Trust-Aware Routing Optimization for Smart Grid Data Transmission Using Dynamic Node Profiling

Abstract

The rapid evolution of smart grids has enabled real-time monitoring, bidirectional communication, and intelligent energy management. However, the increasing interconnectivity of smart grid components exposes communication networks to security threats, unreliable nodes, and routing inefficiencies that can compromise data integrity and system performance. To address these challenges, this paper proposes a Trust-Aware Routing Optimization (TARO) framework for smart grid data transmission based on Dynamic Node Profiling (DNP). The proposed approach evaluates network nodes using multiple parameters, including trustworthiness, residual energy, communication reliability, packet forwarding behavior, latency, and historical performance. These parameters are integrated into a dynamic profiling mechanism that continuously updates node scores according to network conditions. The routing process prioritizes highly trusted and resource-efficient nodes, thereby reducing the likelihood of malicious participation while improving transmission reliability. Furthermore, an optimization module identifies secure and efficient communication paths that balance security requirements with quality-of-service constraints. Experimental evaluation conducted under varying network conditions demonstrates that the proposed framework achieves improved packet delivery ratio, lower end-to-end delay, enhanced network lifetime, and higher resilience against compromised nodes compared with conventional routing approaches. The results indicate that dynamic trust-based node profiling can significantly strengthen communication security and operational efficiency in modern smart grid environments.

Introduction

The text discusses the growing importance of secure and reliable communication in smart grid systems, which rely on advanced technologies for real-time monitoring, automation, and energy management. Existing research has improved areas such as data transmission security, privacy protection, IoT communication, cyberattack detection, and energy management. However, many current solutions focus on encryption, shortest-path routing, or static trust mechanisms and do not adequately address dynamic trust evaluation, malicious node detection, and routing optimization in large-scale smart grid networks.

To overcome these limitations, the paper proposes a Trust-Aware Routing Optimization (TARO) framework based on Dynamic Node Profiling (DNP). The framework continuously evaluates network nodes using parameters such as trust score, residual energy, packet forwarding reliability, latency, link stability, and historical behavior. These factors are combined to create dynamic node profiles that reflect real-time network conditions.

Using these profiles, TARO selects secure and efficient communication paths while avoiding malicious, unreliable, or energy-depleted nodes. The framework aims to improve packet delivery, reduce delays, enhance energy efficiency, and strengthen resilience against cyberattacks such as packet dropping, false data injection, and routing manipulation.

Key contributions of the paper include:

1. A dynamic node profiling mechanism based on trust, performance, and resource metrics.
2. A trust-aware routing strategy that prioritizes secure and reliable nodes.
3. An optimization framework balancing security, energy efficiency, and quality of service.
4. Comprehensive performance evaluation demonstrating improved communication reliability and network resilience.

The methodology consists of five stages:

• Smart grid data collection and communication monitoring.
• Dynamic node profiling and trust assessment.
• Trust-aware routing optimization.
• Secure data transmission with adaptive route updates.
• Performance evaluation and comparative analysis.

Experimental results in a simulated smart grid network of over 1000 nodes show that TARO outperforms conventional routing methods. It achieved:

• 98.2% Packet Delivery Ratio
• 96.8% Trust Detection Accuracy
• 10.9% Routing Overhead

These results demonstrate that integrating dynamic trust evaluation with routing optimization significantly enhances the security, reliability, and efficiency of smart grid communication networks.

Conclusion

This paper presented a Trust-Aware Routing Optimization (TARO) framework for secure and efficient data transmission in smart grid communication networks using Dynamic Node Profiling. The proposed framework integrates trust evaluation, node behavior assessment, residual energy monitoring, communication reliability analysis, and adaptive route optimization to identify secure and efficient communication paths. Experimental results demonstrated that the TARO framework significantly improves packet delivery ratio, throughput, network lifetime, and trust detection accuracy while reducing communication delay and routing overhead compared with conventional routing approaches. The dynamic profiling mechanism effectively identifies unreliable and malicious nodes, thereby enhancing network security, communication resilience, and operational reliability in smart grid environments. Future research can focus on integrating machine learning and deep learning techniques for predictive trust assessment and autonomous routing decisions in highly dynamic smart grid networks. Furthermore, the incorporation of blockchain-based trust management, federated learning for distributed intelligence, and quantum-resistant security mechanisms can further strengthen communication security and scalability. The proposed framework can also be extended to support next-generation smart energy infrastructures, including renewable energy integration, vehicle-to-grid communications, and large-scale Internet of Energy ecosystems.

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Copyright

Copyright © 2026 Sandesh ., Sharad Kumar. 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.