D Udaya Suriya Rajkumar, R Sathiyaraj, A Bharathi, D Mohan, Pellakuri Vidyullatha
Department of Computer Science and Engineering, Global Institute of Engineering and Technology, Melvisharam, Ranipet District, Tamil Nadu, India.
Department of CSE, GITAM School of Technology, GITAM University, Bangalore, Karnataka, India.
Sci Rep. 2024 Dec 2;14(1):29869. doi: 10.1038/s41598-024-81038-1.
Wireless Sensor Networks present a significant issue for data routing because of the potential use of obtaining data from far locations with greater energy efficiency. Networks have become essential to modern concepts of the Internet of Things. The primary foundation for supporting diverse service-centric applications has continued to be the sensor node activity of both sensing phenomena in their local environs and relaying their results to centralized Base Stations. Malware detection and inadequate Cluster Heads node selection are issues with the current technology, resulting in a drastic decrease in the total Internet of Things-based performance of sensor networks. The paper proposes an Enhanced Lion Swarm Optimization (ELSO) and Elliptic Curve Cryptography (ECC) scheme for secure cluster head selection and malware detection in IoT-based Wireless Sensor Networks (WSNs). The paper includes network models, choice of Cluster Head (CH) and attack detection procedures. The proposed method chooses the Cluster Head with the best fitness function values, increasing data transmission speeds and energy efficiencies. Minimum Hop Detection has been implemented to provide the best routing paths against attack nodes. Security level for quick data transmissions via the Internet of Things using Wireless Sensor Networks strengthen sinkhole attacks and black hole nodes, which are successfully removed using this method. The proposed method integrates the use of Lion Swarm Optimization and Elliptic Curve Cryptography (ECC) enhances network security by ensuring secure data transmission and preventing unauthorized access, which is particularly important in IoT-WSN environments. The proposed method achieves less End delay, increased throughput of 93%, lower energy utilization of 4%, increased network lifetime of up to 96%, Packet Delivery Ratio of up to 98% and 97% of malicious node detection efficiently compared to existing methods.
无线传感器网络在数据路由方面存在一个重大问题，因为有可能以更高的能源效率从远处获取数据。网络已成为物联网现代概念的核心。支持各种以服务为中心的应用的主要基础仍然是传感器节点的活动，即感知其本地环境中的现象并将结果中继到集中式基站。恶意软件检测和簇头节点选择不当是当前技术存在的问题，导致基于物联网的传感器网络的整体性能急剧下降。本文提出了一种增强型狮群优化（ELSO）和椭圆曲线密码学（ECC）方案，用于基于物联网的无线传感器网络（WSN）中的安全簇头选择和恶意软件检测。本文包括网络模型、簇头（CH）的选择和攻击检测程序。所提出的方法选择具有最佳适应度函数值的簇头，提高数据传输速度和能源效率。已实施最小跳数检测，以提供针对攻击节点的最佳路由路径。使用无线传感器网络通过物联网进行快速数据传输的安全级别强化了陷洞攻击和黑洞节点，使用此方法可成功消除这些问题。所提出的方法集成了狮群优化和椭圆曲线密码学（ECC）的使用，通过确保安全的数据传输和防止未经授权的访问来增强网络安全性，这在物联网 - WSN环境中尤为重要。与现有方法相比，所提出的方法实现了更低的端到端延迟、吞吐量提高93%、能源利用率降低4%、网络寿命延长高达96%、数据包交付率高达98%以及有效检测97%的恶意节点。
