How can I optimize multi-agent data freshness for better perception?
Fresh2comm: Information Freshness Optimized Collaborative Perception
This paper addresses the challenge of inconsistent communication delays in collaborative perception for self-driving cars, which use multi-agent systems to share sensor data. It proposes Fresh2comm, a framework incorporating "Age of Information" (AoI) to optimize communication resource allocation and improve data freshness. A greedy algorithm minimizes the worst-case transmission delay, enhancing perception accuracy under real-world communication constraints.
Key points for LLM-based multi-agent systems: managing communication delays and data freshness are crucial for effective collaboration. AoI and resource allocation strategies are relevant for optimizing performance in real-world scenarios with inconsistent network conditions. The proposed greedy optimization offers a computationally efficient solution applicable to resource-constrained agents. The exploration of different delay types and their impact on perception accuracy emphasizes the importance of delay modeling for robust multi-agent collaboration.