With expanding realms of Internet of Things (IoT), researchers have started venturing into designing such algorithms for Wireless Sensor Networks (WSN) that support IoT network requirements. However, collaborating a sensor network into Internet of Things applicability faces surging data flow amidst which the senor network is expected to provide reliable data over extended span. Amidst variant techniques like routing, data aggregation, packet scheduling etc. which can be improvised upon, clustering has been a widespread energy efficient technique that primarily shortens distances in large-scale networks while also bring down commuting packets over improved connectivity. Therefore, utilizing clustering to incorporate WSN enabled IoT (WSN-IoT) standards becomes the primary focus of this paper. Heuristic based clustering algorithm termed as Prolong—Lines of Uniformity based Energy Threshold protocol (P-LUET) has been proposed that focusses on expanding the stable operating period of WSN-IoT. This algorithm is based on certain measures of WSN-IoT’s per unit, that is, residual energy, cartesian coordinate based location, shadow distance from the Sink Node, and density within the field. The parameters are employed such that they help combat hotspot issue, cluster overlapping, and network connectivity. A cluster-based conditional gridding Voronoi structure has also been consolidated that enables multi-hop communication. P-LUET algorithm is thoroughly analysed through comparisons with the existing approaches. Furthermore, analysis on P-LUET has also been carried on scenarios that are based on heterogeneity synthesis and b, c parameters.

随着物联网 (IoT) 领域的不断扩大，研究人员已开始尝试为支持物联网网络要求的无线传感器网络 (WSN) 设计此类算法。然而，将传感器网络协作到物联网适用性面临着激增的数据流，其中传感器网络有望在扩展范围内提供可靠的数据。在路由、数据聚合、数据包调度等可以即兴使用的变体技术中，集群是一种广泛使用的节能技术，主要缩短大规模网络中的距离，同时还通过改进的连接减少通勤数据包。因此，利用集群来整合启用 WSN 的物联网 (WSN-IoT) 标准成为本文的主要重点。已经提出了基于启发式的聚类算法，称为 Prolong-Lines of Uniformity based Energy Threshold protocol (P-LUET)，其重点是扩展 WSN-IoT 的稳定运行周期。该算法基于 WSN-IoT 每单位的某些度量，即剩余能量、基于笛卡尔坐标的位置、与汇节点的阴影距离以及场内的密度。采用这些参数是为了帮助解决热点问题、集群重叠和网络连接问题。还整合了基于集群的条件网格 Voronoi 结构，支持多跳通信。通过与现有方法的比较，对 P-LUET 算法进行了彻底的分析。此外，对 P-LUET 的分析也进行了基于异质性合成和b、c参数。