AdaptNet: Rethinking Sensing and Communication for a Seamless Internet of Drones Experience

TL;DR

This paper introduces a novel multi-UAV framework that combines spatial-temporal clustering, ISAC, and MARL to improve safety, reliability, and efficiency in the Internet of Drones by optimizing sensing, communication, and data relevance.

Contribution

It presents an integrated approach that enhances UAV network reliability and safety through clustering, ISAC, and reinforcement learning, setting new standards for adaptive aerial systems.

Findings

Improved UAV safety and reliability in dynamic environments.

Enhanced data relevance filtering reduces bandwidth usage.

Demonstrated effectiveness of MARL in adaptive UAV strategies.

Abstract

In the evolving era of Unmanned Aerial Vehicles (UAVs), the emphasis has moved from mere data collection to strategically obtaining timely and relevant data within the Internet of Drones (IoDs) ecosystem. However, the unpredictable conditions in dynamic IoDs pose safety challenges for drones. Addressing this, our approach introduces a multi-UAV framework using spatial-temporal clustering and the Frechet distance for enhancing reliability. Seamlessly coupled with Integrated Sensing and Communication (ISAC), it enhances the precision and agility of UAV networks. Our Multi-Agent Reinforcement Learning (MARL) mechanism ensures UAVs adapt strategies through ongoing environmental interactions and enhancing intelligent sensing. This focus ensures operational safety and efficiency, considering data capture and transmission viability. By evaluating the relevance of the sensed information, we can communicate only the most crucial data variations beyond a set threshold and optimize bandwidth usage. Our methodology transforms the UAV domain, transitioning drones from data gatherers to adept information orchestrators, establishing a benchmark for efficiency and adaptability in modern aerial systems.