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
- S. Deepa, I.S. Arafat, M. Sathya Priya, S. Saravanan, An improved spectrum sharing strategy evaluation over wireless network framework to perform error free communications , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- M. Deepika, I Antonitte Vinoline, Optimization of an Advanced Integrated Inventory Model Considering Shortages and Deterioration across Varying Demand Functions , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- M. Iniyan, A. Banumathi, Brower blowfish nash secured stochastic neural network based disease diagnosis for medical WBAN in cloud environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Rekha Raghavendra, Shobha Gowda, Jissy Thomas, Fingerprint doorlock system using Arduino uno , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- N. Anbarasi, K. Anitha, S. Hemalatha, A study on energy sum of dominating sets in East Indian states , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- M. Monika, J. Merline Vinotha, A resilient supply chain model integrating demand variability and carbon emissions in imperfect production systems , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- M. Monika, J. Merline Vinotha, A Fuzzy Supply Chain Model Evaluating Energy Management Systems under Imperfect Production and Uncertain Costs , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- Shriram N. Kargaonkar, Sushma Pradeep Chalke, Sunil Mahajan, Statistical Modeling of Consumer Preferences for Eco-friendly Digital Products: A Data-driven Approach Toward Sustainable Consumption in India , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Pallavi M. Shimpi, Nitin N. Pise, Comparative Analysis of Machine Learning Algorithms for Malware Detection in Android Ecosystems , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- Neeraj, Anita Singhrova, A critical review of blockchain-based authentication techniques , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
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

