# Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements
## 8 Results Analysis and Discussion  
This research focused on the power consumption of the model, then the research proposed by the AI based DNN architecture. Before we develop an energy saving model, we need to understand the power consumption of the model and which parts need the protocols to perform energy conservation. After discussing the power consumption, the study then proposed an AI-based DNN architecture. The reason behind using the proposed model is that the model would show intelligent behavior and the decisions taken would be optimal, which would eventually make the model energy conservative. Our WBAN network using 48 source nodes and one coordinator was designed and simulated. The chest, belly, and arms all have nodes. Each node is at a permitted propagation distance from the sink node; moreover, the distances between the coordinator and the nodes are not the same. We used the Network OMNET++ environment to test our proposed WBAN protocol. Each source node receives 50 J of energy, except for the sink node, which receives 100 J for its numerous operations. Even though the allowed WBAN data rate ranges from 10 Kbps to 10 Mbps, the packet generation rate. OMNET++ is one of the most popular and powerful simulation frameworks. As the research is based on wireless-ness and the network of sensors, the OMNET++ is one of the best tools that could be implemented in order to achieve an optimal result. The OMNET++ would build network simulators in order to build a stronger network between the sensors, which is wireless. The sensors network would be handled and simulated by the OMNET++ which would help the model to drain less energy and provide with an optimal network for the model. Application for renewable energy modeling is a must to discuss as it is the result of the research. Most of the renewable energy using models is not monitored because of the abundance of the energy but it is crucial to optimize the energy to improve the efficiency of the model and so the research suggests this improved mode. Moreover, the renewable energy modelling would also describe how the model would perform in the long run. In contrast to the literature the new protocols and data transfer reliability are assessed. From the single hop point, various literature techniques tailor the transmission to a reliable transmission. WBAN configurations that are popular in the industry are used. PLR reduction is implemented as a redundancy measure, i.e., packet loss rate gain by sending the same data piece into the same transmission region using TDMA's packet loss rate strategy. At random between 0 and 1, with the exception that a value of 1 (i.e., r1 + r2 + r3 = 1) must be chosen. The selected parameters as modifications (r1: energy ratio, r2: connection reliability, and r3: SAR) have been thought distinct (keeping other parameters constant). In the application of individual metrics, the response of these measures varies from one to the other, with some indicators outperforming the others. The simulation results show that many parameters in the cost function must be included to meet such compromises. The proposed model is efficiency and provides the best energy savings results and maximizes long life. The QoS enhancement combines parametric cost function variations with a tweak to the minimum contest. Related studies have mainly focused on the renewable energy utilization but not on the optimization of sensor energy consumption. Their aim was to apply renewable energy to various models. Those studies presented excellent models that utilized minimal overall energy, but our research found the main source for energy wastage and suggested to optimize it. Some papers discussed the optimization of rotor speed to improve overall efficiency, but as we are aware that not all models would have rotors, this model would not be applicable to a majority of the models.