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
- Vinodini R, Ritha W, Sasitharan Nagapan, An inventory model on the impact of green investment with deteriorating items and planned back orders for economic efficiency and environmental sustainability , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Rajesh Kumar Sharma, Amrendra Jha, ECOLOGICAL SCREENING OF SHATIYA WETLAND IN RELATION TO AGRICULTURAL PRODUCTIVITY , The Scientific Temper: Vol. 9 No. 1&2 (2018): The Scientific Temper
- Temesgen Asfaw, Customer churn prediction using machine-learning techniques in the case of commercial bank of Ethiopia , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Bratati Dey, Poonam Sharma, A comprehensive review of urban growth studies and predictions using the Sleuth model , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Bhavikgiri Vishnugiri Goswami, Vaseemahmed G. Qureshi, Reclaiming identity: transgender perspectives on inclusion in contemporary India , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- Elangovan G. Reddy, Anjana Devi V, Subedha V, Tirapathi Reddy B, Viswanathan R, A smart irrigation monitoring service using wireless sensor networks , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Swetha Rajkumar, Jayaprasanth Devakumar, LSTM based data driven fault detection and isolation in small modular reactors , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Rashika R. Singh, Nimish Gupta, G. R. Yadav, Scope of electric vehicles and the automobile industry in Indian perspective , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Tewoderos Legesse, Bekelech Sharew, Evaluation of white seeded sesame (Sesamum indicium L.) genotypes on growth and yield performance in Menit Goldya Woreda of West Omo Zone, SWE , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Harshaben Raghubhai Pankuta, Kusum R. Yadav, Evaluating the effectiveness of the Gyankunj Project: Teachers’ perceptions from Gujarat , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
<< < 32 33 34 35 36 37 38 > >>
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

