Sharma Neha, Gupta Vishal
USICT, Guru Gobind Singh Indraprastha University, Delhi, India.
Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.
MethodsX. 2024 Nov 10;13:103030. doi: 10.1016/j.mex.2024.103030. eCollection 2024 Dec.
Wireless sensor networks (WSNs) have been highly utilized and defensible technology in diverse application areas for data gathering from remote and hard-to-approach regions. Wireless Sensor Networks are substantially important for the real-world applications such as environmental monitoring, surveillance, and smart infrastructure. Network coverage, connectivity and energy savings are significant factors in the WSN deployment. Wireless sensor networks (WSNs) undergo a great deal of crucial challenges such as minimize energy consumption, maximize coverage, and network lifetime improvement. Sensor nodes are energy constrained and deployed in resource-constrained environments for many real-world applications. Low energy usage is hence crucial to prolong network life. Meanwhile, to guarantee the performance of a WSN, it is crucial to ensure data transmission with less energy consumption and full coverage. These challenges are the central focus of this work, requiring scalable and efficient deployment strategies. In this paper, a complete survey study on optimization technique for deployment of WSN to improve network performance and resource utilization is offered. The paper also suggests a new algorithm named as Fractal Clustering Based Firefly Deployment Algorithm which is particularly designed for the deployment of sensor nodes deployed in WSNs. The proposed hybridize method uses the principles of fractal clustering and firefly optimization algorithm to make light-weight, energy efficient and enhanced optimized deployment strategy. To start with, the algorithm makes use of a fractal clustering technique to partition an area of interest into regions that have similar attributes. This clustering determines the areas that are needed to have higher sensor node density requirements - regions where events requiring a critical response or data traffic are high. The algorithm represents each cluster by a virtual firefly. The firefly algorithm is a biologically-inspired swarm intelligence optimization technique, inspired by the flashing behavior of fireflies which stochastically moves through input parameter space to find favorable deployment configurations. In this paper, the efficiency of the algorithm is verified by simulating the proposed algorithm using MATLAB2020 and comparing it with other deployment strategies. This analysis shows promising results.
无线传感器网络（WSNs）在从偏远和难以接近的区域收集数据的各种应用领域中已成为高度实用且可靠的技术。无线传感器网络对于诸如环境监测、监视和智能基础设施等实际应用至关重要。网络覆盖范围、连通性和节能是无线传感器网络部署中的重要因素。无线传感器网络面临诸多关键挑战，例如将能耗降至最低、最大化覆盖范围以及提高网络寿命。传感器节点能量受限，并且在许多实际应用中部署在资源受限的环境中。因此，低能耗对于延长网络寿命至关重要。同时，为了保证无线传感器网络的性能，以较低的能耗和全面覆盖确保数据传输至关重要。这些挑战是本研究的核心重点，需要可扩展且高效的部署策略。本文提供了一项关于无线传感器网络部署优化技术以提高网络性能和资源利用率的全面调查研究。本文还提出了一种名为基于分形聚类的萤火虫部署算法的新算法，该算法专门为无线传感器网络中部署的传感器节点而设计。所提出的混合方法利用分形聚类和萤火虫优化算法的原理，制定出轻量级、节能且优化程度更高的部署策略。首先，该算法利用分形聚类技术将感兴趣的区域划分为具有相似属性的区域。这种聚类确定了需要更高传感器节点密度要求的区域——即需要关键响应或数据流量较高的事件的区域。该算法用虚拟萤火虫表示每个聚类。萤火虫算法是一种受生物启发的群体智能优化技术，灵感来自萤火虫的闪光行为，它在输入参数空间中随机移动以找到有利的部署配置。在本文中，通过使用MATLAB2020对所提出的算法进行仿真并将其与其他部署策略进行比较，验证了该算法的效率。该分析显示出了有前景的结果。
