Energy-Efficient Protocols and Autonomous Data Management in Wireless Sensor Networks for Real-Time Psychology Class Applications Article Swipe
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.58346/jowua.2025.i4.029
This paper introduces an energy-efficient communication structure and an independent data management plan for Wireless Sensor Networks (WSNs) in a real-time psychology class setting. Since the contemporary classroom is becoming highly dependent on the continuous collection of physiological and behavioral data, including engagement tracking, cognitive-load measurement, emotional-state detection, etc., the provision of sustainable, low-power, and self-organizing sensing systems is becoming an urgent issue. The suggested framework incorporates adaptive duty cycling, context-sensitive routing, and lightweight data aggregation to drastically reduce power consumption while preserving high data fidelity and low latency. Real-time preprocessing, noise filtering, and event-driven data prioritization are enabled by an autonomous data management layer backed by edge-level intelligence, ensuring that only valid data is transmitted to the centralized analysis unit. The protocol also includes predictive load balancing to increase network durability and fault resilience when used in dynamic classroom applications. Simulated psychology-class environment experimental assessments show significant enhancements in network lifetime, lower communication overhead, and consistent real-time performance across changes in student activity levels. The paper notes the possibility of intelligent WSN design to support emerging real-time psychological analytics, enabling more responsive, data-driven, and energy-aware learning settings.
Related Topics To Compare & Contrast
- Type
- article
- Landing Page
- https://doi.org/10.58346/jowua.2025.i4.029
- https://doi.org/10.58346/jowua.2025.i4.029
- OA Status
- bronze
- OpenAlex ID
- https://openalex.org/W7108644724