Coverage Probability and Area Spectral Efficiency of Clustered Linear Unmanned Vehicle Sensor Networks

Area of Science:

• Robotics and Network Engineering
• Wireless Communication Systems
• Sensor Networks

Background:

• Unmanned vehicle (UV) sensor networks are increasingly used for swarm sensing.
• Linear structures like highways and pipelines present unique deployment challenges.
• Understanding network performance under interference is crucial for effective UV deployment.

Purpose of the Study:

• To analyze the coverage and area spectral efficiency of clustered UV sensor networks in a linear topology.
• To model UV network structures using the Thomas cluster process (TCP).
• To investigate the impact of co-channel interference on network performance.

Main Methods:

• Modeling clustered UV sensor networks using the Thomas cluster process (TCP).
• Utilizing Poisson point process (PPP) for cluster locations and normal distribution for cluster members.
• Analyzing communication within clusters, considering simultaneous frequency band sharing.
• Evaluating performance metrics under co-channel interference from intra- and inter-cluster sources.

Main Results:

• Quantified coverage and area spectral efficiency for linear clustered UV sensor networks.
• Identified key factors influencing network performance in linear deployments.
• Provided insights into managing interference in swarm sensing applications.

Conclusions:

• The study provides a framework for designing and evaluating linear clustered UV sensor networks.
• Findings are applicable to various linear infrastructure monitoring and sensing tasks.
• Optimizing communication protocols is essential for maximizing spectral efficiency in dense UV networks.