Energy Efficient Adaptive Sensing Framework For Wsn

• Author(s):

  Mr. Balaji | Gayathri K | Dharshini M | Jayashree T | Annapoorani S

• Keywords:

• Abstract:

  Energy Optimization Remains A Fundamental Challenge In Contemporary Sensor-based Platforms, Particularly In Domains Such As Environmental Surveillance, Precision Agriculture, Biomedical Telemetry, And Wireless Sensor Networks (WSNs). These Platforms Are Frequently Deployed In Inaccessible Or Energy-constrained Environments, Where Periodic Maintenance And Battery Replacement Are Impractical. Conventional Architectures, Which Operate On Static Sampling Frequencies And Transmission Intervals, Tend To Overutilize Energy Resources Irrespective Of Contextual Data Relevance. This Research Introduces A MATLAB-centric Simulation Environment That Models An Adaptive Sensing Paradigm, Designed To Dynamically Reconfigure System Parameters Based On Real-time Signal Intelligence. At The Core Lies An Adaptive Control Algorithm That Processes Incoming Data Streams To Assess Contextual Significance Using Real-time Statistical Metrics—primarily Variance Analysis, Entropy Measurements, And Event-triggered Thresholds. Based On This Analysis, The System Modulates Its Sensing Resolution And Communication Cycles: Reducing Operational Intensity During Steady-state Conditions, And Ramping Up Activity During Transient Or Critical Events To Maintain Data Integrity And Temporal Accuracy. The Proposed Simulation Framework Emulates Sensor Behavior Using Synthetic Signal Profiles While Implementing Intelligent Duty Cycling Strategies For Both Sensing And RF Communication Subsystems. MATLAB Scripting Facilitates Precise Power Modelling, Enabling A Quantitative Comparison Between Static And Adaptive Configurations. Experimental Evaluations Across Diverse Sensor Modalities Demonstrate Energy Savings Of Up To 45%, With No Degradation In Responsiveness Or Sensing Fidelity. In Many Scenarios, The System's Adaptive Prioritization Mechanism Enhances The Semantic Relevance Of Captured Data By Focusing Resources On Periods Of High Informational Value. A User-configurable Graphical User Interface (GUI) Is Integrated For Interactive Visualization, Parameter Manipulation, And Environmental Scenario Testing. The Modular Software Architecture Supports Seamless Integration Of Heterogeneous Sensors And Extensible Control Logic, Promoting Scalability For Future Research Or Application-specific Adaptations. One Of The Primary Contributions Of This Work Is The Demonstration That Software-defined Adaptive Sensing Can Be Effectively Modeled And Validated In MATLAB Without Reliance On Physical Hardware, Making It An Accessible And Powerful Tool For Prototyping, Academic Instruction, And Sustainable System Design. The Framework Embodies Principles Of Green Engineering By Optimizing Operational Energy Profiles Through Software Intelligence, Contributing To The Development Of Next-generation Low-power Embedded Sensing Systems.

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Other Details

• Paper id:

  IJSARTV11I5103660

• Published in:

  Volume: 11 Issue: 5 May 2025

• Publication Date:

  2025-05-24

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