APRA: Affinity Propagation-Based Resource Allocation Scheme in M2M for System Capacity Maximization

In this paper, we propose an enhanced affinity propagation (AP)-based resource allocation scheme (APRA) to overcome major issues in machine-to-machine (M2M), such as delay, complexity, throughput, and system capacity. There would be rapid increase of added devices, such as cellular and machine-type devices. It would be difficult for Evolved Node B (eNB) to control all of them. Considering this problem, we propose an AP-based group formation method in which machines make groups with other similar type of machines. After making groups, group members in each group can communicate directly with each other by getting a channel from eNB via their group head. A resource allocation method is proposed for different groups that can use the same channel at the same time. Considering energy constraints, we also propose different methods to rotate the role of a group head among group members, through the modification of AP or the application of Markov chain model. As expected, the group head will drain energy at a higher rate than the group members. Thus, the rotation of the group head will increase the overall performance. Simulation results show that the proposed method can minimize both data delivery delay and operation complexity while increasing the throughput, system capacity, and energy efficiency through the rotation of the group head.