# Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
## 4 Simulation Setup
### Results and Discussion
Figure 6: The performance evaluation of the proposed algorithm compared to QL-MAC and CTEER for packet delivery ratio. Figure 7a,b illustrates the performance evaluation of the proposed algorithm for packet disturbance with existing solutions. It can be determined as a ratio of the number of packets lost to the data packets transmitted in a communication system. Compared to other work, the proposed algorithm improves the packet disturbance ratio by an average of 34% and 28% under a varying number of nodes and rounds. This is because the proposed algorithm does not overlook the constraint resource of the nodes and uniformly distributes the communication load among devices using reinforcement learning. Unlike QL-MAC and CTEER, the proposed algorithm utilizes network conditions in terms of multiple criteria and balances the data distribution on transmission links with the evaluation of the link cost. The proposed algorithm utilizes the PRR and response time factors in determining the optimal neighbors from the set of nodes. Additionally, with better utilization of the energy consumption of data forwarders, the proposed algorithm increases the strength of routes and prolongs the stability of the transmission system. Moreover, the D2D multi-criteria reinforcement learning-based routing decision avoids the chance of selecting faulty and overloaded links for the forwarding of IoT data. Based on the reward value, the proposed algorithm increases the efficiency for learning and offers a stable routing performance. Unlike QL-MAC and CTEER, the authentication and verification process of the proposed algorithm offers trustworthy communication among devices and supports improved packets' distribution over the links. Figure 7: The performance evaluation of the proposed algorithm compared to QL-MAC and CTEER for packet disturbance. Figure 8a,b illustrates the experimental results of the proposed algorithm compared to the existing solution. It measures the round-trip time of forwarding the network data towards its destination in the communication system. It was observed that the proposed algorithm significantly decreased the data delay by an average of 20% and 23% for varying nodes and rounds. The proposed algorithm utilizes the concept of mobile sensors that rapidly shift their positions for the observation and forwarding of network data. In addition, the multi-criteria parameters in the forwarding scheme explicitly achieve optimal performance for constraint devices. Unlike most of the other proposed reinforcement learning schemes, the proposed algorithm assigns the appropriate rewards to nodes, decreasing the response time and data delay for smart mobile devices. Moreover, it uses the error rate metrics in determining the loss ratio, and thus only optimal neighbors whose link cost is not congested are chosen for data routing. The D2D direct authentication and verification in the routing of data packets also decrease the involvement of unauthorized nodes. Such an approach improves the transmission path, avoiding unnecessary delays and retransmissions. Unlike other solutions that impose overheads for securing the data forwarding and lead to a high delay rate, our proposed algorithm offers lightweight session keys based on secure routing, which explicitly minimizes the latency ratio on communication paths. In Figure 9a,b, the performance analysis of the proposed algorithm is evaluated compared to other solutions in terms of energy consumption. It is computed as a ratio of  
Figure 8: The performance evaluation of the proposed algorithm compared to QL-MAC and CTEER for data delay. depleted energy to the total network energy in data sensing, receiving, and transmitting. It was found that the proposed algorithm minimized the energy consumption by an average of 22% and 27% under a varying number of nodes and rounds. This is because of the uniform load distribution among forwarders based on the machine learning technique.