Mohan Saranga, Panda Sunita
Department of Electrical, Electronics, and Communication Engineering, GITAM School of Technology, GITAM (Deemed to Be University), Bengaluru, India.
Heliyon. 2024 Feb 15;10(5):e25998. doi: 10.1016/j.heliyon.2024.e25998. eCollection 2024 Mar 15.
The convergence of wireless sensor network-assisted Internet of Things has diverse applications. In most applications, the sensors are battery-powered, and it is necessary to use the energy judiciously to extend their functional duration effectively. Mobile sinks-based data collection is used to extend the lifespan of these networks. But providing a scalable and effective solution with consideration for multi-criteria factors of quality of service and lifetime maximization is still a challenge. This work addresses this problem with a hybrid wireless sensor network-Long term evolution assisted architecture. The problem of maximizing lifetime and providing multi-factor quality of service is solved as a two-stage optimization problem involving clustering and data collection path scheduling. Hybrid meta-heuristics is used to solve the clustering optimization problem. Minimal Steiner tree-based graph theory is applied to schedule the data collection path for sinks. Unlike existing works, the lifetime maximization without QoS degradation is addressed by hybridizing multiple approaches of multi-criteria optimal clustering, optimal path scheduling, and network adaptive traffic class-based data scheduling. This hybridization helps to extend the lifetime and enhance the QoS regarding packet delivery within the proposed solution. Through simulation analysis, the introduced approach yields a noteworthy increase of at least 6% and reduces packet delivery delay by 26% compared to existing methodologies.
无线传感器网络辅助的物联网融合具有多种应用。在大多数应用中，传感器由电池供电，因此有必要明智地使用能量，以有效延长其功能持续时间。基于移动汇聚节点的数据收集用于延长这些网络的寿命。但是，在考虑服务质量和寿命最大化等多标准因素的情况下，提供一种可扩展且有效的解决方案仍然是一项挑战。这项工作通过一种混合无线传感器网络-长期演进辅助架构来解决这个问题。将寿命最大化和提供多因素服务质量的问题作为一个涉及聚类和数据收集路径调度的两阶段优化问题来解决。使用混合元启发式算法来解决聚类优化问题。基于最小斯坦纳树的图论用于为汇聚节点调度数据收集路径。与现有工作不同的是，通过将多标准最优聚类、最优路径调度和基于网络自适应流量类的数据调度等多种方法进行混合，解决了不降低服务质量的寿命最大化问题。这种混合有助于在所提出的解决方案中延长寿命并提高数据包交付方面的服务质量。通过仿真分析，与现有方法相比，所引入的方法产生了至少6%的显著增加，并将数据包交付延迟降低了26%。
