It is becoming more common to use Wireless Body Sensor Networks (WBSNs), which are groups of sensors that can sense, process, and communicate to more accurately monitor remote or mobile subjects at reduced costs in many fields, such as healthcare, environmental monitoring, and smart infrastructure. Sensors, referred to as nodes in these networks, manage four essential functions: gathering, transmitting, receiving, and processing data. Consequently, they must utilize resources efficiently, optimizing memory usage, CPU cycles, and above all, energy consumption to extend network lifetime and ensure reliable data transmission. In this paper, we present an energy informatics-driven model that characterizes and optimizes energy loss patterns in typical WBSN deployments, integrating its diverse energy components within a unified framework. This model provides a systematic approach for evaluating and enhancing the energy efficiency of sensor-based applications by simplifying component interactions and identifying critical energy-saving opportunities. The goal is to develop an Energy Mapping Architecture tailored for WBSN applications, encapsulating both fundamental and application-specific energy components, and modeling how these elements interact to impact the total system consumption. We propose a Self-Calibration TDMA (SC-TDMA) method that adaptively minimizes energy waste and queue overheads by dynamically adjusting node transmission schedules based on real-time network conditions. The proposed energy-efficient topology management strategy is benchmarked against conventional TDMA approaches, demonstrating its capacity to establish reliable, low-latency links between nodes with significantly reduced energy footprints. Extensive simulation experiments conducted on randomized WBSN scenarios validate that SC-TDMA algorithms substantially enhance energy efficiency, throughput stability, and communication reliability, affirming their suitability for next-generation energy informatics applications.