Efficient IoT Devices Deployment Using Branch and Bound Method

The Internet Of Thing (IoT) networks are widely investigated in 5G system and will be still a key technical system to drive massive connectivity of 6G systems. As the IoT devices and networks are getting smarter, the IoT ecosystem allows us to bridge between human life and digital life and accelerate the transition towards a hyper-connected world. Optimal and scalable IoT network design has been investigated in many research groups but key challenges in this topic still remain. In this paper, we investigate IoT devices deployment problem to minimize the transmission and computation cost among network nodes. We formulate the IoT devices deployment problem as Mixed-Integer Nonlinear Programming (MILNP) problem. After relaxing the constraints and transforming the problem to a mixed integer linear programming (MILP) problem, we propose a new branch and bound (BB) method with a machine learning function and solve the MILP problem. In the numerical analysis, we evaluate both conventional BB method and the proposed BB method with weighting factors and compare the objective function values, the number of explored nodes, and computational time. The performances of the proposed BB method are significantly improved under the given simulation configuration. We find the optimal mapping of IoT devices to fusion nodes.