Energy-aware latency minimization in multi-source spatiotemporal data fusion for IoT networks

As smart city infrastructures evolve, they generate massive volumes of spatiotemporal data from diverse sources such as surveillance cameras, wearable health monitors, drones, and environmental sensors. Efficient fusion of this data is crucial for applications that demand ultra-low latency (typically between 2 ms and 5 ms), to support time-sensitive operations. However, the heterogeneity of data sources, coupled with constraints on communication bandwidth and computational resources, poses a significant challenge to maintaining such stringent latency and energy efficiency standards. This study proposes a joint optimization framework for multi-source spatiotemporal data fusion that dynamically allocates bandwidth and CPU cycles to minimize a weighted objective of latency and energy consumption under realistic wireless channel conditions and strict resource constraints. The results demonstrate that the proposed method consistently delivers low latency and energy-efficient performance across all data sources. It outperforms traditional equal and delay-tolerant strategies by significantly reducing both latency and energy consumption. This efficiency, combined with the framework's robustness andscalability, makes it highly suitable for smart city applications.