# Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture
## 5 Results with Discussion  
We evaluated the performance of the proposed framework with other studies in terms of energy efficiency. The performance evaluation of the proposed framework against those of related works is shown in Figure 6a,b for varied IoT sensors and the distance from the sink node. It was found that the proposed framework, even in the presence of malfunctioning nodes, considerably improves the delivery rate of data packets by an average of 20% for varying nodes, and 22% for varying distances from the sink. It results from exploring security approaches, key generation, and mutual trust. In the proposed paradigm, the intelligent system achieves sustainability and effectively identifies harmful activities by utilizing the secret and personal data of devices. Furthermore, the sink nodes are more powerful than IoT sensors and verify each activity before sending it to the end users. Unlike the majority of the proposed research, our framework explicitly provides intelligent decision modules for enhancing packet delivery performance with the aid of machine learning and sustains the network load with improved throughput. Figure 7a,b demonstrate the comparison of the proposed framework in the literature and it was discovered that it notably improves the network reliability by an average of 18% and 23% for both scenarios. This is due to the exposure of machine learning techniques to identify the optimal IoT nodes and intelligently interact with cloud systems. Moreover, the proposed framework can tackle redundant information and decreases the chances of data unavailability and congestion. Additionally, the security solution decreases unwanted traffic across the open transmission system and stops malicious devices from sending false route request packets. As a result, the proposed framework lengthens the response time for critical situations with a nominal delay rate. The comparison of the proposed and existing solutions is revealed in Figure 8a,b in terms of varying nodes and distances from the sink. The statistical analysis demonstrated that the proposed framework increased energy usage by 15% and 24%, respectively. Energy efficiency was found to be negatively impacted when the number of devices increased. On the other hand, the proposed framework provides a smart energy solution based on a distributed machine learning technique and smoothly selects the updated routes by utilizing the optimum solution. Additionally, the proposed framework effectively defends against multiple attacks and minimizes the consumption of network bandwidth by employing a security algorithm. Consequently, the nodes' energy is increased, which improves the performance of the entire network. By finding the redundant nodes in the routing table, the flag status is modified. Nodes whose flag value is false are therefore prohibited from participating in data routing. The performance comparison of the proposed framework to the existing solutions for network connectivity is illustrated in Figure 9a,b. Network connectivity indicates the active time at which nodes use particular communication links. The connectivity ratio for the proposed framework against various IoT devices and varied distances is remarkably enhanced by an average of 16% for varying distances from the sink and 20% in terms of varying nodes. This is due to the ability of the proposed framework to efficiently manage power distribution across the sensors and investigate the machine learning principles for accomplishing forwarding decisions.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Agriculture sensors & Varying 150–750 \\ Sensor’s type & Temperature, air humidity, water quality, soil moisture \\ Fog nodes & 10–15 \\ Interface & IEEE 802.15.4 \\ Round step & 20 s \\ Sink nodes & 2 \\ Number of rounds & 2000 \\ Initial energy & 5 J \\ Field dimension & \(1000\times 1000\) \\ Simulations & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.