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Performance evaluation of a lightweight consensus protocol for blockchain in IoT networks.

Kaur Manpreet, Gupta Shikha

Computer Science

2025

T. 26 (1)

27--47

The consensus protocol is essential in practically every blockchain application. Most of these existing blockchain consensus protocols need massive computa-tional capabilities, substantial energy consumption, and dependency on monetary stakes. These shortcomings in the mainstream consensus approach leadto their unsuitability for low-resource applications like IoT. As a result of thiswork, a lightweight consensus process referred as Delegated Proof of Accessibility (DPoAC) is implemented and evaluated. DPoAC makes use of Shamir secretsharing, Proof of Stake (PoS) with random selection, and the Inter-PlanetaryFile System (IPFS). The DPoAC operation is composed of four modules: secret generation and distribution, retrieval of secret shares, block creation and verification, and block rewards and penalty. A detailed description of DPoAChas been provided and implemented in JavaScript and experimental resultsdemonstrate that our solution meets the necessary performance and securityrequirements for a lightweight scalable protocol for IoT systems.

Sentiment-aware enhancements of pagerank-based citation metric, impact factor, and h-index for ranking the authors of scholarly articles

Gupta Shikha, Kumar Animesh

Computer Science

2024

T. 25 (2)

173--209

Heretofore, the only way to evaluate an author has been frequency-based citation metrics that assume citations to be of a neutral sentiment. However, considering the sentiment behind citations aids in a better understanding of the viewpoints of fellow researchers for the scholarly output of an author. We present sentiment-enhanced alternatives to three conventional metrics namely Impact Factor, H-index, and Page Rank-based index. The proposal studies the impact of the proposed metrics on the ranking of authors. We experimented with two datasets, collectively comprising almost 20,000 citation sentences. The evaluation of the proposed metrics revealed a significant impact of sentiments on author ranking, evidenced by a weak Kendall coefficient for the Author Impact Factor and H-index. However, the Page Rank-based metric showed a moderate to strong correlation, due to its prestige-based attributes. Further more, a remarkable Rank-biased deviation exceeding 28% was seen in all cases, indicating a stronger rank deviation in top-ordered ranks.