ESPEAR: Enhanced Stream Processing for Environmental Analytics and Renewable Energy Integration Using Smart Dielectric Sensors

This paper presents ESPEAR (Enhanced Stream Processing for Environmental Analytics and Renewable Energy), an innovative methodology integrating smart dielectric sensors with stream processing for environmental monitoring and renewable energy optimization. Building upon recent advances in green chemical reactions using inert solid dielectrics, we develop a comprehensive system that combines real-time environmental data processing with 5G millimeter-wave technology. The system implements the STAR cache-based warehouse architecture for efficient spatial data management while incorporating sustainable energy practices. Our experimental results across fourteen countries demonstrate significant improvements in processing efficiency (45%), energy consumption reduction (38%), and monitoring accuracy (92%) compared to existing methods. The implementation shows particular promise in smart city applications, achieving a 40% reduction in response latency while maintaining 99.9% system reliability.

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Publication Details

Published
2025-04-13
DOI
https://doi.org/10.1109/itikd63574.2025.11004739
Primary Topic
Water Quality Monitoring Technologies
Type
article
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article

ESPEAR: Enhanced Stream Processing for Environmental Analytics and Renewable Energy Integration Using Smart Dielectric Sensors

Sheila Mahapatra, Akey Sungheetha, Rajesh Sharma R, C H Sairam
Water Quality Monitoring Technologies
article

ESPEAR: Enhanced Stream Processing for Environmental Analytics and Renewable Energy Integration Using Smart Dielectric Sensors

Sheila Mahapatra, Akey Sungheetha, Rajesh Sharma R, C H Sairam
article en

Abstract

This paper presents ESPEAR (Enhanced Stream Processing for Environmental Analytics and Renewable Energy), an innovative methodology integrating smart dielectric sensors with stream processing for environmental monitoring and renewable energy optimization. Building upon recent advances in green chemical reactions using inert solid dielectrics, we develop a comprehensive system that combines real-time environmental data processing with 5G millimeter-wave technology. The system implements the STAR cache-based warehouse architecture for efficient spatial data management while incorporating sustainable energy practices. Our experimental results across fourteen countries demonstrate significant improvements in processing efficiency (45%), energy consumption reduction (38%), and monitoring accuracy (92%) compared to existing methods. The implementation shows particular promise in smart city applications, achieving a 40% reduction in response latency while maintaining 99.9% system reliability.

Alliance University (IN)
Affordable and clean energy
Openalex Percentile: Top 88%
Water Quality Monitoring Technologies
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