A reinforcement sensor embedded vertical handoff controller for vehicular heterogeneous wireless networks.

Area of Science:

• Vehicular Communications
• Wireless Networking
• Mobility Management

Background:

• Vehicular communication relies on IEEE 802.11p, often integrated with cellular networks for extended coverage.
• IEEE 802.11p faces challenges in heterogeneous networks due to limited coverage and decreasing throughput with more users.
• Vehicular communication sessions in WLANs are typically short, demanding efficient mobility management.

Purpose of the Study:

• To propose a novel vertical handoff control strategy for vehicular communication networks.
• To enhance mobility management and ensure seamless connectivity for high-mobility users.
• To address the limitations of existing solutions in heterogeneous vehicular network environments.

Main Methods:

• Development of a reinforcement sensor (RFS) with online learning capabilities.
• Integration of vehicular mobility, traffic load, handoff latency, and network status into the RFS algorithm.
• Simulation-based evaluation of the proposed RFS embedded vertical handoff control strategy.

Main Results:

• The RFS algorithm demonstrates adaptive learning for optimal handoff decisions without prior knowledge.
• Simulation results confirm the algorithm's ability to adapt handoff strategies for optimal network selection.
• The proposed strategy effectively maintains user connectivity to the best available network.

Conclusions:

• The reinforcement sensor (RFS) provides an effective solution for adaptive vertical handoff control in vehicular networks.
• The strategy guarantees a high-quality user experience by ensuring continuous and optimal network connectivity.
• The RFS approach addresses key challenges in heterogeneous vehicular network management.