Smart Card Research Articles

Reconstruction of incomplete public transportation check-out records by heuristic approaching

Smart cards, integrated circuit cards, are frequently used to pay transportation fees in many countries such as the United States, the European Union, Korea and others. Smart card transaction histories in public transportation have been used in making diverse traffic policies. Unfortunately, data preprocessing is required to organize records to extract useful information since the raw data contain artifacts and incomplete information. The paired data, i.e. a boarding and its associated alighting, are one of the key pieces of information required to yield the city traffic demand. However, it is difficult to gather the paired data mainly because the card tagging policies and data collecting methods vary from city to city and from institution to institution. The broken trip links between the boarding and its associated alighting information are the most frequent incomplete data in the smart card histories. Although huge budgets are spent on annual traffic surveys in many countries, there are no accurate Smart card data analysis methods that cover all city cases due to high data complexity and limited base information. Among these two restrictions, limited information is more difficult to resolve because it has a large codomain, thousands of stops, for inference under tiny samples. It gives handicaps of applying well-known high-performance approaches such as deep learning and probability analysis. We propose a reconstruction method by a customized clustering algorithm based on coordinates of transactions-occurred stops, user trip histories and actual vehicle movement trajectories for incomplete trip pairs. The proposed method is derived from evaluating each passenger’s historical clusters and trip patterns. In a blind test 79.1% accuracy is achieved in predicting missing alighting stops while conventional methods reach only 42.3% accuracy.

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Spatiotemporal trip profiles in public transportation reveal city modular structure

Understanding urban mobility patterns within public transportation (PT) systems is key for cities to improve services and promote sustainable mobility. Exploring daily PT riders’ traffic flows using anonymized big data is a first step to analyze when and to where people travel. Nevertheless, assessing the degree to which PT usage patterns correlate with the spatial distribution of points of interest (POI) and subsequently affect the city structure is less attempted. Classic approaches use questionnaires and survey data that are costly and often yielding limited statistical significance. The purpose of this research is to understand associations between travel patterns of urban commuters and the functional organization of a city. To this end, we propose a network-constrained temporal distance measure for modeling PT rider travel patterns from smart card data; and further introduce a fully autonomous approach to describe the span of services available at catchment areas around metro stations. The end result is a detailed analytical prescription of spatiotemporal commuting patterns in the city of Lisbon as well as an analytical contextual information that enables us to understand the functional modular structure of urban facilities. Using the city of Lisbon as the guiding case study, the gathered results confirm the hypothesis that PT rider’s daily flows along the PT network of metro stations reveal a city modular structure.

Breaking Fault Attack Countermeasures With Side-Channel Information

In the persistent fault-based collision attack (PFCA) [1], the adversary captures the information that the intermediate states have collided through identical correct/incorrect ciphertexts. However, fault countermeasures achieve suppression of incorrect ciphertexts and prevent the PFCA. In this paper, we measure the collision of internal states (or state bytes) using side-channel information. First, for round-level countermeasures, we identify state bytes hitting the same persistent fault during the first round of encryption by the shortest runtime. Additionally, we design sliding-window algorithms to automatically identify the runtime of one-round encryptions suitable for different execution environments. Second, for algorithm-level protections, we detect the collision of the internal states after the first round of encryption through the maximum similarity of power consumption traces. Meanwhile, to address the low success rate of key recovery caused by miss detection due to noise within runtime or power consumption, we further revise the original filtering algorithm in PFCA. Third, we implement round-level protected AES on PC to measure runtime, and both AES protected by round-level (or algorithm-level) countermeasures and SM4 [2] protected by a round-level countermeasure on a smart card to collect power consumption. Finally, the experimental result proves that the revised PFCA successfully recovers the key.

Smart Parking Monitoring System using RFID

Abstract: Parking of the vehicle is the major issue in the cities at different location. Although there are many parking issue solutions and considering by past experience a parking solution in which help of RFID device. A smart reserve parking system using RFID is an advanced solution for managing parking spaces using radio-frequency identification (RFID) technology. This system allows drivers to reserve and access parking spaces securely and conveniently using RFID tags. The system uses RFID readers to identify and track the location of tagged vehicles, enabling drivers to find available parking spaces easily.The authentication technique implemented is RFID on the algorithm based on smart cards (RFID- tags). The data stored in the algorithm allows the entry/exit of the vehicle through which unauthorized vehicle not be permitted. The system offers benefits such as enhanced security, reduced traffic congestion, and improved user experience. Additionally, it provides parking operators with real-time data and analytics, enabling them to optimize parking space usage and generate revenue. Despite some challenges associated with the implementation of RFID technology, the benefits of the smart reserve parking system make it a promising solution for managing parking spaces in various environments, including urban areas, airports, and shopping malls

E- Health Card

Abstract: This study focuses on the deployment of smart health cards and E- Healthcare to improve the standard of healthcare services through coordinated healthcare processes and IT. The administration of electronic medical records is made simpler and the privacy protection of sensitive data is improved using smart cards. A web application with a user- friendly interface was developed to store and manage patient medical records on cards and servers, reducing the risk of data loss due to card damage. The project exemplifies real- time database management, providing easy, efficient, and reliable data processing and management for healthcare providers.

