Improving Clustering Efficiency for Incentive-Based Mechanism in a Clustered D2D Network

Abstract

In today’s world, we have more than a trillion devices which communicate with each other through the networks. All users need reliable networks with high transmission speed and with minimum latency as well. To fulfil their requirements all the data travel through different infrastructure of the network which ultimately takes time because of congestion in the network. In this situation Device to Device communication plays important roles to reduce crowds in the network. We will try to analyse variety incentive mechanisms and their motivation as well. It will provides detailed information on incentive mechanisms and how Machine learning can be used here. We will go through all analysis of mechanisms and provides solutions to the current problem. We found many problem like user not ready to take part to help others by sharing resources and main reason is because they are not getting better rewards by completing task and along with resource are also being wasted. This mainly aim towards motivating users for taking part in communication and to continuously motivate them we need to provide some incentives to users so then can participate regularly. If we apply clustering of similar interest of device then it would be easy to search device and we can make D2D communication fast. In this research we apply different clustering mechanism and try to find best one so we can improve the efficiency of clustering and it helps to identify device in very effective way so we can reduce device discovery problem as well.