# Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements
## 1 Introduction  
WBAN's electronic health (eHealth) management system facilitates the integration of patient data collection and communication with healthcare resources or devices and is a potential method for improving healthcare quality. Sensor nodes in WBAN track the condition of the patients and transfer the summarized data to the doctor [1]. As a result of this study [2], a comprehensive approach to developing a maturity model was needed. That will include a wide variety of factors that concern healthcare facilities in all sectors and subsystems. In this paper [3], the author has represented the life of medical WBANs by using the relationship between disease recognition and applying the scheduling algorithm on the medical sensors. [PERSON] et al. [4] suggests an energetic, effective distributed adaptive cooperative routing (EDACR) to WMSN following quotas and power usage restrictions as a disclosure of the energy efficiencies issue as a discretionary and finite-state decision-making mechanism (DFMDP). Reference [5] finds that WBANs face several matters of operations, standardization, and protection that concern consumer protection and privacy efficiency and maintenance. In reference [6], the proposed technology scans the sensors at near-optimum positions at various times, using the shortest path algorithm, to route information from target to destination. The wireless charging algorithm [7] proposed for WBAN implanted SNs is designed to be multi-collision prevention (CSMA/CA) per carrier sense access process. We used fusion protocols with improved performance parameters to create an energy-efficient [8] and stable WBAN link in this chapter. We demonstrated that durability while maintaining a low SAR level. Since a powerful network application necessitates a large number of network resources, CW and back-off mechanism adjustments in the MAC improve network capacity, PDR, energy consumption, and life span in various parametric combinations. Similarly, the latency and routing overheads for different parameter combinations show significant sleep variations. Cooperation with the proposed methods increased overall network reliability, as shown by the discussion of simulation outcomes, even though maximizing all performance parameters simultaneously is difficult in WBAN [9] performance enhancement. Instead, the WBAN implementation option takes priority over essential output parameters [10] (Sleep Mode, Network lifetime, Path loss, Stability, Residual energy, Packet delivery ratio). Therefore, various low-layer approaches can be added to promote improved network performance [11] and upper-layer protocols based on the sensitivity of the WBAN program. The methods employed in this study generally offered connection efficiency, stability and enhanced various network capacity parameters significantly [12]. Strengthening training focused on the Deep Neural Network [13]. The approach to improving learning is first to sensitize the radio environment and decide on a channel's fate. No mathematical channel model has been established by IEEE 802.15.6, the IEEE standard paper [14], however, contains experimented values that are the basis for such a model. Based on these experimental findings, we developed a mathematical model that considers the posture of the body and the shadowing results. The WBAN sensors based on WSN sensors consist of a limited number of sensor nodes which work on low and unstable power energy. Several networks use parameters like communication, data storage, and the ability to perform several processes and sensing data. The WBAN sensors are capable of working on solar energy and wind energy, which are renewable energy resources. The WSN sensor working is completely based on low energy power systems, therefore the energy generated is unstable. Because several sensors run continually, renewable energy resources can be used to generate a continuous flow of energy. We suggest a new energy allocation algorithm to achieve the optimal degree of interference at the gateway boundary.