Pengembangan Pteridophyta Smart Card Sebagai Media Pembelajaran Tumbuhan Paku untuk Mengurangi Plant Blindness di Kalangan Mahasiswa

This study aims to produce a development product in the form of Pteridophyta Smart Cards to overcome the high problem of plant blindness, especially ferns in college student. This type of research includes research and development using the modified ADDIE model (Analysis, Design, Development, Implementation and Evaluation). The product developed is then assessed for its feasibility by involving one material expert, one media expert and one design expert using a validation questionnaire. Based on the results of the Pteridophyta Smart Card product development research, it can be concluded that the product is very suitable for use in learning ferns. This is based on the results of the validation of material expert which reached a feasibility percentage of up to 96.67%, media expert 97.06% and design expert up to 94.44%.

A Lightweight, Secure Big Data-Based Authentication and Key-Agreement Scheme for IoT with Revocability

With the rapid development of Internet of Things (IoT), designing a secure two-factor authentication scheme for IoT is becoming increasingly demanding. Two-factor protocols are deployed to achieve a higher security level than single-factor protocols. Given the resource constraints of IoT devices, other factors such as biometrics are ruled out as additional authentication factors due to their large overhead. Smart cards are also prone to side-channel attacks. Therefore, historical big data have gained interest recently as a novel authentication factor in IoT. In this paper, we show that existing big data-based schemes fail to achieve their claimed security properties such as perfect forward secrecy (PFS), key compromise impersonation (KCI) resilience, and server compromise impersonation (SCI) resilience. Assuming a real strong attacker rather than a weak one, we show that previous schemes not only fail to provide KCI and SCI but also do not provide real two-factor security and revocability and suffer inside attack. Then, we propose our novel scheme which can indeed provide real two-factor security, PFS, KCI, and inside attack resilience and revocability of the client. Furthermore, our performance analysis shows that our scheme has reduced modular exponentiation operation and multiplication for both the client and the server compared to Liu et al.’s scheme which reduces the execution time by one third for security levels of λ = 128 . Moreover, in order to cope with the potential threat of quantum computers, we suggest using lightweight XMSS signature schemes which provide the desired security properties with λ = 128 bit postquantum security. Finally, we prove the security of our proposed scheme formally using both the real-or-random model and the ProVerif analysis tool.

Rotating behind security: an enhanced authentication protocol for IoT-enabled devices in distributed cloud computing architecture

With the continuous progress of the Internet of Things (IoT) technology, IoT devices have gradually penetrated all aspects of life. However, due to rapid data growth, IoT devices with limited memory resources cannot store massive data. Cloud computing is an Internet-centric network that can provide security services and data storage. The combination of IoT devices with cloud computing considerably promotes resource sharing, facilitates users’ access to information at any time, and provides users with corresponding cloud computing services on time. Because the information transmitted through public channels is easily intercepted, tampered with, and eavesdropped on by malicious attackers. As a result, users’ and servers’ private information are disclosed. Numerous scholars have proposed different authentication protocols in this environment to protect the communications between users and servers. Amin et al. devised a smart card based authentication protocol. Unfortunately, Kang et al. demonstrated that their protocol was insecure. Huang et al. discovered Kang et al.’s improvement also has security flaws and then designed a protocol to enhance security. In this paper, we first show that Huang et al.’s protocol cannot resist privileged insider and temporary value disclosure attacks. Afterward, we propose an enhanced protocol based on their protocol. Finally, we use formal/informal security analysis to demonstrate the security of the improved protocol. The comparison results are indicated that our protocol has lower computational and communication costs under the same security level.

Introducing Autonomous Shuttle Services Based on Travel Patterns for the Elderly

The transportation-disadvantaged population is rapidly increasing through aging; moreover, the mobility of the elderly has increased due to life expectancy improvements and lifestyle changes. To respond to these changes, a customized mobility service specifically tailored to the travel patterns of the elderly was developed in this study. In particular, an autonomous shuttle service that can improve the operational efficiency and the accessibility of existing transportation methods was proposed by considering the travel characteristics of elderly people, who mainly travel short distances. To this end, a study was conducted in Seongnam City, Republic of Korea, where mobility support services for the elderly are insufficient. Using smart card data, the elderly were classified according to their travel patterns, with autonomous shuttle routes suggested for each travel purpose. We derived four clusters via the Gaussian mixture model clustering, with travel purposes classified according to the spatiotemporal travel patterns. Finally, the major routes for each travel purpose were selected, and feasible road paths for autonomous shuttle operation were suggested using the concept of ODD (operational design domain). This holistic methodology is expected to contribute to the development of autonomous mobility services for the elderly and for the establishment of welfare policies based on the smart card data.

