Radio Frequency Identification (RFID) System

Radio Frequency Identification (RFID) administrationLiterature review2.1 RFIDThe Radio Frequency Identification (RFID) dodge is a technology for automatedidentification. Exploration of RFID technology dates back to 1948 when Harry Stockman published his inquiry titled Communication by means of the reflected power. Unfortunately technologies such asintegrated circuits, transistors and micro fulfillors were non yet available and RFID had to wait an early(a)20 years for its first commercial application (Landt 2005). Between 1970 and 1980 several researchlaboratories and academic institutions carried out work on RFID implementations for animal tracking,theft prevention, item labelling and access control systems (Want 2006). Regardless of theseapplications, RFID systems remained obscure for many years. The first signifi chiffoniert smorgasbord to thisoccurred in the early nineties when companies across the world began to use RFID tags on a large scaledue advancements in their energy efficiency and coat reductions (Landt 2005).Todays systems are usually composed of either passive or active RFID tags and RFID readers.Active tags contain their own power source and thereby shag mail stronger signals and tail beaccessed from further distances. Most commonly they operate on the ultra-high frequency (UHF) bandand puke achieve up to 100 metres range depending on the surrounding environment (Weinstein 2005).There are currently two types of active tags. Transponders, also called semi-active tags, and Beacons.Transponders stay in standby mode until receiving signal from the reader and thus transmit a signalback. Beacons emit signals and advertise their presence at pre-set intervals. Because of their on boardpower source, active tags are expensive, priced from $20 to $70 and shift in size from 2 cen epochtresupwards (Williams et al. 2014). Passive tags do not incorporate a power supply and are powered by theelectromagnetic signal received from the reader through th e tags antenna. They operate on low, highand ultra-high frequency with signals ranging up to 10 metres depending on the tags backscatter power(Weinstein 2005). The smallest passive tags can be size of a grain of rice and cost 1/10 of the price ofthe active tag (Williams et al. 2014).Silva, Filipe and Pereira (2008) proposes a RFID based student attending recording systemthat comprises of RFID readers operating at the 125 Kilohertz (KHz) frequency with an effective readrange up to 10 15 centimetres and passive RFID tags enter into plastic cards. The tags store abinary identifier which is unique to each(prenominal) student. Readers are connected to the local network with RJ45connector through which they transfer scanned tag id to the horde using the Transmission ControlProtocol / Internet Protocol (TCP/IP). At least one reader is mounted in each of the classrooms andstudents need to bear off their card out and place it near the reader in order to register their attendance.Nainan , Parekh and Shah (2013) claimed that a similar RFID attendance adaptation system setup fall the time needed to record a students attendance by 98% compared to the manual entrymethod. Collected data shows that the RFID system was able to record the attendance of 5 students per flash, however considering the short effective read range we have to conclude that multiple readerswere used during that experiment to achieve such result. Despite advances everyplace the paper basedregisters, efficiency of attendance systems based on passive RFID tags is limited by the number ofreaders located in the classroom. Analogous systems based on the active RFID technology couldincrease ids collection efficiency by scanning multiple tags simultaneously from a further distance(Yoon, Chung and Less 2008), however such systems would introduce a number of additionaltechnological and social issues. Bandwidth limitations coerce RFID tags to dowry a common broadcastfrequency and as a force multiple tags responding concurrently to the same reader can causepacket collisions. Therefore to solve these issues, advanced anti-collision algorithms and methods mustbe employed during development process (Bin, Kobayashi and Shimizu 2005). Increased reading rangeadditionally raises serious privacy concerns as the users location could be tracked without their ownconsent (Ferguson, Thornley and Gibb, 2014).2.2 BiometricsNumerous properties must be satisfied to categorise the biological measurement of a humanphysiological or behavioural characteristic as biometrics. The characteristics should be unique, every psyche should have it and it needs to be tender so it can be measured. There are a number of differentstudies exploring biometric authentication for attendance registration systems.2.2.1 Voice actualisationRecent experiments by Dey et al. (2014) explore the capabilities of an attendance registrationsystem based on voice recognition. The main core of the system is a Linux OS innkeeper int egrated with acomputer telephony interface (CTI) card and pre-installed with interactive voice response (IVR)software. The server is accessible only from the previously pre-defined phones which are installed inthe classrooms. Using installed phones users have to record a reference voice sample to enrol into thesystem. During enrolment users are provided with a unique quadruplet digit speaker identification then theyare asked to read for 3 minutes text of their own choice. Enrolled users can register their attendance by ingress the previously received speaker identification number and then answering some simple randomquestions generated by the system. The system logs user attendance if the save speech matches thestored reference sample. Initial system evaluation performed on the group of 120 students indicatedvery low efficiency. In order to achieve 94.2% recognition rate, each user needs to produce at least a 50seconds sample. Authentication time is additionally extended by an av erage 26 seconds computationaltime needed to analyse provided speech sample. additive limitations come with the maximum numberof 32 concurrent calls that each server can handle. In essence, a long compulsory enrolment process,the unnecessary burden of remembering a personal speaker identification number and the poorregistration efficiency time make the system a poor candidate for large group registers.2.2.2 FingerprintsAccording to Akinduyite et al. (2013) fingerprint attendance management systems can be more bona fide and efficient than the voice based equivalent. They have achieved 97.4% recognition accuracywith an average registration time of 4.29 seconds per student. The system implements fingerprintscanners connected to