Multi-UAV path planning utilizing the PGA algorithm for terrestrial IoT sensor network under ISAC framework

The integration of internet of things (IoT) technologies with unmanned aerial vehicles (UAVs) has initiated a groundbreaking revolution in data acquisition and communication systems across diverse domains. This document introduces an innovative endeavor, the integration of multi-UAV path planning for integrated sensing and communication (ISAC) within ground-based CAT-M1 IoT sensor networks, accomplished through the application of the Pareto-based genetic ant colony optimization (PGA) algorithm. The PGA algorithm is highly capable of UAV path planning due to its efficiency, adaptability, and seamless integration of domain expertise. By employing the PGA algorithm, we simultaneously minimize UAV travel distance while optimizing energy consumption, resulting in multi-objective optimization. The synergy of ground-based IoT sensors and seamless UAV communication, coupled with a convex optimization resource allocation algorithm, empowers real-time data acquisition and heightens situational awareness. Our proposed UAV path planning PGA algorithm with resource allocation is crafted to maximize the efficiency of ground-based sensor data acquisition. We stand at the forefront of advancing multi-UAV data collection systems with this pioneering approach, promoting increased efficiency, robustness, and transformative solutions across diverse domains. The proposed system for ISAC achieves an impressive throughput of up to 95% of the system's capacity.