College of Computer & Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia.
Sensors (Basel). 2022 Sep 3;22(17):6676. doi: 10.3390/s22176676.
In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network's resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.
在最近几十年,网络智能设备和尖端技术已被应用于农业领域的多个方面,以提高农业生产效率。智能传感器和智能农业技术的部署支持农业部门实时信息采集,减轻了农民的负担。许多解决方案已被提出,旨在利用物联网网络实现农业系统的自动化;然而,识别冗余数据流量是最重大的研究问题之一。此外,农民无法及时获得所需信息,例如水压和土壤状况的数据。因此,这些解决方案会降低农民的生产效率并增加成本。此外,在开发智能解决方案时,还应考虑以受控方式控制所有农业作业。因此,本研究提出了一个将雾计算与智能农业相结合的系统框架,可有效控制网络流量。首先,所提出的框架可以有效地监控冗余信息,避免通信带宽的低效使用。它还可以控制恶意行为下的重传次数,并有效地利用网络资源。其次,通过利用雾节点在农业传感器之间建立可信链,解决安全问题并提高可靠性,防止恶意通信。通过基于仿真的广泛实验,与其他相关工作相比,所提出的框架在能源效率、安全性和网络连接性方面表现出了改进的性能。