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Title	A Novel Iot-Driven Model For Real-Time Urban Wildlife Health And Safety Monitoring In Smart Cities
ID_Doc	3415
Authors	Dasari K.; Ali M.A.; Shankara N.B.; Reddy K.D.; Bhavsingh M.; Samunnisa K.
Year	2024
Published	8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024 - Proceedings
DOI	http://dx.doi.org/10.1109/I-SMAC61858.2024.10714601
Abstract	This study introduces an innovative IoT-driven model designed to enhance the monitoring and protection of urban wildlife in smart cities. Addressing the key limitations of existing systems, the model overcomes issues related to the lack of real-time data integration, signal interference, and excessive power consumption, which compromises the data accuracy and system sustainability. The proposed model integrates a network of advanced IoT sensors that continuously monitor environmental and wildlife health indicators coupled with real-time data processing units that perform filtering, aggregation, and analysis. Predictive analytics is employed to proactively identify potential threats to wildlife, shifting the approach from reactive to proactive conservation. Robust security and privacy protocols ensure data confidentiality and system resilience. The study demonstrated significant improvements, including a 15-20% increase in data accuracy, 25% boost in energy efficiency, and 70% reduction in response delays. These results highlight the effectiveness of the proposed model in providing timely, accurate, and secure wildlife monitoring. The scalability and adaptability of the model suggest its potential for broader applications in urban management and environmental monitoring, reinforcing its role in advancing sustainable, wildlife-friendly infrastructure within the framework of smart city development. © 2024 IEEE.
Author Keywords	Environmental Monitoring; IoT-Driven Model; Predictive Analytics; Real-Time Data Processing; Smart Cities; Sustainability; Urban Wildlife Monitoring

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