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Design and Implementation of a Multi-Tier Scheduling Framework for Real-Time Urban Water Logging Detection and Dispatch Optimization
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 54 |
| Year of Publication: 2025 |
| Authors: Harshvardhan Dwivedi, Asmita Gupta, Roshani Maurya, Jignesh Patel |
10.5120/ijca2025925915
|
Harshvardhan Dwivedi, Asmita Gupta, Roshani Maurya, Jignesh Patel . Design and Implementation of a Multi-Tier Scheduling Framework for Real-Time Urban Water Logging Detection and Dispatch Optimization. International Journal of Computer Applications. 187, 54 ( Nov 2025), 11-21. DOI=10.5120/ijca2025925915
Abstract
Urban waterlogging has escalated into a chronic and debilitating crisis across India, inflicting severe economic, infrastructural, and public health consequences. This systemic failure of modern urban water management stands in stark contrast to the sophisticated and resilient hydraulic engineering of the ancient Indus Valley Civilization. This paper introduces a novel Multi-Tier Scheduling Framework designed to address this contemporary challenge by drawing inspiration from ancient design philosophies while leveraging state-of-the-art technology. The framework employs a three-tier architecture—Perception, Fog, and Cloud—that facilitates real-time waterlogging detection, predictive analysis, and optimized emergency resource dispatch. The Perception Tier integrates a dense network of low-cost IoT sensors (ultrasonic and pressure) and fuses this quantitative data with qualitative insights derived from Natural Language Processing (NLP) of social media feeds and meteorological forecasts. The Fog Tier, operating at the network edge, utilizes a hybrid Transformer-Long Short-Term Memory (LSTM) deep learning model for low-latency, localized waterlogging prediction. The Cloud Tier orchestrates city-wide response, employing a metaheuristic optimizer based on a hybrid Ant Colony Optimization and Genetic Algorithm (ACO-GA) to solve the dynamic vehicle routing problem for emergency dispatch. A preemptive, priority-based real-time scheduler governs the entire framework, ensuring that time-critical tasks are prioritized during emergencies. A simulated implementation using geospatial and hydrological data from a flood-prone urban zone demonstrates the framework's efficacy. The results indicate a significant improvement in prediction accuracy and a substantial reduction in emergency response times compared to baseline models. This research presents a holistic, technologically advanced, and historically informed blueprint for building climate-resilient and intelligent urban water management systems in India and beyond.
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