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N. Velmurugan

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Open access Aug 2026

A trust-aware replay-resistant dual-chain blockchain protocol with zero-knowledge authentication for secure and scalable IoMT systems

The rapid growth of Internet of Medical Things (IoMT)-based healthcare systems has increased the need for secure and interoperable data exchange across heterogeneous blockchain environments. Existing interoperability solutions often provide limited privacy protection and remain vulnerable to replay attacks, unauthorized access, and data inference threats. To address these challenges, this paper proposes the Attack-Resilient Secure Interoperability Protocol (ARSIP), a secure cross-chain protocol for privacy-preserving and replay-resistant communication between heterogeneous blockchain networks. ARSIP integrates four complementary security mechanisms: (i) zero-knowledge proof (ZKP)-based authentication, (ii) ECDH-based key agreement, HKDF session-key derivation, and AEAD (AES-GCM) authenticated encryption for confidentiality and integrity, (iii) nonce–timestamp-based replay protection, and (iv) dual-chain audit anchoring for traceability and auditability. To demonstrate deployment feasibility, ARSIP is implemented within an Ethereum–Hyperledger Fabric architecture supported by a cognitive serverless edge layer for adaptive workload management and low-latency processing. Experimental evaluation using a public IoMT healthcare dataset and a prototype-scale blockchain deployment shows that the ARSIP-enabled framework achieves a 30% reduction in latency, a 43% improvement in attack detection accuracy, and a 35% reduction in energy consumption compared with baseline blockchain-based healthcare systems. Formal verification and experimental analysis further support the protocol’s security properties. The results demonstrate the feasibility and effectiveness of ARSIP for secure cross-chain interoperability and indicate its potential applicability to future IoMT healthcare environments.

Kumar Velmurugan, P. Venkata, Sateesh Kumar et al. · 0 citations
Conference Jul 2026

Event-Triggered Decentralized Intelligence with Energy-Aware Federated Learning for Real-World Systems

The rapid growth of large-scale interconnected systems, such as smart cities, industrial automation, and environmental monitoring, demands intelligent decision-making frameworks that are resilient, scalable, and resource-efficient. Traditional centralized intelligence approaches suffer from communication bottlenecks, high energy demands, and vulnerability to single points of failure, making them unsuitable for realworld deployment. This work introduces an event-triggered decentralized intelligence framework with energy-aware federated learning designed to address these challenges. In the proposed system, distributed nodes collaborate by exchanging model updates only when significant events or anomalies occur, rather than relying on continuous communication. This event-driven strategy substantially reduces bandwidth consumption while enabling timely adaptation to dynamic environments. To further enhance sustainability, the framework integrates energy-aware scheduling, allowing devices with limited power resources to contribute adaptively based on their energy profiles. A multilayer coordination mechanism ensures local autonomy and global consensus without centralized control. Experimental evaluations on representative real-world datasets demonstrate that the proposed method achieves competitive accuracy compared to conventional federated learning while reducing communication overhead by more than 40% and extending device lifetime in energy-constrained settings. Additionally, the framework incorporates Byzantine-resilient aggregation and is analyzed under communication latency and varying network topology conditions.

M. Kishore, N. Velmurugan · 0 citations