Mobility Aware Handover Execution Model for Heterogeneous Wireless Networks

Modern (Internet of Things) IoT applications requires seamless connectivity with high reliability; thus the IoT device are connected to heterogeneous wireless network by integrating LTE and mmWave network. mmWave offers better bandwidth and less latencies for achieving high p[performance]. However, due to direction antenna and high dependencies on cloud lead to coverage blindness/hotspot and interference problems. The IoT device are mobile in nature and is powered by batteries; poor handover execution will lead to wastage of energy. Thus, this paper present mobility aware handover execution (MAHE) algorithm by reducing signaling overhead through XGBoost machine learning algorithm. Further, for mitigating interference obstacle aware path loss component is considered. Experiment outcome shows the MAHE model achieves better handover success rate, less handover failure, and better throughput when compared with standard handover execution methodology.