with a PID systems; (3) RFID and RTLS identifiers; (4) proximity sensor, magnetic sensor, inductive sensor, capacitive sensor, photoelectric sensor, humadity sensor, thermocouple, thermostat, thermistor, opto coupler, limit switch, push button, analog V, ultrasonic, pressure sensor, speed sensor; (5) AC motor actuators, servo motors, stepper motors, valves, pneumatic-hydraulic cylinders, lamps, heaters, relays, SSR, 24V-5V converters; (6) LCD, LED monitor display; (7) data communication between controllers, (8) data communication modules to the internet; (9) user manual and job sheet. The test results show: content experts have given a score of 76.7 from a scale of 100, media experts have given a score of 88.6 from a scale of 100 and users have given a score of 85.4 from a scale of 100. Field trials through training show results can improve competencies in industry 4.0. CPI training kits strongly support the education and training needs of various competencies to develop the capacity of workers in industry 4.0. CPI training kits can be used in schools, universities, and training centers for industrial technology 4.0.

Achromatic plumage brightness predicts stress resilience and social interactions in tree swallows (Tachycineta bicolor)

AbstractTheory suggests that signal honesty may be maintained by differential costs for high and low quality individuals. For signals that mediate social interactions, costs can arise from the way that a signal changes the subsequent social environment via receiver responses. These receiver-dependent costs may be linked with individual quality through variation in resilience to environmental and social stress. Here, we imposed stressful conditions on female tree swallows (Tachycineta bicolor) by attaching groups of feathers during incubation to decrease flight efficiency and maneuverability. We simultaneously monitored social interactions using an RFID network that allowed us to track the identity of every individual that visited each nest for the entire season. Before treatments, plumage coloration was correlated with baseline and stress-induced corticosterone. Relative to controls, experimentally challenged females were more likely to abandon their nest during incubation. Overall, females with brighter white breasts were less likely to abandon, but this pattern was only significant under stressful conditions. In addition to being more resilient, brighter females received more unique visitors at their nest-box and tended to make more visits to other active nests. In contrast, dorsal coloration did not reliably predict abandonment or social interactions. Taken together, our results suggest that females differ in their resilience to stress and that these differences are signaled by plumage brightness, which is in turn correlated with the frequency of social interactions. While we do not document direct costs of social interaction, our results are consistent with models of signal honesty based on receiver-dependent costs.

Scheduling RFID networks in the IoT and smart health era

As a potential way to dramatically save energy and live in a green and smarter planet, the internet of things (IoT) aims to utilize energy-efficient enabling technologies such as the RFID systems in our daily life applications. RFID, or Radio Frequency Identification, is used to efficiently locate items using tags and readers. In this paper, we propose localized reader scheduling algorithms for RFID networks. We consider readers with limited amounts of energy, powered by a battery. Using only local information, the readers schedule themselves to minimize energy usage and maximize network lifetime. We compare the performance of our localized algorithms to a centralized heuristic (the research problem is NP hard) based on a set cover approximation solution and show that the localized algorithms obtain equal or better performance in comparison to centralized solution, achieving 5% higher area under the curve (AUC) in scenarios with 50% readers, and 13 and 8% higher AUC in 25% and 15% reader scenarios, respectively.

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.

Proposal on EPC system enhancement to realise supply chain process analysis

Although IT services for business process analysis have become popular, they do not cover the business process of the Supply Chain Management (SCM), since information in the real space is difficult to capture by existing IT systems. Therefore, process analysis for the item moves in the supply chain also is not commonly used. Assuming the use of Electronic Product Code (EPC) system, an implementation of networked RFID systems, this study proposes two supply chain process analysis methods, a performance analysis method and an anomaly analysis method. In addition, this study proposes a way to enhance EPC system with Hadoop system to meet the needs for massive transaction data processing. The proposal is evaluated using an agent-based simulation tool, and the feasibility of the business process analysis methods as well as system enhancement is confirmed.

An efficient resource allocation scheme in a dense RFID network based on cellular learning automata

SummaryRadio‐frequency identification (RFID) is a wireless communication technology. Radio frequencies can cause interference in a dense RFID system, thus decreasing efficiency. In recent years, many protocols have been proposed to reduce reader collisions based on multiple‐access techniques. The main weakness of Time Division Multiple Access (TDMA)‐based schemes is the random selection of resources. Additionally, they do not consider the distance between the interfering readers. Therefore, the likelihood of interference in an RFID system will be increased. To address this problem, we propose a new scheme for allocating resources to readers using a learning technique. The proposed scheme takes into account the distance between interfering readers, and these readers acquire the necessary knowledge to select new resources based on the results of the previous selection of neighboring readers using cellular learning automata. This approach leads to reduced interference in an RFID system. The proposed scheme is fully distributed and operates without hardware redundancy. In this scheme, the readers select new resources without exchanging information with each other. The simulation results show that the percentage of kicked readers decreased by more than 20%, and the proposed scheme also provides higher throughput than do state‐of‐the‐art schemes for dense reader environments and leads to further recognition of tags.