Intelligent adaptive gossip-based broadcast protocol for UAV-MEC using multi-agent deep reinforcement learning

• Main Authors: REN, Zen, LI, Xinghua, MIAO, Yinbin, LI, Zhuowen, WANG, Zihao, ZHU, Mengyao, LIU, Ximeng, DENG, Robert H.
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2023
• 主題: Autonomous aerial vehicles; Floods; Gossip protocol; partially observable Markov decision process; Propagation delay; Protocols; Reinforcement learning; reinforcement learning; Semantics; sparse rewards; Topology; UAVs; Information Security; Theory and Algorithms
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/8275; https://ink.library.smu.edu.sg/context/sis_research/article/9278/viewcontent/Intelligent_Adaptive_Gossip_av.pdf
• 機構: Singapore Management University

UAV-assisted mobile edge computing (UAV-MEC) has been proposed to offer computing resources for smart devices and user equipment. UAV cluster aided MEC rather than one UAV-aided MEC as edge pool is the newest edge computing architecture. Unfortunately, the data packet exchange during edge computing within the UAV cluster hasn't received enough attention. UAVs need to collaborate for the wide implementation of MEC, relying on the gossip-based broadcast protocol. However, gossip has the problem of long propagation delay, where the forwarding probability and neighbors are two factors that are difficult to balance. The existing works improve gossip from only one factor, which cannot select suitable forwarding probability and avoid redundant messages. Besides, these schemes do not consider the historical packet reception of new neighbors when UAVs fly around, which decreases forwarding efficiency. To solve these problems, we first propose a data structure called Bitgraph that can record the historical packet reception of UAVs. Then, we formulate gossip broadcasting as a partially observable Markov decision process. Based on Bitgraph, we design the reward function. Finally, we design a multi-agent reinforcement learning algorithm, Branching Deep Graph Network (BDGN), which simultaneously makes decisions on forwarding probability and neighbors. Extensive experiments illustrate that our proposal gets more than 29% advantage in terms of the propagation delay and 20% advantage in terms of the redundant messages compared to the existing works.