# Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors
## 2 Related Work  
IoT is one of the most promising technologies of the current era and interacts with sensors for observing the physical world [19; 20; 21]. These technologies have expanded into the real-time environment and support the applications to govern their operations. Recently, many solutions have presented to optimize the transmissions and increase the accuracy of online data retrieval systems. The authors of [22] determined the required resources of energy at the BS for IoT-enabled systems. Numerous agricultural sensors have utilized in precision agriculture for continually monitoring the field and communicating with the smart nodes. They presented a unique product density model for estimating the energy requirements for BS. Additionally, a method for Improved Duty Cycling was provided that makes use of the residual energy parameter. The proposed routing protocol [23] employs a region-based static clustering technique to efficiently cover the agricultural area while utilizing threshold-sensitive hybrid routing to send sensed data to the base station. In addition, the proposed protocol uses fuzzy logic to select the optimal cluster head (CH) among all sensor nodes in a given round, minimizing node energy usage during each data transmission period. The suggested energy-efficient protocol is compared to establish benchmark protocols, such as energy-efficient heterogeneous clustering (EEHC), developed distributed energy-efficient clustering (DDEEC), and region-based hybrid routing (RBHR). The research and testing findings indicate that user-defined transmission thresholds substantially decrease the data transmission rate. Furthermore, the balanced employment of fuzzy logic, static clustering, and hybrid routing effectively reduces the energy consumption of sensor nodes throughout each data transmission round, therefore extending the network's total lifetime. In [24], the authors proposed PAwCOR to develop a distributed method for the selection of CH by using node energy, latency, and congestion characteristics. Energy saving is accomplished via the use of nodes that are selected depending on sensing inaccuracy. PAwCOR enables the application of periodic data with the least amount of delay possible via the use of various routing routes. Additionally, it fulfills the need for non-delay-tolerant applications by utilizing service differentiation to prioritize time-critical data transmission. By allocating at least one route for both essential and routine data transfers, the suggested method improves performance compared to current protocols. It improved the performance in terms of latency, average energy consumption, packet delivery ratio, and average residual energy to attain reliable transmission. The authors of [25] proposed CTEER, an energy-efficient routing protocol based on cluster trees, to address the fast energy loss experienced by ordinary nodes while using the conventional static routing tree method. This protocol is a rendezvous-based method with a low-delay characteristic. As a result, the protocol is well-suited for time-critical applications, such as network live broadcast systems, automated railway operation systems, ticketing software, and intelligent home systems. It creates a cross-routing tree in which the mobile sink serves as the central node. Clustering algorithms are used to group the ordinary nodes and aggregate the data packets based on the routing tree. The suggested approach outperforms RRP in terms of the network lifecycle, energy consumption, and data latency. The authors of [26] proposed a deep-reinforcement learning-based quality-of-service (QoS)-aware secure routing protocol (DQSP). It aims to ensure the QoS and extract knowledge from traffic history by cooperating with the observing environment. Moreover, the proposed protocol optimizes the policies of routing. It performs significant improvement under different network metrics and has proven high convergence and effectiveness.