IoT Service Placement Optimization in Fog Computing using Linear Programming: A Comparative Study

Optimizing IoT service placement in fog computing using linear programming focuses on optimizing IoT service placement in fog computing environments using linear programming techniques. Fog computing, as a decentralized computing infrastructure, aims to reduce latency and improve quality of service (QoS) for real-time IoT applications by providing storage and computing space adjacent to IoT devices. The abstract discusses the challenges of deploying IoT services in fog nodes and the need for optimization to balance various objectives such as bandwidth cost, energy consumption, delay minimization, load balancing, QoS, and security. The use of linear programming models, such as mixed integer linear programming (MILP) and integer linear programming (ILP), highlights these challenges. The abstract also emphasizes the comparative study of different linear programming approaches and their effectiveness in optimizing the deployment of IoT services in fog computing environments, taking into account criteria such as latency, energy consumption and cost efficiency[2,3,4,5,7].

IoT Service Placement Optimization in Fog Computing using Linear Programming: A Comparative Study Keywords:

Fog Computing , linear programming , IoT service placement