Towards Secure Embodied Communication Management in AI Era: Reputation-Guided Agent Message Exchange

For large-scale embedded sensor-actuator networks, such as robotic swarms deployed over vast areas and other embedded intelligent devices, end-to-end message exchange is often impossible due to their limited communication range, power constraints, and device mobility. Devices, thus, rely on multi-hop relaying, exposing them to Man-in-the-Middle (MitM) attacks where compromised relays tamper with, forge, or inject false messages. The existing countermeasures, including end-to-end encryption or Byzantine consensus, involve high overhead while requiring global coordination and, thus, renders them impractical for time-sensitive message exchange in embedded intelligence. Security management on communication among embodied devices is highly desired. To address this challenge, we propose Reputation-Guided Dynamic Relay Selection (RDRS), a lightweight, distributed countermeasure against MitM attacks that leverages interactive feedback to evaluate reputation of embedded devices. Specifically, each device maintains reputation scores updated via recent interaction success rates with decay factors to counter dynamic adversaries. During exchanging messages, embedded devices select next-hop neighbors weighted by reputation scores, effectively bypassing malicious devices without explicit detection or in-path verification. Comprehensive simulations in embedded sensor-actuator networks demonstrate that RDRS reduces tampering success rate (TSR) by 80-95% compared to the baselines, martians request satisfaction rate (RSR) above 79% even at 40% malicious nodes, and achieves lower delay 64% with comparable overhead.