# Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
## 3 Proposed Multi-Criteria Learning Algorithm Using Secured Sensors  
Sensors integrated with IoT objects are utilized in different domains to gather data and support the community using a smart communication system. IoT network provides the processes of data collection and assists the end-users in observing and optimizing the transmission based on environmental conditions. In this section, we present the details of the proposed algorithm and its working flow.  
The proposed algorithm is comprised of two stages. In the first stage, D2D authentication is performed, and afterward, using the machine learning approach the optimal forwarding tables are established. The forwarding tables are updated based on the network conditions, which decreases the overheads in determining the optimal routes. The second stage provides the trustworthiness forwarding in terms of privacy and integrity from the observing field to network applications. In this stage, the proposed algorithm ensures the accuracy of the collected data and eliminates the number of attacks from unknown devices. Additionally, the proposed algorithm imposes the lowest computing cost and data diverting for ensuring security between mobile devices, with nominal communication delays. Figure 1 illustrates the development flow of the proposed algorithm.  
The contributions of the proposed algorithm are as follows:  
1. The first component is D2D authentication and key distribution. It consists of mobile devices and is associated with the inline gateway for obtaining the secret keys. Additionally, gateways are directly associated with the sink node for forwarding the network information to data centers.
2. Reinforcement learning is executed in the second component by fetching the nodes' statistics from the constructed forwarding tables along with information of packets' reception. The forwarding tables are updated iteratively, thus the proposed algorithm converges the desired outcomes optimally. Using the machine learning technique, the proposed algorithm imposes lower routing overhead on the constraint devices and informs about the latest information to mobile nodes by exploring network parameters.
3. The third component is secured IoT communication and accomplishing sustainable routing with the support of a D2D session-oriented system. It provides au  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Comparative Approaches** & **Pros and Cons** \\ \hline \multirow{7}{*}{Existing solutions} & Most of the existing solutions have proposed for efficient utilization of energy consumption with constraint devices and improved the performance of data delivery. \\  & However, it is noticed that some solutions can tackle mobile devices at the cost of frequent data lost and overloaded wireless channels. \\  & Although machine learning techniques are explored by different researchers for IoT networks; however, it was seen that they overlooked security threats such as privacy, integrity, and authentication for mobile devices. Such a solution affects the reliability of smart cities and compromised \\  & communication system in the presence of unknown machines. \\ \hline \multirow{7}{*}{Proposed D2D multi-criteria learning algorithm using secured sensors technologies} & An algorithm is developed for smart cities using reinforcement learning techniques based on devices and packets’ reception information. It supports gathering \\  & real-time data by imposing security restrictions for mobile \\ \cline{1-1}  & devices against malicious actions. Moreover, mobile devices \\ \cline{1-1}  & are verified first, and afterward, they are allowed to be \\ \cline{1-1}  & involved in the data-gathering phase. It also supports data \\ \cline{1-1}  & encryption with a session-oriented function and leads to \\ \cline{1-1}  & lightweight complexity for the mobile network. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related discussion.  
thentic and verifiable sessions between devices, gateways, and sink nodes with low-security costs.