Distributed Sequential Detection for Cooperative Spectrum Sensing in Cognitive Internet of Things

Area of Science:

• Wireless Communication
• Internet of Things (IoT)
• Spectrum Management

Background:

• Increasing demand for wireless spectrum due to proliferation of IoT devices.
• Spectrum scarcity poses a significant challenge for IoT device connectivity and performance.
• Existing methods lack efficient spectrum utilization strategies for IoT networks.

Purpose of the Study:

• To introduce a collaborative spectrum sensing (CSS) framework for IoT devices.
• To address the challenges of sensing time and decision costs in spectrum sensing.
• To optimize spectrum access for IoT devices while avoiding interference with primary users (PUs).

Main Methods:

• Development of a distributed cognitive IoT model with sequential decision rules.
• Definition of sensing time and cost functions for IoT devices.
• Formulation of an average cost optimization problem in CSS.
• Application of person-by-person optimization (PBPO) and dynamic programming to solve the optimal sensing time problem.

Main Results:

• Numerical simulations validate the proposed framework's effectiveness.
• The framework successfully minimizes global false alarm and miss detection probabilities.
• Achieved minimal average cost across various observation costs and thresholds.

Conclusions:

• The proposed CSS framework provides an efficient solution for spectrum scarcity in IoT environments.
• Optimized sensing strategies reduce operational costs for IoT devices.
• The model ensures reliable spectrum access for IoT devices without compromising primary user communications.