Ultra-Low Latency Emergency Event Detection Using Cloud Integrated Teen Sensor Architecture

Abstract

WSNs (WSNs) play a critical role in environmental monitoring, industrial automation, and real-time event detection by enabling large-scale data collection through energy-constrained sensor nodes. However, efficient data transmission remains a major challenge due to limited battery power, dynamic network topology, and increasing communication overhead. Traditional routing approaches in WSNs primarily rely on hopbased shortest path routing, where data packets are forwarded through the minimum-hop route without considering node energy levels or sensing conditions. While simple to implement, such methods often lead to uneven energy consumption, increased latency, higher error rates, and premature node failures, thereby reducing overall network lifetime and reliability. To address these challenges, this research focuses on the design and analysis of an energy-aware routing mechanism inspired by the Threshold Sensitive Energy Efficient Network (TEEN) concept. The proposed system models an optimized routing strategy that emphasizes efficient path selection using weighted links and adaptive routing behavior, aiming to reduce unnecessary transmissions and improve communication performance. A simulation-based framework is developed using graph-based network modeling to compare the proposed TEEN routing approach and traditional hop-based routing. Performance evaluation is carried out using key metrics such as latency, mean square error (MSE), bit error rate (BER), hop count, and path length. Experimental results demonstrate that the proposed approach significantly outperforms traditional hop-based routing in terms of reduced latency, lower error rates, and improved routing efficiency. The proposed system highlights the importance of energyaware routing strategies in enhancing the performance, scalability, and sustainability of WSNs.