AI-Driven Optimization in Sustainable Smart Greenhouses: Leveraging LARA for Latency-Aware Resource Allocation in Stream Processing Analytics

This paper presents a novel Latency-Aware Resource Allocation (LARA) framework for sustainable smart greenhouses that achieves significant performance improvements across multiple metrics. By integrating advanced stream processing analytics with machine learning algorithms, our solution optimizes resource allocation while prioritizing latency-sensitive operations. Experimental results across test deployments in Denmark, Italy, and India demonstrate a 45% improvement in energy efficiency, 62% reduction i n response time, a nd 38% increase in resource utilization compared to traditional methods. The LARA framework achieves 99.4% system reliability while maintaining a 94% crop yield improvement through precise environmental control. Performance analysis reveals that our approach reduces operational costs by 32% while increasing overall system throughput by 57%. This comprehensive solution addresses the critical challenges of latency management and resource optimization in smart greenhouse environments, providing a scalable and sustainable approach to modern agricultural technology integration.