An efficient channel assignment plays an important role in mitigating co-channel interference in ultra-dense wireless networks. A simple solution is to separate interfering network nodes into orthogonal channels to reduce the interference among them. However, determining the optimal channel assignment is considered to be a non-linear problem, which may also be associated with practical implementation issues such as high computational complexity and control signaling issues. In an effort to cope with these challenging issues, we propose a distributed channel assignment algorithm that efficiently finds the optimal channel configuration by utilizing the concept of belief propagation. Based on a message-passing framework, the proposed distributed channel assignment algorithm maximizes the overall sum rate of the ultra-dense network with a low computational load for each network node. In addition, we design a network protocol and frame format to implement the proposed message-passing framework to real-world wireless networks. The main advantage of the proposed approach is that network nodes autonomously determine the optimal channel assignment and rapidly adapt to dynamic changes of the network. Simulation results confirm that the proposed distributed channel assignment algorithm outperforms conventional algorithms in terms of various network performance aspects, such as the sum rate, scalability, latency, and user mobility.INDEX TERMS Ultra-dense networks, channel assignment, distributed control, message passing, belief propagation.

Dipterocarpus littoralis known locally as pelahlar, is a producer of endemic commercial wood in Nusakambangan. Pelahlar habitat that is isolated in one island and a narrow genetic distribution causes the risk of extinction of this species is increasing. The effort to conserve rare flora can be done by monitoring and observation the balance of essential nutrients at the site of growth. So we need a system that is able to monitor the balance of essential nutrients and pH in the soil to support the growth of rare flora based on the Internet of Things (IoT). This system consists of a sensor station and a central gate station that is connected based on a star network. scheduling of sending data from the sensor station to the gate station is determined from the results of competition between sensor stations based on the backoff of each sensor station. through this scheme an increase in throughput and collision values is based on the number of sensor stations. From our proposed scheme, it is found that the more sensor stations that compete, the smaller the value of the throughput of the system.