A Multi-Tiered Decentralized Framework for Context-Aware Access Control and Trusted Authentication in Scalable IoT Ecosystems

Abstract

The scalability, adaptability, and security of traditional centralized access control mechanisms are severely limited as Internet of Things (IoT) ecosystems grow to billions of linked devices. In order to enable adaptive and fine-grained authorization, this work suggests a decentralized multi-layer architecture that combines Context-Aware Access Control (CAAC), Attribute-Based Access Control (ABAC), and a semantic Long Short-Term Memory (LSTM) prediction model. The system integrates contextual intelligence processing, consortium blockchain-based policy validation via the Raft consensus protocol, and edge-level preliminary authentication. The framework maintains distributed trust and minimal verification delay while enabling high transaction throughput. Semantic prediction of environmental circumstances reduces computational overhead and improves resilience to replay and interception assaults, according to experimental data. For large-scale IoT environments, the suggested method effectively strikes a balance between scalability, security, and adaptability.

Introduction

The rapid growth of Smart Cities and Industry 4.0 has significantly increased sensitive data sharing among IoT devices, creating the need for secure, scalable, and adaptive access control systems. Traditional models like Role-Based Access Control (RBAC) are insufficient because they cannot adapt to dynamic contextual factors such as location, time, and behavior. Additionally, centralized architectures introduce single points of failure and scalability issues, making systems vulnerable to attacks.

To address these challenges, the proposed framework combines decentralized trust, context awareness, and predictive analytics. It uses a consortium blockchain for decentralized identity management and tamper-proof policy enforcement, a smart contract-based Context-Aware Access Control (CAAC) system for real-time authorization, and a semantic LSTM model to predict risks and enforce least-privilege access dynamically.

The system introduces a four-tier architecture:

1. IoT Edge Layer – Handles device-level operations, request generation, and initial authentication.

2. Context Intelligence Layer – Uses LSTM-based modeling to analyze behavior and compute dynamic risk scores.

3. Blockchain Trust Layer – Ensures decentralized verification, immutable policy enforcement, and token-based access control using smart contracts.

4. Policy Governance Layer – Provides monitoring, auditing, and adaptive policy updates through a feedback loop.

Key contributions include decentralized multi-tier design, dynamic context-aware access control, predictive risk modeling, tokenized permission management, and optimized blockchain consensus (Raft).

Conclusion

In order to overcome the shortcomings of conventional access control methods in extensive IoT ecosystems, this article introduced a scalable and context-aware security architecture that combines Blockchain-based trust management with predictive machine learning. The proposed system provides fine-grained, adaptive, and risk-aware authorization choices by merging Attribute-Based Access Control (ABAC) with Context-Aware Access Control (CAAC) and a semantic LSTM-based environmental prediction engine. Blockchain-based policy verification, contextual intelligence processing, edge-level pre-authentication, and governance control are all divided by the multi-tier decentralized architecture. In contexts with limited resources, this structural deconstruction increases scalability, decreases single points of failure, and boosts operational effectiveness. Adopting a consortium blockchain with Raft consensus guarantees low-latency transaction processing and predictable validation while preserving access record immutability and auditability. Experimental evaluation demonstrates that the proposed framework achieves high throughput and reduced computational overhead compared to conventional centralized and non-contextual methods for controlling access. By dynamically enforcing minimum-permission principles, the integration of semantic environmental prediction strengthens resilience against replay attacks, impersonation attempts, and privilege escalation, hence contributing to proactive risk mitigation. Overall, the findings show that the suggested method successfully strikes a balance between scalability, security, and adaptability, making it appropriate for use in industrial IoT systems, smart city infrastructures, and other large-scale distributed contexts.

References

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Copyright

Copyright © 2026 Srivalli Ch, Dr. Vinay Chavan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.