The Efficient Mobile Management Based on Metaheuristic Algorithm for Internet of Vehicle

Area of Science:

• Telecommunications Engineering
• Computer Networks
• Mobile Communication Systems

Background:

• Fifth-generation (5G) mobile networks enable ultra-low latency and high reliability applications.
• The Internet of Vehicles (IoV) is a critical application demanding robust communication for safety.
• 5G's use of millimeter waves and complex infrastructure necessitates advanced handover management.

Purpose of the Study:

• To address handover selection challenges in 5G networks for IoV applications.
• To optimize network performance considering delay, energy efficiency, and load balancing.
• To propose an effective mobile management mechanism for seamless vertical and horizontal handovers.

Main Methods:

• Formulating handover selection as a multi-objective optimization (MOO) problem.
• Developing a mobile management mechanism based on location prediction.
• Integrating heuristic algorithms with location prediction for handover decisions.

Main Results:

• The proposed mechanism outperforms distance-based methods in energy efficiency, load balancing, and latency.
• Optimizations exceeding 20% were observed in key performance indicators.
• Demonstrated effectiveness in managing complex 5G environments for IoV.

Conclusions:

• The developed location-prediction-based handover mechanism significantly enhances 5G network performance for IoV.
• The multi-objective optimization approach effectively balances competing network demands.
• This research provides a viable solution for improving reliability and efficiency in critical IoV communications.