# TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing

arXiv: https://arxiv.org/abs/2108.08275 · HTML: https://mlsanity.com/paper/2108.08275/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 12 of 12)

- [Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs](https://mlsanity.com/paper/2003.11511/) (2020)
- [Memoryless Techniques and Wireless Technologies for Indoor Localization with the Internet of Things](https://mlsanity.com/paper/2005.01877/) (2020)
- [CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans](https://mlsanity.com/paper/2101.00691/) (2021)
- [Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey](https://mlsanity.com/paper/2106.14631/) (2021)
- [PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing](https://mlsanity.com/paper/2004.03544/) (2020)
- [BeepTrace: Blockchain-enabled Privacy-preserving Contact Tracing for COVID-19 Pandemic and Beyond](https://mlsanity.com/paper/2005.10103/) (2020)
- [Blockchain-based Proof of Location](https://mlsanity.com/paper/1607.00174/) (2016)
- [Blockchain Meets Database: Design and Implementation of a Blockchain Relational Database](https://mlsanity.com/paper/1903.01919/) (2019)
- [PriLok: Citizen-protecting distributed epidemic tracing](https://mlsanity.com/paper/2005.04519/) (2020)
- [Blockchain Meets COVID-19: A Framework for Contact Information Sharing and Risk Notification System](https://mlsanity.com/paper/2007.10529/) (2020)
- [BLE Beacons in the Smart City: Applications, Challenges, and Research Opportunities](https://mlsanity.com/paper/2102.08751/) (2021)
- [Microlocation for Smart Buildings in the Era of the Internet of Things: A Survey of Technologies, Techniques, and Approaches](https://mlsanity.com/paper/2104.02659/) (2021)

## Built on by (most cited first, 3 of 3)

- [Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization](https://mlsanity.com/paper/2109.00126/) (2021)
- [JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization](https://mlsanity.com/paper/2205.08422/) (2022)
- [Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion](https://mlsanity.com/paper/2210.15132/) (2022)

## Abstract (arXiv)

Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) credit-based consensus algorithm coupled with Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node's behavior based on its underlying credit-based mechanism. For achieving high localization performance, we capitalize on availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data driven localization model based on Bluetooth Low Energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by implementation of a highly accurate contact tracing model while improving the users' privacy and security.