The healthcare system across the globe has been under colossal pressure since the emergence of novel coronavirus pandemic. The pandemic has also unveiled some of the greatest gaps in the existing healthcare systems. The research paper authored by Dr Ashok Kumar Pradhan and his PhD student E Bhaskara Santhosh, Department of Computer Science and Engineering, proposing a blockchain-based pandemic detection model was recognised by WHO and the paper was listed in COVID-19 Global literature on coronavirus disease. The paper titled “iBlock: An Intelligent Decentralised Blockchain-based Pandemic Detection and Assisting System” was published in collaboration with Saraju Mohanty, University of North Texas and Dr Venkata Ramana Badarla, Associate Professor, IIT Tirupati. The authors have expressed their deepest gratitude to Science and Engineering Research Board (SERB) for Grant number TAR/2019/000286 and SRM University-AP for supporting this work.

Abstract of the Research

The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models become inadequate in handling the pandemic mitigation process in a real-time dataset. This is because, the present models are server-centric and controlled by a single party, hence to manage confidentiality, integrity, and availability (CIA) of dataset is a challenging task. Therefore, a decentralised user-centric model is essential, where the CIA of user data can be assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system named as (iBlock) that uses robust technologies like hybrid computing and IPFS to support system functionality. Moreover, a pseudo-anonymous personal identity is suggested using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML technology.

Contributions of the Research

i) Proposes a novel architecture model for pandemic detection and alertness using a blockchain called as “iBlock”. It supports sharing of real-time data utilization.
ii) The proposed system introduces suitable privacy and security mechanisms to cover system-level data privacy and security.
iii) It also suggests a logical combination of blockchain and AI/ML on hybrid computing to support global level requirements during pandemic mitigation and alerting the systems.

Social Implications

The proposed system helps in the early detection of Covid-19 and encourages people to use their health data anonymously in pandemic detection and mitigation process. Moreover, iBlock maintains all crucial data on blockchain for future sustainable healthcare solutions. The majority of pandemic detection and alerting systems are limited to prediction, however iBlock further simplifies the area-labelling to cover area wise mitigation mechanisms. The classification of areas helps the government and healthcare organizations to plan sustainable preventive measures in a real-time scenario. It also helps in prediction of new cases and death rates with the aid of a dedicated AI/ML detection engine module. To motivate the people to share legitimate data, the proposed model even suggests a reward mechanism to influence the participants.

The research investigates the advanced possibilities in smart healthcare architecture to bring down the time and effort for pandemic mitigation activities. Read to know more.

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Soha Muskaan Sayyad, Samah Maaheen Sayyad, Juhita Naga Priya Velagapudi, and Trisha Chilukuri from 2nd-year Computer Science Engineering have achieved 2nd place in the I-Wallet Design Techfest, organised by IIT Bombay. Dr Sonam Maurya, Assistant Professor, Department of Computer Science and Engineering, guided the students to achieve this incredible feat.

The students started their work in December and explored a lot about IoT while working on it. More than 100 teams participated in this competition. In January, ten teams got selected for the second round, i.e., Report Submission, and our students were one of them. In February, based on the reports submitted, six teams, including our students, were selected for the final round, i.e., to present the proposed idea to the judges’ panel. Finally, the students bagged second place in the competition with cumulative prize money of INR 90,000.

Regarding the I-Wallet, students say that “Smart Wallet is an easy-to-use wallet that is designed for providing secured transactions”. It has minimal energy consumption, more security, and a few advanced features, some of which are not included in the regularly used wallets. The advanced IoT features enable it to be kept in our pockets with smartphones and smartwatches. The design received recognition from the expert panel members based on these features.

The team has also initiated the process to file a patent on the design of wallet. Students expressed their gratitude to Dr Sonam Maurya, Assistant Professor, Computer Science and Engineering, for her relentless guidance and continuous support towards the project. They also thanked SRM University-AP for providing the opportunity to participate in this national level competition organised by IIT Bombay.

Prof B V Babu, Dean, School of Engineering and Sciences, appreciated the students and their faculty mentors. “Keep up the good work, continue to excel in all your future endeavours at professional and personal levels and bring laurels to your alma mater”, he said in the congratulatory message.