Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

▓ Local Randomness for True Random Number generators ▓ Non-local Randomness for distribution of Cryptographic Keys ▓ Towards faster, longer distances and cheaper QKD engines ▓ Quantum Repeaters ▓ Device-Independent Quantum Information Processing

Quantum communication

The Internet of Things (IoT) is the ability to provide everyday devices with a way of identification and another way for communication with each other. The spectrum of IoT application domains is very large including smart homes, smart cities, wearables, e-health, etc. Consequently, tens and even hundreds of billions of devices will be connected. Such devices will have smart capabilities to collect, analyze and even make decisions without any human interaction. Security is a supreme requirement in such circumstances, and in particular authentication is of high interest given the damage that could happen from a malicious unauthenticated device in an IoT system. This paper gives a near complete and up-to-date view of the IoT authentication field. It provides a summary of a large range of authentication protocols proposed in the literature. Using a multi-criteria classification previously introduced in our work, it compares and evaluates the proposed authentication protocols, showing their strengths and weaknesses, which constitutes a fundamental first step for researchers and developers addressing this domain.

A Survey of Internet of Things (IoT) Authentication Schemes

With the exponential growth of the Industrial Internet of Things (IIoT), multiple outlets are constantly producing a vast volume of data. It is unwise to locally store all the raw data in the IIoT devices since the energy and storage spaces of the end devices are strictly constrained. self-organization and short-range Internet of Things (IoT) networking also support outsourced data and cloud computing, independent of the distinctive resource constraint properties. For the remainder of the findings, there is a sequence of unfamiliar safeguards for IoT and cloud integration problems. The delivery of cloud computing is highly efficient, storage is becoming more and more current, and some groups are now altering their data from in house records Cloud Computing Vendors' hubs. Intensive IoT applications for workloads and data are subject to challenges while utilizing cloud computing tools. In this report, we research IoT and cloud computing and address cloud-compatible problems and computing techniques to promote the stable transition of IoT programs to the cloud.

pdf/iot-and-cloud-computing-issues-challenges-and-opportunities-43c4707r5n.pdf

IoT and Cloud Computing Issues, Challenges and Opportunities: A Review

Radio frequency identification (RFID) and wireless sensors networks (WSNs) are two fundamental pillars that enable the Internet of Things (IoT). RFID systems are able to identify and track devices, whilst WSNs cooperate to gather and provide information from interconnected sensors. This involves challenges, for example, in transforming RFID systems with identification capabilities into sensing and computational platforms, as well as considering them as architectures of wirelessly connected sensing tags. This, together with the latest advances in WSNs and with the integration of both technologies, has resulted in the opportunity to develop novel IoT applications. This paper presents a review of these two technologies and the obstacles and challenges that need to be overcome. Some of these challenges are the efficiency of the energy harvesting, communication interference, fault tolerance, higher capacities to handling data processing, cost feasibility, and an appropriate integration of these factors. Additionally, two emerging trends in IoT are reviewed: the combination of RFID and WSNs in order to exploit their advantages and complement their limitations, and wearable sensors, which enable new promising IoT applications.

/pdf/a-review-of-iot-sensing-applications-and-challenges-using-25y8w5njex.pdf

A review of IoT sensing applications and challenges using RFID and wireless sensor networks

The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method.

An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor.

Currently, Internet of Things (IoT) technologies are used in many areas, such as intelligent transportation, smart city, hospital, games, education. Earlier interactive response system uses infrared or radio frequency (RF) wireless communication technologies to transmit the students’ answer to teachers’ managerment system, where there exists high cost, inconvenient usage, difficult deployment. How to use IoT to improve the quality of higher education becomes a very important topic in the researh area of teaching. Radio Frequency Identification (RFID) is one of key technologies to implement IoT applications, and most of universities use the High Frequency (HF) RFID card as the students’ identification devices in China. In this paper, a kind of WiFi supported RFID reader (WiRF) is implemented using open source hardware platforms, such as Node MCU and RFID-RC522. Then the proposed WiRF system is used to assist teacher to perform automatic attendance record and students’ behavior record. In addition, Quick Response (QR) code is another technology to enable IoT. In this paper, QR code is designed to quickly access course video and perform real-time interactive response in the classroom, which will provide multidimensional learning and strengthen the motivation of students’ learning. This IoT system can improve the attendance of students, and give a positive impact on students’ learning process for higher education.

