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Model of a blockchain-based social contact tracking system in metropolitan subway systems: an evaluation
 
Код статьиS207751800022481-8-1
DOI10.18254/S207751800022481-8
Тип публикации Статья
Статус публикации Опубликовано
Авторы
Тарханов Иван Александрович 
Должность:  заведующий лаборатории
Аффилиация:
Государственный академический университет гуманитарных наук
Федеральный исследовательский центр "Информатика и управления" РАН
Адрес: Российская Федерация, Москва
Шмелев Илья Александрович
Должность: аспирант
Аффилиация: НИТУ "МИСИС"
Адрес: Российская Федерация, Москва
Космарский Артём Анатольевич
Должность:  заместитель заведующего лаборатории
Аффилиация: Государственный академический университет гуманитарных наук
Адрес: Российская Федерация, Москва
Название журналаИскусственные общества
Выпуск
Аннотация

Общественный транспорт является основным источником распространения COVID-19 в мегаполисах. В данной статье обсуждается концептуальная модель системы отслеживания социальных контактов COVID-19 в метро, основанная на эксклюзивном блокчейне (DLT). Подробно рассмотрена архитектура основных компонентов системы: алгоритм записи контактов, уведомления о заражении и анализа рисков, процесс публикации информации о зараженном человеке в сети. Для проверки адекватности модели предлагается использовать ранее предложенный метод моделирования пиковой нагрузки. Proof-of-stake (метод защиты) основан на данных о реальных пиковых нагрузках в московском и нью-йоркском метро. Этот расчет показывает применимость данного метода для крупнейших мегаполисов мира, обосновывает технические характеристики узлов сети блокчейн.

 

Ключевые словаCOVID-19, блокчейн, отслеживание контактов, масштабирование
Источник финансированияThe article was prepared at the State Academic University for Humanities within the state assignment of the Ministry of Science and Higher Education of the Russian Federation (topic No. FZNF-2020-0014).
Получено04.10.2022
Дата публикации18.12.2022
Кол-во символов20852
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Оглавление

1. Introduction
2. Conceptual model of the contact-tracking system
2.1 An algorithm of contact recording
2.2 Infection notifications and risk analysis
2.3 The process of publishing information about an infected person in the network
3. Network load evaluation method
4. The results of the calculations
4.1 The calculations for the Moscow subway
4.1 The calculations for the New York subway
5. Discussion
6. Conclusion
1

1. Introduction
2

The COVID-19 pandemic, which started rapidly in 2020 and has been unraveling ever since, has globally changed the way people live. According to the latest research, it will not disappear from our lives in the coming years [7]. One effective way to control the spread of COVID-19, as well as other virus-borne diseases, is to track social contacts. Numerous contact tracing studies and applied projects of varying degrees of sophistication have appeared in 2020 -2022 [6, 8, 9]. A number of approaches involve a high degree of human involvement (e.g., in confirming whether or not a person visited a certain place) [6]. Others include modern technological and digital tools using Internet of Things (IOT) [10], radio frequency identification (RFID) [9], Bluetooth low energy (BLE) [11], [12] distributed ledger technology (DLT) [10], etc. Blockchain (alternatively called Distributed Ledger Technology, hereinafter DLT) is one of the most promising tools to ensure the immutability and transparency of statistical data, as a repository. Closer to the medical informatics, the papers [13], [14], [15], [16], [17] consider DLT as a key part of a decentralized system for tracking social contacts between people in order to control the spread of the disease. However, these papers pay little attention to the problem of adapting it to the real workload – an important problem to be solved. As such systems are designed to handle large amounts of data, the problem of DLT scaling is obviously relevant [14]. To assess the feasibility of using such systems in everyday life, it is useful to make an assessment of scalability based on real data. Hence the objectives of this paper:

  1. To describe the model of a contact tracking system built on an exclusive blockchain [18] in detail in contrast to [2];
  2. To apply previously used a methodology [2] to assess the scalability of such systems using blockchain technology as an independent repository of information on contacts with those who fell ill;
  3. To conduct calculations for the mega-cities (Moscow and New York), in order to confirm the applicability of the proposed method (proof-of-state) on the basis of real data on the loads during peak hours in the subway.
3 The paper is structured as follows: section 2 provides the conceptual model of a DLT-based social contact tracking system, identifies the methods and algorithms of its main parts, which will allow a better understanding of the proposed scaling estimation methodology. Section 3 describes the scaling estimation calculation methodology itself and its parameters. Then, in Section 4 we perform a calculation using open-source statistics on subway congestion. Section 5 discusses the results and the limitations of the proposed research. At the end of the paper the results of the study and the obtained results are summarized.

2. Conceptual model of the contact-tracking system
4

The data storage for the system in question is an exclusive blockchain [18] controlled by a consortium of individuals responsible for the platform. The detailed benefits of using exclusive blockchain have been described previously [2]. The solution proposed in this paper is implemented on a blockchain framework that supports smart contracts, DPoS (Delegated Proof-of-Stake) or PoA (Proof-of-Authority) consensus algorithms and rights customization for an exclusive blockchain. For example, they could be Substrat, Exonum, Cosmos SDK, etc.
5
11

Fig. 1 The model of the proposed software solution
6 Further consider the model of our hybrid software solution, which is demonstrated on Figure 1.
7

The conceptual model proposed here is largely based on [10] and [2], which considers a network with IoT devices using BLE technology. These devices act as witnesses of the physical presence of the user in a particular location. The process consists of 5 main steps:

  1. Registering a social contact in a specific location where the IoT device is installed;
  2. Information from this device is recorded on the DLT;
  3. Testing a person for COVID-19 and identifying the fact of infection;
  4. Making information about the infection accessible online;
  5. Notifying the people who have visited the location.
8

In the proposed conceptual scheme it is worth paying attention to the following number of key components [2]:

  1. An algorithm of contact recording;
  2. Notification of infection and infection risk analysis;
  3. The process of recording information about the infected person in the network.

2.1 An algorithm of contact recording
9 Similar to [5], in the proposed model, we do not directly record the contacts between users’ devices, in order to make it easier to provide the necessary level of privacy to the users. Instead of the interaction between two anonymous devices, in this approach communication is performed by:
  • A publicly identified device – an IoT device with BLE technology, belonging to a particular organization;
  • A user's device that is not identified in any way, in order to preserve anonymity.
10 The identifiability of the IOT device is ensured by asymmetric cryptography:
  • The organization’s public key is available in open access;
  • The organization’s private key is available only to the organization; using that key the IoT device creates a signature when sending its messages, so that the receiving device can verify the device’s access to the system.

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