# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
## 3 Material and Methods
### System Model and Background of the Proposed Framework  
The system model consists of sensors, fog nodes, and the sink node. They are deployed at random positions and the sink node is mobile. The fog nodes are considered more intelligent and have sufficient resources as compared to ordinary sensors. The mobile sink has a direct association with cloud systems. Initially, IoT sensors are arranged in an undirected graph \(G\) with finite vertices \(N\) and edges \(E\). Each node has enough memory to hold and maintain its neighbors' information. Similarly, neighbors in the fog system's proximity are arranged in the form list based on a particular score. Fog systems not only aggregate the agricultural data for transmission towards the sink node but also offer a security layer among sensors and cloud services. Each node has a prefixed transmission range, and in case the sink node is far away from it, the proposed framework adopts a multi-hop forwarding decision. We show the scenario for the proposed framework in Figure 1. A robust routing strategy was built with the identification of the redundant nodes, and agricultural sensors are installed in the field to sense the various situations. In the case of redundant nodes, such information is not permitted for transmission. Later, fog layers are made up of numerous fog nodes to initiate communication with sink nodes. Agriculture users may simply obtain the data from their smart devices with the support of cloud platforms. Our proposed framework also ensures data privacy and security in an unpredictable environment.  
Figure 2 depicts the block diagram of the proposed framework. It is comprised of three sub-blocks, i.e., network structure, machine learning system, and fog-based secured communication. Firstly, sensors, actuators, gateways, and other communication devices initialized themselves for sensing and forwarding agricultural data. The sensors continuously record environmental information, which is then sent to sink nodes via local coordinators or gateways. Second, a machine learning-based technique was created to reduce the communication overheads of the sensor nodes. Finally, the proposed system adopts the distributed regression function to assess the various attributes of the system and achieves efficient node management [42]. Moreover, the machine learning approach not only balances the load over the multiple routes, but the proposed framework can also identify the redundant nodes. Duplicate data are therefore prohibited from entering in proximity to fog nodes. In the end, a fog-based secured scheme is developed with the intelligence of fog nodes and cryptographic techniques. Fog nodes perform dual functionalities for communication with both the IoT system and cloud platform. This validates the authenticity of incoming packets, and accordingly, appropriate actions are taken. In the case of authorized nodes,  
Figure 1: Scenario for fog-based IoT agricultural system.  
they are allowed to send the data towards the cloud system or the request packets are dropped by the fog nodes. The information is recorded in the table about false messages and non-authentic devices.