Repository logo

Browsing by Author "Gupta, Shikha"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Performance evaluation of a lightweight consensus protocol for blockchain in IoT networks
    (Wydawnictwa AGH, 2025) Kaur, Manpreet; Gupta, Shikha
    The consensus protocol is essential in practically every blockchain application. Most of these existing blockchain consensus protocols need massive computational capabilities, substantial energy consumption, and dependency on monetary stakes. These shortcomings in the mainstream consensus approach lead to their unsuitability for low-resource applications like IoT. As a result of this work, a lightweight consensus process referred as Delegated Proof of Accessibility (DPoAC) is implemented and evaluated. DPoAC makes use of Shamir secret sharing, Proof of Stake (PoS) with random selection, and the Inter-Planetary File 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 DPoAC has been provided and implemented in JavaScript and experimental results demonstrate that our solution meets the necessary performance and security requirements for a lightweight scalable protocol for IoT systems.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Sentiment-aware enhancements of PageRank-based citation metric, Impact Factor, and h-index for ranking the authors of scholarly articles
    (Wydawnictwa AGH, 2024) Gupta, Shikha; Kumar, Animesh
    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 PageRank-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 PageRank-based metric showed a moderate to strong correlation, due to its prestige-based attributes. Furthermore, a remarkable Rank-biased deviation exceeding 28% was seen in all cases, indicating a stronger rank deviation in top-ordered ranks.