Ali Shujaat, Nadeem Muhammad, Ahmed Sheeraz, Tahir Muhamad
Department of Computer Science, Faculty of Computing and Information Technology International, Islamic University, Islamabad, Pakistan.
IQRA National University Peshawar, Peshawar, Pakistan.
Sci Rep. 2024 Oct 15;14(1):24122. doi: 10.1038/s41598-024-74664-2.
In today's world, there is an increasing demand for environmental monitoring, surveillance, and oceanographic research, which poses challenges in improving energy efficiency and data transfer reliability in Acoustic Sensor Networks. Existing methods face hurdles due to limited energy resources and unreliable data transmission. We propose a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) to address these issues. This framework optimizes energy consumption and enhances data reliability by incorporating advanced energy management strategies such as adaptive duty cycling and efficient data transmission mechanisms by minimizing forwarding nodes. Simulation results demonstrate that REEFSM reduces energy consumption by up to 43% and increases data reliability by 35% compared to protocols like EERBCR and DEADS. REEFSM ensures zero dead nodes, minimizes packet drops, and maintains high data accuracy throughout the simulation. This research outcome provides a sustainable and reliable solution for energy-efficient data collection in underwater environments. The future research directions, including integrating autonomous path planning, energy harvesting, and machine learning techniques, hold great potential for further advancements in the field.
在当今世界，对环境监测、监视和海洋学研究的需求日益增加，这给提高声学传感器网络的能源效率和数据传输可靠性带来了挑战。由于能源资源有限和数据传输不可靠，现有方法面临障碍。我们提出了一种具有汇聚节点移动性的可靠且节能框架（REEFSM）来解决这些问题。该框架通过纳入先进的能源管理策略（如自适应占空比）以及通过最小化转发节点的高效数据传输机制，优化了能源消耗并提高了数据可靠性。仿真结果表明，与EERBCR和DEADS等协议相比，REEFSM可将能源消耗降低多达43%，并将数据可靠性提高35%。REEFSM确保零死节点，最小化数据包丢失，并在整个仿真过程中保持高数据准确性。这一研究成果为水下环境中的节能数据收集提供了一种可持续且可靠的解决方案。未来研究方向，包括整合自主路径规划、能量收集和机器学习技术，在该领域具有进一步推进的巨大潜力。
