Task Scheduling in Smart Cities using Fog/Cloud Computing: Optimizing Energy Consumption and Traffic Flow

This study introduces a hybrid fog-cloud computing model to optimize resource use in smart cities by addressing real-time data processing, dynamic task allocation, and renewable energy management. A multi-objective scheduling algorithm balances energy efficiency, reduces latency, and improves traffic flow. Simulations show a ۳۳% reduction in congestion delays, ۲۰% higher traffic throughput, and energy savings of ۳۶% (peak) and ۲۵% (off-peak), with ۴۵% increased renewable energy use. The scalable, modular framework enhances sustainability, public safety, and emergency coordination, marking a paradigm shift in urban planning toward energy-efficient, resilient cities adaptable to various urban environments.

Keywords:

Fog-Cloud Computing , Task Scheduling , Smart Cities , Energy Efficiency , Traffic Optimization