The system uses AI algorithms to dynamically adjust the compression of surveillance data depending on the current bandwidth availability.

It implements edge computing at remote sites to preprocess and compress the data locally, thereby reducing the amount transmitted back to command centers.

This local pre-processing involves using machine learning models trained to prioritize critical segments of video and sensor data, ensuring that essential data is sent with higher fidelity.

Additionally, a hybrid network technology composed of low-bandwidth satellite links and mesh networking optimizes and diversifies transmission paths to ensure reliability and speed.

The ADCTS enhances real-time surveillance capabilities in bandwidth-limited remote areas by significantly reducing data volume without loss of critical information, ensuring that security personnel receive timely, actionable insights.

This solution bypasses the need for expensive infrastructure overhauls, offering a cost-effective and rapid deployment alternative.

Using established technologies like AI, edge computing, and adaptive compression, ADCTS is technically feasible and can be integrated with current surveillance systems.

Initial costs are moderate, focused on software development rather than infrastructure, and regulatory compliance for military communications is a pre-existing pathway.