Table 2.

Comparison of existing works

Work	Methodology	Inferences
[9]	Application using mobile and fog computing, privacy-preserving e-government framework to trace and prevent COVID-19 community transmission	Ensures data privacy, security, optimization of data communication, low power consumption, and also enhances efficiency in terms of cost, network delay, and energy consumption
[2]	IoT-based tracing framework. Anonymized RFID contact tracing of Infection spread. Blockchain technology is used for data storage to ensure privacy	Secure and efficient for contact tracing The identity privacy problem is protected by the combination of zero-knowledge proof and key escrow. By the connection of unique cryptographic identity and on-chain proof-of-location commitment is decoupled such that it is almost impossible to track and identify the person
[13]	A peer-to-peer system of a blockchain protocol, used for contact tracing. Provides users with a unique ID, transparent data storage, location proofing, and zero-knowledge proof-based data ownership authorization	Does not require trusted third-party services and centralized servers and ensures the anonymity of users
[14]	A decentralized approach for contact tracing	Secure storage and Efficient for contact tracing. APIs used to support applications developed by governments, health workers intended to work seamlessly
[15]	Machine learning used for screening, prediction, forecasting, contact tracing, and drug development for SARS-CoV-2	Requires large amounts of data to achieve higher efficiency Training of data might take a long time