摘要
数据采集是使用标准化的、经过验证的技术为特定应用程序任务收集、测量和分析信息的过程。在移动辅助的水下无线传感器网络(UWSNs)中，由于3 m/s的水流，节点不能正常工作，数据采集成为一项艰巨的任务。为移动sink提供优化数据传输路径规划和调度的工作很少。这些系统传输数据的速度不足以提供实时数据传输，因为这些方法没有考虑数据采集中的bufer占用率和延迟。提出了一种基于混合灰狼优化算法(GWOWOA)的移动水槽激励传输路径规划技术。与其它优化技术相比，该混合技术包括许多更新过程，例如随机位置更新、灰狼优化器(GWO)的猎物搜索和鲸鱼优化算法(WOA)的猎物搜索。本文根据到移动sink的距离、bufer占用率、能级和数据采集延迟计算ftness函数。这些变量的使用使得所提出的技术具有创新性。为了验证该系统的有效性，将GWOWOA系统与现有系统进行了比较。仿真结果表明，该系统提高了采集数据的能量和精度，并使时延最小。
Abstract
Data acquisition is the process of collecting, measuring and analysing information using standardised, validated techniques for application-specifc tasks. In mobility-assisted underwater wireless sensor networks (UWSNs), where nodes are not fxed due to water current of 3 m/sec, data collection becomes an arduous task. There are few works that pro- vide a mobile sink with optimised data transmission path planning and scheduling. These systems do not transmit the data fast enough to provide real-time data transmission as these methods do not consider the bufer occupancy rate and latency in data acquisition. In this paper, a stimulating transmission path planning technique for mobile sinks using the hybrid Grey Wolf Optimizer Whale Optimization Algorithm (GWOWOA) is proposed. In contrast to other optimization techniques, this hybrid technique includes a number of update pro- cesses such as random position update, prey search by the Grey Wolf Optimizer (GWO) and prey search by the Whale Optimization Algorithm (WOA). In this paper, the ftness function is calculated in terms of distance to the mobile sink, bufer occupancy rate, energy level and data acquisition latency. The use of these variables makes the proposed technique innovative. To prove the efciency of the proposed system, GWOWOA is compared with existing systems. The simulation results show that the proposed system increases the resid- ual energy and accuracy of the collected data and minimises the delay.
NETL
NSTL