Research

Current urban drainage systems are under increasing strain due to rapid urbanization, resulting to frequent blockages, urban flooding, and massive public health hazards. To address these issues, we design an event-driven edge intelligence framework that employs spiking neural networks (SNNs) for early detection of blockage-related anomalies in sewer pipelines. Unlike traditional IoT monitoring approaches that rely on centralized processing and continuous data sampling, the proposed system executes local and spike-based inference at distributed sensor nodes. With the help of environmental and physical sensor signals, we collect water-quality field parameters such as conductivity, turbidity, and flow and encode them into spike events by capturing significant deviations from baseline conditions. These events are processed by lightweight SNNs to classify pipeline conditions into four different states: (i) normal; (ii) contaminated; (iii) partial blockage, and (iv) severe blockage. To demonstrate the practicality of our method, we implement a lightweight neuromorphic inference testbed using ESP32 microcontroller with multi-sensor feature fusion approach. Experimental results show that our proposed framework achieves an overall accuracy of 86.60%, with balanced precision (86.40%) and a F1- score (86.43%), demonstrating reliable and efficient multi-class anomaly detection. 

This is the era of data-intensive artificial intelligence in which conventional computing architectures are increasingly constrained by energy consumption and memory bandwidth limitations, which hinder scalability and sustained innovation. Neuromorphic computing, inspired by the remarkable efficiency of biological neural systems, is evolving with the integration of principles from computational neuroscience. This review surveys computational neuroscience inspired neuromorphic computing, covering key methods, biological inspiration, neuromorphic hardware architectures, applications, and emerging technologies developed to address challenges in energy efficiency, scalability, and realtime intelligent processing.