基于环境中移动运输代理的传感器网络建模
Modeling of sensor networks based on mobile transport agents in environment
赵海军 1陈华月 1崔梦天2
作者信息
- 1. 西华师范大学计算机学院 南充 637009
- 2. 西南民族大学计算机科学与技术学院 成都 610041
- 折叠
摘要
针对大型稀疏传感器网络中的数据获取,本文提出了一种利用环境中普遍存在的移动代理来连接稀疏传感器的网络体系结构和一种 2-维网格随机游走分析模型;提出的传感器网络模型由 3 个抽象层构成,即由无线传感器构成的底层、由各种运输代理构成的中间层和由接入点/中央存储库构成的顶层.具体实现原理是位于中间层的移动运输代理从底层分布的无线传感器收集数据并缓冲数据,然后经过游走运输,最后将从底层的无线传感器收集的数据交付到顶层必要的接入点进行必要的存储和处理,从而实现整个传感器网络的数据获取;理论分析和仿真实验结果表明,提出的基于移动运输代理的传感器网络模型不仅具有较好的鲁棒性和可扩展性,而且相比于基站网络模型和Ad-hoc网络模型,在传感器功率消耗、数据成功率和基础设施投入成本方面有明显的优势.
Abstract
Aiming at the data acquisition in large sparse sensor networks,a network architecture using the ubiquitous existence of mobile agents in environment to connect sparse sensors and a 2-dimensional grid random walk analysis model are proposed in this paper.The proposed sensor network model consists of three abstract layers,namely,bottom layer composed of wireless sensors,the middle layer composed of various transportation agents,and the top layer composed of access points/central repositories.The specific implementation principle is that the mobile transport agents located in the middle layer collect data from the wireless sensors distributing at the bottom layer and buffer the data,and after wandering transport,finally deliver the data collected from the wireless sensors at the bottom layer to the necessary access points at the top layer for necessary storage and processing,so as to achieve the data acquisition of the entire sensor network.The theoretical analysis and simulation experiment results show that the proposed sensor netwoks model based on mobile transport agents not only has robustness and scalability,but also has obvious advantages over base station network model and Ad-hoc network model in terms of sensor power consumption,data success rate and infrastructure invested cost.
关键词
传感器网络/移动代理/网格模型/随机游走/马尔科夫链/缓冲容量/数据成功率/功率消耗Key words
sensor network/mobile agent/grid model/random walk/markov chain/buffer capacity/data success rate/power consumption引用本文复制引用
基金项目
四川省自然科学基金(2022NSFSC0536)
国家自然科学基金(12050410248)
出版年
2024
NETL
NSTL
万方数据