Using Link Disconnection Entropy Disorder to Detect Fast Moving Nodes in MANETs

Area of Science:

• Computer Science
• Network Engineering
• Wireless Communication

Background:

• Mobile ad-hoc networks (MANETs) exhibit inherent dynamism due to node mobility and traffic.
• Evaluating node behavior is crucial for identifying optimal connection paths.
• Existing metrics may require specialized hardware like GPS.

Purpose of the Study:

• To propose a novel relative index for evaluating single node reliability in MANETs.
• To assess the effectiveness of link disconnection entropy disorder as a metric.
• To determine if this metric can identify nodes with unstable communications or high mobility.

Main Methods:

• Developed a relative index based on link disconnection entropy disorder.
• Utilized neighboring nodes as a reference for evaluation.
• Simulated various scenarios to test the index under different speeds and traffic densities.
• Correlated the metric with packet delivery ratio.

Main Results:

• Link disconnection entropy disorder effectively identifies fast-moving nodes and unstable communications.
• The metric does not require specialized sensors like GPS.
• A strong relationship was observed between link disconnection entropy disorder and packet delivery ratio.
• Network simulations successfully identified high-speed nodes using only the link disconnection entropy disorder.

Conclusions:

• Link disconnection entropy disorder is a viable metric for evaluating node stability and communication reliability in MANETs.
• The proposed index offers a practical alternative for assessing node behavior in dynamic wireless environments.
• This metric enhances the understanding of node dynamics and network performance without external hardware dependencies.