Maximizing Resource Allocation for IoT Services in Fog Computing withLinear Programming : A Comparative Study

In the evolving landscape of Fog Computing, which extends cloud services to thenetwork edge, optimizing resource allocation for Internet of Things (IoT) servicesis crucial for enhancing performance and reducing latency. This study investigatesthe application of linear programming techniques to maximize resource allocationefficiency in Fog Computing environments. By comparing various linearprogramming models, the research aims to identify the most effective strategies fordistributing computational resources among IoT devices. The study evaluates theimpact of these models on key performance metrics such as energy consumption,computation delay, and overall system throughput. Results indicate that optimizedresource allocation significantly improves the quality of service (QoS) for IoTapplications, particularly in scenarios with high device density and stringentlatency requirements. The findings provide valuable insights for designing robustand efficient Fog Computing frameworks capable of supporting the growingdemands of IoT networks [۹,۱۰,۱۱,۱۲].

Keywords:

Fog Computing , linear programming , IoT services , LinearProgramming , Optimization , Quality of Services (QoS) , Latency Reduction