Enhancing Congestion Control in Wireless Sensor Networks for Intelligent Agriculture through Cognitive IoT Integration

Abstract

Wireless sensor networks (WSNs) are made up of different wireless gadgets that have been outfitted with different kinds of sensors to gather environmental data. In agricultural operations, wireless sensor networking is widely utilized to boost output and lower losses in a number of ways. The greenhouse makes planting easier, which is advantageous for agriculture in a number of ways. Soil pH sensors and gas sensors are frequently utilized in agricultural models. These sensors may be used for a variety of integrated agricultural Internet of Things (IoT) applications. When traffic volume in wireless sensor networks (WSN) exceeds the combined or individual capacity of the underlying channels, congestion control becomes a critical domain. As a result, more care must be taken to create complex methods for preventing, identifying, and resolving congestion. When developing such strategies to maximize throughput, the limited resources of the WSN must be taken into account. In the last several years, a number of strategies have been established, such as specialized congestion control protocols and routing protocols that assist with congestion detection and control mechanisms. The Penman-Monteith equation is used to examine important issues, such as congestion control. This work aims to split the connection equally by the number of sources by using more than two reference factors, such as humidity and evapotranspiration, under various situations. According to the research, comparable variations with the same source value are achieved, demonstrating the effectiveness and equity of the suggested paradigm. These systems also show lower delay rates and faster throughput in an ideal location.