Nonlinear effects of built environment features on metro ridership: An integrated exploration with machine learning considering spatial heterogeneity

This study explored the nonlinear effects of built environment features on metro ridership and proposes an analytical framework that integrates a gradient boosting decision tree with spatial calibration and validation. Station-level boarding and alighting ridership at different times of the day was obtained from smart card records and used as the dependent variable. Nineteen independent variables, including land use, were calculated based on the directional and size-various catchment area defined by shared bike's origin-destination data. This framework, which accounts for spatial heterogeneity demonstrated strong goodness-of-fit and prediction capability, which has been ignored in previous studies. Furthermore, the proposed framework contributed to modeling based on geographical weighted regression and global machine learning models. Local relative importance mapping of built environment variables revealed varying impacts across Shanghai, diverging from the common practice of averaging into a single value in global machine learning models. Additionally, the nonlinear relationship between influencing variables, such as leisure and shopping, demonstrated a positive trend with boarding and alighting ridership in different periods, and spatio-temporal heterogeneity with the effective range and threshold effect. Rather than focusing on increasing development density to boost metro ridership, this study assesses the saturation of station-level built environment to enable more accurate decision-making based on location, station design, station-area planning, and investment priorities in urban areas.

Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study

COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea – a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators – from perspectives of trip frequency, spatial diversity, and travel range – to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors’ mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors’ transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.

Inferring urban polycentricity from the variability in human mobility patterns

The polycentric city model has gained popularity in spatial planning policy, since it is believed to overcome some of the problems often present in monocentric metropolises, ranging from congestion to difficult accessibility to jobs and services. However, the concept ‘polycentric city’ has a fuzzy definition and as a result, the extent to which a city is polycentric cannot be easily determined. Here, we leverage the fine spatio-temporal resolution of smart travel card data to infer urban polycentricity by examining how a city departs from a well-defined monocentric model. In particular, we analyse the human movements that arise as a result of sophisticated forms of urban structure by introducing a novel probabilistic approach which captures the complexity of these human movements. We focus on London (UK) and Seoul (South Korea) as our two case studies, and we specifically find evidence that London displays a higher degree of monocentricity than Seoul, suggesting that Seoul is likely to be more polycentric than London.

Key Agreement Scheme Based on Smart Cards

An efficient roaming service over wireless networks is essential for mobile users. It allows mobile users to seamlessly access the services provided by the home agent without losing connectivity when they visit a foreign network. This handover communication happens with the help of a foreign agent. In most cases, the communication between the mobile user and the foreign agent occurs over an unsecured channel. Therefore, researchers have proposed various authentication schemes to protect data transmitted over this unsecured channel. Most of the proposed schemes are focused on key agreement schemes. However, the key agreement schemes researchers have submitted are primarily high in computational and communication costs. Therefore, this research proposed an authenticated key agreement scheme based on passwords and smart cards with lower computational and communication costs without compromising the scheme’s security. This criterion was achieved due to using lower-cost operations and functions in the scheme. Moreover, the scheme’s development is based on the result of analyzing and improving other schemes proposed by other researchers.

Public Transit Passenger Profiling by Using Large-Scale Smart Card Data

The term “user persona” recently has become more popular and can reflect the characteristics and needs of each user. To analyze the individual characteristics of each passenger in order to better implement targeted transportation policies, a method to mine travel profiles from each passenger based on their smart card transaction records is proposed, mainly mining six labels of passengers: activity, loyalty, whether they are stable commuters, where they live, where they work, and how they prefer to travel. A case study was conducted in the Huitian area of Beijing, the largest community in Asia, which demonstrated the high practicality and accuracy of the passenger profiling method. The results show that 33.06% of passengers were given the stable commuter label and were more likely to have high activity and loyalty labels, whereas 66.94% of other passengers were more likely to have low activity and loyalty labels; 88.9% of passengers were given the residence label and 67.71% of stable commuters received the workplace label, with an accuracy rate of over 90% according to a travel survey. In addition, the application of passenger labels during the design of demand responsive transit (DRT) was discussed to illustrate how to use the labels to improve the efficiency of DRT. The proposed passenger profiling method is applicable to the data mining of passenger travel labels in a simple and accurate way, and can help public transport service providers and researchers to study individual passenger characteristics and provide a theoretical basis for transit network planning and personalization measures.