/pdf/teaching-management-system-with-applications-of-rfid-and-iot-50izk4rz2m.pdf

Teaching Management System with Applications of RFID and IoT Technology

Nowadays, location-based services, which include services to identify the location of a person or an object, have many uses in social life. Though traditional GPS positioning can provide high quality positioning services in outdoor environments, due to the shielding of buildings and the interference of indoor environments, researchers and enterprises have paid more attention to how to perform high precision indoor positioning. There are many indoor positioning technologies, such as WiFi, Bluetooth, UWB and RFID. RFID positioning technology is favored by researchers because of its lower cost and higher accuracy. One of the methods that is applied to indoor positioning is the LANDMARC algorithm, which uses RFID tags and readers to implement an Indoor Positioning System (IPS). However, the accuracy of the LANDMARC positioning algorithm relies on the density of reference tags and the performance of RFID readers. In this paper, we introduce the weighted path length and support vector regression algorithm to improve the positioning precision of LANDMARC. The results show that the proposed algorithm is effective.

An RFID Indoor Positioning Algorithm Based on Support Vector Regression

With the fast development of the Internet of Things, Radio Frequency Identification (RFID) has been widely applied into many areas. Nevertheless, security problems of the RFID technology are also gradually exposed, when it provides life convenience. In particular, the appearance of a large number of fake and counterfeit goods has caused massive loss for both producers and customers, for which the clone tag is a serious security threat. If attackers acquire the complete information of a tag, they can then obtain the unique identifier of the tag by some technological means. In general, because there is no extra identifier of a tag, it is difficult to distinguish an original tag and its clone one. Once the legal tag data is obtained, attackers can be able to clone this tag. Therefore, this paper shows an efficient RFID mutual verification protocol. This protocol is based on the Physical Unclonable Function (PUF) and the lightweight cryptography to achieve efficient verification of a single tag. The protocol includes three process: tag recognition, mutual verification and update. The tag recognition is that the reader recognizes the tag; mutual verification is that the reader and tag mutually verify the authenticity of each other; update is supposed to maintain the latest secret key for the following verification. Analysis results show that this protocol has a good balance between performance and security.

https://www.mdpi.com/1424-8220/18/3/760/pdf

A Lightweight RFID Mutual Authentication Protocol Based on Physical Unclonable Function

Ultra-high frequency radio frequency identification (UHF RFID) technology has been widely used in many areas, and RFID localization becomes a research hotspot. There are many kinds of research on absolute localization; however, due to some disadvantages of absolute localization, relative localization is more effective in some situations. At present, there are some problems with relative localization: existing methods have low localization accuracy, and it is difficult for them to deal with high-density tags. Aiming at these problems, this paper proposes PRDL: relative localization method of RFID tags via phase and RSSI based on deep learning. By using deep learning, the variation characteristics of RFID phase and RSSI are extracted with limited data accuracy conditions. On this basis, we can infer the relative positional relationship of RFID tags with high accuracy, and design the corresponding sorting algorithm to obtain the sequence arrangement. PRDL has experimented with bare tags and actual books, and the experimental results show that PRDL can achieve better results than the traditional relative localization methods. A series of tests also showed that PRDL has good robustness and generalization ability.

/pdf/prdl-relative-localization-method-of-rfid-tags-via-phase-and-397h62kghk.pdf

PRDL: Relative Localization Method of RFID Tags via Phase and RSSI Based on Deep Learning

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