# Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications
## 4 Simulation Environment
The proposed algorithm also provides an intelligent energy-efficient approach to cope with balancing communication costs on mobile devices. It offers a systematic approach to handling route management in case of route damages by evaluating the link performance. If any link has an increased data loss rate, then such paths are removed from the existing routing tables and updated with alternate and optimal routes. We contrasted the performance of the proposed algorithm with related work in terms of packet drop rate. According to Figure 5a,b, it was found that the proposed algorithm reduces the ratio for packet dropping by an average of 10% and 13%, respectively. This is due to the integration of fog nodes with mobile devices, which enhances the route maintenance phase and increases the stability of the chosen route. Moreover, by exploring the delay time over the particular link, the proposed algorithm efficiently determines the faulty links and marks them infeasible in the neighboring table. The forwarder nodes are continuously re-evaluated in their status based on the network metrics, and if they identified themselves as not sufficient to be a part of the routing phase, then they declare their status with the neighbors. The lightweight authentication process also increases the security against malicious devices and decreases the level of packets capturing and dropping. The performance comparison of the proposed algorithm and existing in terms of overhead is shown in Figure 6a,b. Under varying mobile devices and message generation intervals, it can be demonstrated that the proposed algorithm significantly improves the overhead by an average of 9% and 12% as compared to other work. This is because the proposed algorithm effectively manages the energy resources among mobile devices, and utilizes the support of fog nodes. The fog node reduces the transmission distance of the forwarders to transmit the environment data toward the sink node. The proposed algorithm also re-evaluates the routing states when the delay time over the communication link is greater than a certain threshold. Accordingly, it identified the congested routes from the available list and selected the alternative option for the transmission of the data. The security solution also decreases the non-authentic data flowing on the links and avoids malicious devices from flooding the false packets. Figure 4: (**a**) Network throughput with message generation interval. (**b**) Network throughput with mobile devices. The performance comparison of the proposed protocol with related studies in terms of reliability is illustrated in Figure 7a,b. With varying devices and data generation rates, it was observed that the proposed algorithm offers a high degree of reliability in network communication. We tested the reliability of the proposed algorithm and other solutions by deploying the malicious nodes, and it was noticed that the proposed algorithm increases the reliability by an average of 10% and 11%, respectively. This is due to the incorporation of a lightweight security scheme to verify the devices for involvement in the process of data forwarding. Moreover, keys are randomly generated, and by exploring the hybrid cryptographic principles, the proposed algorithm established the secured session by utilizing private and public keys. Once the node is declared authentic, the data routing phase is initiated for the particular node, with the combination of trusted and optimal forwarders. Figure 5: (a) Packet drop rate with message generation interval. (b) Packet drop ratio with mobile devices. Figure 6: (a) Overhead with message generation interval. (b) Overhead with mobile devices. Figure 8a,b illustrates the performance of the proposed protocol with other work in terms of varying times. We conducted two experiments under varying times for delay and the number of received packets. In both experiments, the proposed solution provides significant improvement by an average of 14% and 16%.