Department of Computer Engineering, University of Saravan, Saravan, Iran.
PLoS One. 2024 Nov 4;19(11):e0297728. doi: 10.1371/journal.pone.0297728. eCollection 2024.
Given the resource limitations of wireless sensor networks (WSNs), energy conservation is of utmost importance. Moreover, minimizing data collection delays is crucial to maintaining data freshness. Additionally, it is desirable to increase the number of collected data samples to enhance accuracy and robustness in data collection. For this purpose, this research article proposes a clustering-based routing protocol aimed at maximizing the delivery of data samples while minimizing energy consumption and data collection delays. The protocol employs a scattered search algorithm and fuzzy logic to cluster the sensor nodes. By considering the distance to the sink and the remaining energy level of the battery, the network is dynamically divided into clusters using a lightweight clustering approach. To evaluate the effectiveness of the proposed method, simulations were conducted in OPNET using the AFSRP protocol. The results demonstrate superior performance of the proposed method in terms of end-to-end delay by 13.44%, media access delay by 75.2%, throughput rate by 20.55%, energy consumption by 13.52%, signal-to-noise ratio by 43.40% and delivery rate of successfully sending data to the sink is 0.21% higher than the well-known AFSRP method.
鉴于无线传感器网络（WSN）的资源限制，节能至关重要。此外，最小化数据采集延迟对于保持数据的新鲜度至关重要。此外，增加采集数据样本的数量可以提高数据采集的准确性和鲁棒性。为此，本文提出了一种基于聚类的路由协议，旨在最大限度地提高数据样本的传输量，同时最小化能量消耗和数据采集延迟。该协议采用分散搜索算法和模糊逻辑对传感器节点进行聚类。通过考虑到与汇聚节点的距离和电池的剩余能量水平，网络使用轻量级聚类方法动态地划分为簇。为了评估所提出方法的有效性，使用 OPNET 中的 AFSRP 协议进行了模拟。结果表明，所提出的方法在端到端延迟方面的性能优于 AFSRP 协议，延迟降低了 13.44%，媒体访问延迟降低了 75.2%，吞吐量提高了 20.55%，能量消耗降低了 13.52%，信噪比提高了 43.40%，成功将数据发送到汇聚节点的传输率比著名的 AFSRP 方法高 0.21%。
