Research Publications

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    publications.listelement.badgeOpen Access
    ResQNet: A Multi-Layer Hybrid AI Framework for Priority-Aware Disaster Communication over Decentralized Mesh Networks
    (Sri Lanka Institute of Information Technology, 2026-05-21) Perera,W.M.R
    Effective emergency communication is critical during disaster events, yet conventional infrastructure frequently fails through physical damage, power outages, and network congestion precisely when reliable coordination is most needed. This paper presents ResQNet, a decentralized hybrid intelligent framework designed to prioritize and route distress messages under post-disaster network conditions. The system employs a multi-layer processing pipeline integrating rule-based keyword scoring, TF-IDF with Linear Support Vector Classification (LinearSVC), a novel Distress-Aware Priority Evaluation (DAPE) heuristic, fuzzy logic-based trust evaluation, and mesh network routing using Breadth-First Search (BFS). An iterative development process upgraded the ML classifier from Multinomial Naive Bayes to LinearSVC with an expanded training corpus of 100 labeled bilingual messages, improving standalone ML accuracy from 32.5% to 70.0%. End-to-end evaluation on 80 live dashboard-tested messages demonstrates that the full hybrid pipeline achieves 71.2% classification accuracy — a 38.7 percentage-point improvement over the original ML baseline — with HIGH-priority message recall of 0.87, confirming reliable detection of safety-critical content. The system maintains stable message delivery across Dense, Sparse, and Damaged network topologies with a mean hop count of 3.4, and priority ordering effectiveness of 79.9%. Trust evaluation identified 21.2% of messages as low-credibility inputs, demonstrating resilience against noisy and misleading content. These results confirm that combining complementary AI techniques produces a significantly more robust disaster communication system than single-method approaches, with meaningful potential for real-world deployment.
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    PublicationOpen Access
    Decentralized Database Management: A Comprehensive Review of Blockchain- Based Data Systems
    (SLIIT City UNI, 2025-07-08) Sukirthan, T; Arunpirakash, S; Karuneswaran, G; Tharmmendra, T; Vithiyasahar, V
    The emergence of blockchain technology has revolutionized decentralized data management by offering robust alternatives to traditional centralized database systems. This paper provides a systematic and comprehensive review of blockchain-based distributed databases, highlighting key architectural transformations, core enabling technologies such as Merkle Trees, PBFT, and Zero-Knowledge Proofs, and comparing them with conventional distributed databases. Real-world implementations including Hyperledger Fabric, BigchainDB, and OrbitDB are analyzed to assess their scalability, interoperability, and security capabilities. The paper also explores intrinsic security mechanisms, performance bottlenecks, and regulatory challenges that affect adoption. Finally, it identifies open research questions and future directions necessary for building scalable, privacy-aware, and interoperable decentralized database ecosystems suitable for enterprise and multi-stakeholder environments.