a centralised server through the existing Wi-Fi infrastructure. As with the voicerecognition system, an administrator has to capture reference fingerprint data from every user beforethe system can be used. Collected fingerprint templets are stored on the server in a Microsoft SQLServer database and later used to match scanned samples. Almost identical recognition rate of 98.57%was achieved by Talaviya, Ramteke and Shete (2013) in the similar fingerprint system setup. Analogousto the RFID based systems, the efficiency is closely related to the total number of the available scanners.2.2.3 machine-driven Face recognitionAll of the prior systems require users to provide a biometric sample manually by using one ofthe available scanners located in the environment. Kawaguchi et al. (2005) proposed a considerablydifferent solution which automates sample collection. They introduced a face recognition method basedon continuous observation. The system requires two cameras blow live data to the centralized unitwith preinstalled face detection and recognition software. The first camera, called the espial camerais installed on the ceiling and points towards the rooms sitting area. The second camera, called thecapturing camera is located in front of the seats to capture students faces. The sensing camera scansover the room in order to detect seats set-aside(p) by the students. Received image data is analysed usingthe Active Student Detecting (ASD) method developed by Nishiguchi et al. (2003). Once a student isdetected, the system directs the capturing camera to the found location. The face image collected fromthe capturing camera is then processed by the system and the students attendance is recorded if amatching template is found. Experiments in which the described system was evaluated on a group of12 students revealed 80% accuracy in engaged seats detection and the same train during face detection.The whole experiment took 79 minutes in which 8 scanning cycles were performed, resulting in 70%total accuracy for the attendance registering. Despite advances in automated biometric samplescollection, the described system seems to be inefficient, especially if we consider time required tocollect and analyse samples on such small group of students. Additional issues may arise if there areany obstructions in the room which can restrict the cameras view or if a low ceiling prevents sensingcamera from covering the entire seating area.2.2.4 SummaryThe biometric systems have many advantages over the other authentication technologies. Thebiometric characteristics are tightly linked to the owner and can prevent identity theft, are difficult toduplicate and are very convenient as they are always available. Despite all these advances, all thebiometric systems share serious ethical, social and security implications. It was evidenced by manyresearchers that there is a fear of biometric technologies on the whole. The individuals and potentialsystem users are concerned to the highest degree privacy, autonomy, bodily integrity, dignity, equity and personalliberty (Mordini and Tzovaras 2012 Kumar and Zhang 2010). The system administrators haveadditional overhead with the security of the collected biometric data. The indi vidual biometriccharacteristic cannot be replaced if they get stolen, hence the legal responsibilities whilst storing thiskind of data are colossal.2.3 Wi-FiAn interesting and novel attendance registration method was proposed by Choi, Park and Yi(2015). The authors created a system which incorporates Wi-Fi technology reinforced into smartphonedevices. They had developed two variances of a smartphone application, one for the lecturers and onefor the students. When a class session starts the lecturer has to create a Wi-Fi Access Point (AP) usinghis version of the application. The students attend the lecture and scan for the available Wi-Fi AccessPoints and if the lecturers AP is discovered and students device stays in its range for specified amountof time then attendance registration process is triggered. To overcome limitations with the maximumnumber of concurrent connections that single AP can handle, the created students version scans onlyfor the nearby networks but never connect s to the found APs. attendance is registered by submitting aMessage Digests 5 (MD5) hash token that combines a Service Set Identifier (SSID) of the found APand students smartphone Media Access Control (MAC) address. The hash token is uploaded to theserver which verifies submitted data and registers the students attendance in the local store. The systemarchitecture requires collection of the reference MAC address of all the students for the purpose of thelater validation. The study does not describe what smartphone models were used throughout theexperiment, but it seems that they did not consider privacy features on iOS devices. According to Apple(2013), since the release of iOS 7.0, the MAC identifier is no longer accessible through third partyapplications, moreover after iOS 8.0 release, real device MAC address is hidden from the access pointsand swapped with a randomly generated one (Apple 2015 A). fetching into account that over 98% ofiOS devices run on iOS 7.0 and above (Apple 2015 B), only confirms that the proposed system designshould be reviewed again.2.4 Other2.4.1 QR Code with face recognitionFadi and Nael (2014) combine biometrics with Quick Response Codes (QR). The proposedmethodology requires lecturers to generate a unique QR code and display it in the class. In order toregister their attendance, students need to download a mobile application, install it on their smartphonesand use it to scan the presented QR code. The scanned code is then submitted to the server via theexisting University Wi-Fi infrastructure. Furthermore the application performs an identity check byscanning the students facial image which is later used to create matching score by analysing a referenceimage stored on the servers. Lecturer can manually validate submitted images to confirm a studentsidentity if a low matching score raises any concerns. The QR code image could be effortlessly forwardedto other students outside the classroom, therefore the system also collects a loc ation stamp on the codesubmission. The apparent vulnerability of the system lies in the number of technologies that it dependson. Authors mistaken that every student will have a smartphone device with front and back facingcameras for the facial images and the QR scans and also a Global Positioning System (GPS) modulewhich will be accessible during the registration stage. Each classroom has to be also equipped with alarge screen to present codes to the students and this may not always be available.