# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
## 1 Introduction  
Today, the Internet and the Internet of Things (IoT) dominate nearly everyone's life. IoT is a paradigm that connects things, people, or networks and allows them to process and react precisely to any kind of physical or virtual communication [1; 2; 3]. IoT has applications in every industry, including healthcare, agriculture, and home controllers. It efficiently provides user-required services by utilizing Internet connectivity, sensors, and a variety of other technologies and protocols for collecting and analyzing data [4; 5; 6]. The Internet of Things helps companies to automate operations and improve service delivery using Internet technologies and cloud-based data transmission. For the various industries it is utilized in, IoT does not permit the adoption of universal software architecture; instead, it must be modified to meet user requirements [7; 8; 9]. Nowadays, smart agriculture is more important than ever because of the expanding global population and rising food demand. In this context, smart technologies have become a crucial route to cutting-edge agricultural practices [10; 11; 12]. There are many different applications, protocols, and prototypes in the field of agricultural land.  
Furthermore, several IoT policies and standards have been developed in the agriculture sector in numerous nations and organizations worldwide. Due to the limited boundaries of IoT devices, sensors, and actuators, a detailed study of IoT in the context of agriculture is required to understand the present state of research [13; 14; 15]. Throughout history, the agricultural industry has played a significant role in human cultures worldwide. Machine learning is a subset of artificial intelligence widely explored for identifying malicious attacks. Such a technique also focuses on making the system smart so that additional overhead can be reduced on the IoT devices [16; 17]. The organization and practices of modern agriculture are significantly impacted by the development of information and communication technologies (ICTs). Despite the benefits of this evolution, several security concerns have the potential to negatively affect the agriculture sector [18; 19; 20]. Since farmers could sustain a significant financial and personal loss in the event of a data breach, data integrity and confidentiality are critical security concerns within the agricultural sector [21; 22; 23]. In this work, the main contributions are as follows:  
1. It provides a strategy to effectively manage IoT resources, identify the redundant nodes that are collecting the same data and forbid using them. Such a scheme is efficient in utilizing the resources of the network and prolonging the system life cycle.
2. It develops intelligent methods using distributed machine learning to predict routing decisions for the selection of optimal forwarders with increasing data delivery and load balancing.
3. Another aspect of the proposed framework is the usage of a fog system to establish a secure chain in the presence of unidentified and faulty nodes by establishing a reliable group of nodes.
4. We verified the proposed framework with other work and the results demonstrate enhanced performance for various network parameters.  
This paper is further organized as follows: the discussion for related studies is presented in Section 2. Section 3 presents the detail of the proposed framework. In Section 4, the simulation environment is briefly explained. Section 5 presents the results and discussion of the experiments. Lastly, the conclusion is in Section 6.