Selfish Behavior in IEEE 802.11ah Networks: A Detection Algorithm and Mitigation Strategies

Area of Science:

• Computer Science
• Electrical Engineering
• Networking

Background:

• IEEE 802.11ah introduces Restricted Access Window (RAW) to manage Internet of Things (IoT) network contention.
• Selfish station behavior can degrade overall network quality of service (QoS) by prioritizing individual throughput.

Purpose of the Study:

• To analyze the inherent fairness of the IEEE 802.11ah protocol.
• To evaluate the impact of selfish stations on network fairness, throughput, and packet loss.
• To develop and validate a novel algorithm for detecting and mitigating selfish behavior in IEEE 802.11ah networks.

Main Methods:

• Simulation of IEEE 802.11ah networks with varying percentages of selfish stations.
• Analysis of existing IEEE 802.11 selfish behavior detection algorithms for applicability.
• Proposal of a new definition for selfish behavior tailored to IEEE 802.11ah.
• Development of the Selfish Stations Quarantine Punishment Algorithm (SSQPA) with two variants.

Main Results:

• IEEE 802.11ah exhibits inherent fairness in its default configuration.
• Existing detection algorithms perform poorly on IEEE 802.11ah.
• The proposed SSQPA effectively mitigates the negative impacts of selfish stations.
• Both honest station-centric and network-centric SSQPA variants demonstrate success in improving QoS.

Conclusions:

• IEEE 802.11ah is susceptible to performance degradation from selfish behavior.
• A novel detection and mitigation strategy (SSQPA) is crucial for maintaining QoS in IEEE 802.11ah networks.
• SSQPA offers flexible deployment options to suit network objectives, enhancing fairness and throughput while reducing packet loss.