A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data

The modelling of mode and route choices of public transport passengers is an essential component of travel demand modelling and transportation planning. Traditionally, choice models are trained using data from revealed and stated preference surveys which are not only cost and time intensive but also suffer from bias due to limitations in sample size. In this article, we present a novel approach utilizing a combination of emerging data sources for non-private modes, namely public transit smart card data, taxi GPS trajectory data, and taxi trips transaction data in order to calibrate an integrated taxi and transit mode and route choice model. We apply our approach for a case study of the taxi and public transport system of Singapore using data sets obtained from the local transport authorities. We solve the first mile and last mile data gaps by overlaying the individual transport nodes against geospatial land use data in order to identify their actual origin and destination points of each journey. To model the behavioural inter-dependencies between the choice of taxi and transit options, we tested a two-level nested logit model and a cross-nested logit model. Along with the commonly included exploratory variables such as in-vehicle travel time, transfer time and number of transfers, our model also incorporates differences in travel cost and separate mode specific constants for peak and off-peak periods. Generic and mode-specific in-vehicle travel time and cost coefficients are tested in the utility functions for the transit and taxi alternatives. Willingness to pay estimates are calculated and compared against similar estimates from Singapore. We also present an application of the model by predicting the mode split between taxi and transit under selected transit and taxi fare change scenarios. Our modelling methodology is highly generalizable and can be applied to other cities with similar data availability.

Efficient Authentication in RFID Devices Using Et Als Algorithm

Abstract: Security plays a vital role during the transmission of private data from one sender to the other. Although there are many security algorithms implemented but here we are providing the security algorithms on the RFID devices. The authentication techniques implemented in RFID is based on the new algorithm based on smart cards. The data send through the tags can be made secure using the proposed algorithm so that the unauthorized users can’t access the data without any further unique numbers

Critical success factors of smart card technology in South African public hospitals

Background: Even though the government has set several admirable targets for raising the standard of healthcare, as highlighted by communities and media reports, public health institutions’ services continue to fall short of patients’ expectations and basic standards of care. For this reason, the general public has lost faith in the healthcare system. The public healthcare system in South Africa is completely dysfunctional and urgently needs to be transformed to serve the majority of those who use public hospitals.Objectives: The study aimed to improve healthcare for the majority of South Africans by investigating the critical success factors (CSFs) that influence the adoption of smart card technology (SCT) in South African public hospitals.Methods: A thorough review of peer-reviewed literature was conducted to determine potential barriers to adopting SCT. Furthermore, a hybrid model that combines the Health Unified Technology of Acceptance Theory (HUTAUT) model, DeLone and McLean IS success model (DM) and the diffusion of innovation (DOI) theory will be developed, validated and tested to identify the CSFs adoption of SCT in public hospitals in South Africa.Results: The validated research model has been developed to be adopted by nurses at public hospitals.Conclusion: This research will contribute to the development of a new framework that identifies the CSFs for SCT adoption in South African public hospitals.Contribution: The study’s results will make a special contribution to the body of knowledge in the fields of health informatics, particularly e-health.

Smart Billing System Using Arduino

Abstract: The modern technology has increased the standard of living for the humans. With the advancement of technology, the level of acceptance of people of all ages towards electronic gadgets is increasing steadily. Electronic gadgets such as barcode scanner smart card reader and RFID scanner are gaining more and more usage in various kinds of industries. So with the help of these we came up with this innovative project is of an automated billing system which can be placed at a side where the customer is flexible to use at any time. This automated payment system consists of a RFID reader which is controlled by Arduino. So, whenever the customer places the tag it is detected by the RFID module and is displayed on LCD along with the price of the item. As the customer goes on adding items, all items are detected by the module and therefore the price will increase accordingly. In case if customer changes his/her mind and doesn’t want any item added to the bill he/she can remove it and the price added will be deducted automatically. At the end of the entire process the total amount to be paid will be displayed. We can also pay the bill using the mobile application in the app itself. The mobile application is operated by the Wi-Fi module used in the hardware implementation. After the payment the bill will generate successfully. Hence this technique is an appropriate method to be used in places like hotels so this will help in reducing manpower and helps in making a better billing experience for customers.

Auto-Identification Technologies in Academic Libraries

The study focuses on the importance and advantages of various auto-identification technology which are used in academic libraries. Libraries are not safe and secure and facing many problems, like theft, mutilation of library materials and other unethical losses. Auto-identification technologies are useful for libraries to solve those problems. There are various Auto-identification technologies which are used in libraries, like bar codes, smart cards, bio-metric, GPRS, and radio frequency identification (RFID). In this paper, studied some auto-id technology and benefits and challenges of these technologies.