Hierarchical Fuzzy Clustering and Sleep Scheduling for Load Balancing in 5G-IoT Networks

Abstract

Introduction: Small cells have been added to Fifth Generation (5G) networks for handling the increasing demands for ubiquitous service and mobile traffic. Furthermore, it makes it possible for mobile broadband technology to be used everywhere to locate Internet of Things (IoT) applications. Existing research suggests strategies to maintain network segment distribution to various applications effectively.

Objectives: The major objective of this work is to balance the loads among the cells in IoT-5G networks.

Methods: This paper proposes Hierarchical Fuzzy clustering and sleep scheduling (HFC-SS) technique for load balancing in 5G-IoT networks. This approach creates and groups sub-segments for every network segment for handling IoTs applications with various resource demands. The hierarchical Fuzzy clustering techniques is applied for sub-splitting based on a cumulative rank of Quality of service (QoS) metrics. Then, an adaptive sleep scheduling technique is applied on every small cell base station (SBS) depending on its load. The overloaded traffic may be sent to the Macro cell under the load balancing policy (LBP), when the mean load of any SBS surpasses the Macrocell's load.

Results: The validation of simulation results against analytical results demonstrates that the proposed techniques provide maximum delivery ratio and power efficiency with minimized energy usage and drop rate of SBSs.

Conclusions: The proposed HTC-SS technique thus provides efficient load balancing for each SBS.