Energy-aware Security Optimized Elliptic Curve Digital Signature Algorithm for Universal IoT Networks
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.9.06Keywords:
Digital Signature Algorithms, Energy-aware security, Network Security, Internet-of-Things.Dimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Ensuring security, integrity, and energy efficiency in Internet of Things (IoT) networks is a critical challenge due to the resource constraints of IoT devices. Traditional digital signature algorithms such as RSA, ECDSA, and EdDSA provide security but often lack energy optimization, making them inefficient for large-scale IoT deployments. To address these challenges, this research proposes an Energy-aware Security Optimized Elliptic Curve Digital Signature Algorithm (EECDSA) for universal IoT networks. EECDSA enhances conventional ECDSA by integrating three novel functional modules: Lightweight Context Sensitivity Imposer (LCSI), Adaptive Computational Complexity Overseer (ACCO), and Energy-aware ECDSA Signer (EAES). These modules dynamically adjust security parameters based on contextual sensitivity, optimize computational complexity to balance security and resource consumption, and ensure energy-efficient digital signing in IoT environments. The proposed method is evaluated using OPNET simulations, measuring both security and network performance metrics, including Accuracy, Precision, Sensitivity, Specificity, F-Score, Throughput, Latency, Jitter, Energy Consumption, Packet Delivery Ratio, and Security Levels. Experimental results demonstrate that EECDSA outperforms existing security solutions, achieving higher security resilience (99.55%), reduced energy consumption (511.6mJ), and improved network performance. These findings validate EECDSA as an efficient and scalable security mechanism for IoT ecosystems.Abstract
How to Cite
Downloads
Similar Articles
- A. Anand, A. Nisha Jebaseeli, AI-driven real-time performance optimization and comparison of virtual machines and containers in cloud environments , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Jhankar Moolchandani, Kulvinder Singh, English language analysis using pattern recognition and machine learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Rekha R., P. Meenakshi Sundaram, Enhanced malicious node identification in WSNs with directed acyclic graphs and RC4-based encryption , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Nida Syeda, Kishore Selva Babu, Exploring the role of digital humanities in the centralization of knowledge production: Clusters, networks, or echo chambers , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- K. Kalaiselvi, M. Kasthuri, Tuning VGG19 hyperparameters for improved pneumonia classification , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- T. Ramyaveni, V. Maniraj, Hyperparameter tuning of diabetes prediction using machine learning algorithm with pelican optimization algorithm , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Suprabha Amit Kshatriya, Jaymin K Bhalani, Early detection of fire and smoke using motion estimation algorithms utilizing machine learning , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- A. Kalaiselvi, A. Chandrabose, Fuzzy logic-driven scheduling for cloud computing operations: a dynamic and adaptive approach , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Bhavya Sathenapalli, Kali Charan Sabat, Unleashing entrepreneurial spirit: Driving innovation and growth in a rapidly changing world , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Karthik Baburaj, Navaneeth kattil Madathil, Roshini Barkur, NLP Based Voice Assistant Usage on Consumer Shopping , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
<< < 6 7 8 9 10 11 12 13 14 15 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Yasodha V, V. Sinthu Janita, AI-driven IoT routing: A hybrid deep reinforcement learning and shrike optimization framework for energy-efficient communication , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper

