# Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
## 1 Introduction  
IoT-based technologies have gained numerous growth in the development of smart cities and support to real-time communication systems [1, 2, 3]. Wireless sensor networks (WSN) enable low-power deployments, and they have become the dominating choice in the composition of IoT devices. The technology of WSN is broadly utilized in various applications, such as precision agriculture, healthcare, vehicle transportation, smart cities, etc. [4, 5, 6]. It is comprised of tiny and battery-powered nodes with limited memory and transmission power, and such constraints restrict the amount of computation in facilitating the network users [7, 8, 9]. In large network domains, most of the solutions prefer the multi-hop paradigm rather than the single hop, which improves the connectivity among IoT networks and supports the connected devices for transmitting their collected data. However, with increases in data traffic, frequent changes occur in communication channelsand most of the network systems degrade their performance in terms of resource management and security. Many solutions have used the techniques of machine learning to make an intelligent decision and forward the monitoring data with nominal overhead [10; 11; 12]. However, most of the existing solutions are not able to cope with malicious threats in the existence of mobile IoT devices. Accordingly, it is easy to degrade the performance of smart cities while transferring the gathered data among the D2D communication system over the unreliable source of network channels [13; 14; 15]. Furthermore, due to the constraints in devices for resources, authentication, integrity, and security are other significant parameters in transmitting the smart data from the critical field [16; 17; 18]. Therefore, network devices must be protected from unauthorized access and maintain their accuracy in terms of privacy and trustworthiness.  
This study aims to propose a D2D multi-criteria reinforcement learning algorithm using secured and mobile IoT devices. It offers an efficient way of collecting sensors' data and utilizes the technique of machine learning with the least overheads on sensors and provides intelligent methods for ensuring low-latency data routing. Additionally, unlike most of the existing solutions, the proposed algorithm increases the robustness of the mobile network for security, given as follows. (i) Mobile devices are authenticated in their transmission radius before connecting to the route discovery process, and after mutual verification, the proposed algorithm allows them to become involved in the data routing. (ii) It offers a trusted analysis of security measurements and improves enhancement in terms of privacy with data integrity in mobile networks. (iii) Moreover, it provides the security flow between three stages i.e., mobile devices, gateways, and the sink node. The significant contributions of the proposed work are as follows:  
1. D2D authentication algorithm is developed for a mobile network to ensure the authenticity and trustworthiness between partial and fully-connected nodes.
2. Using a multi-criteria process, the reinforcement learning technique is applied and the network system is trained using realistic conditions. The proposed algorithm offers the selection of optimal neighbors using the computation of rank value that is comprised of energy, speed, link cost, and radio coverage. Accordingly, it reduces the sizes of routing tables and avoids excessive routing intervals.
3. Moreover, the proposed algorithm protects devices and attains uncompromised data against security attacks.
4. The simulations are performed to verify the improvement of the proposed algorithm in the comparison of existing work.  
The rest of the article is divided into the following subsections: The related work and problem statement are described in Section 2. Section 3 explains the proposed algorithm with flow diagrams. Simulation configuration and experimental results are discussed in Section 4, and Section 5 provides the conclusion with suggestions for future work.