Collaborative Deep Neural Networks(DNNs)inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things(IoT)devices in maritime edge intelligence netw...Collaborative Deep Neural Networks(DNNs)inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things(IoT)devices in maritime edge intelligence networks.However,the resource constraints of devices,the delay-sensitive nature of tasks,and the dynamic environmental conditions present significant challenges.While Multi-Armed Bandit(MAB)algorithms have been explored for task offloading,their performance is often constrained in highly dynamic scenarios with complex,nonlinear utility dependencies.To address these challenges,we propose a Group Neural MAB(GN-MAB)approach that jointly optimizes idle device selection(i.e.,arm groups)and DNN partitioning decisions(i.e.,arms)for efficient collaborative inference.Building upon the neural upper confidence bound algorithm,GN-MAB dynamically balances the exploration and exploitation,enabling continuous adaptation of offloading strategies across sequential inference tasks.Extensive experimental results show that GN-MAB outperforms baseline approaches,achieving superior inference performance while exhibiting robust adaptability to the fluctuating conditions of maritime environments.展开更多
基金supported by the Fundamental Research Funds for the Central Universities,South-Central MinZu University(No.CZQ25005)the Young Innovative Talents Project of Department of Education of Guangdong Province(Nos.2025KQNCX261,2025KQNCX262)+1 种基金the National Natural Science Foundation of China(No.62201621)the Young Scientific and Technological Talents Training Program of Hubei Province(No.2025DJA072).
文摘Collaborative Deep Neural Networks(DNNs)inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things(IoT)devices in maritime edge intelligence networks.However,the resource constraints of devices,the delay-sensitive nature of tasks,and the dynamic environmental conditions present significant challenges.While Multi-Armed Bandit(MAB)algorithms have been explored for task offloading,their performance is often constrained in highly dynamic scenarios with complex,nonlinear utility dependencies.To address these challenges,we propose a Group Neural MAB(GN-MAB)approach that jointly optimizes idle device selection(i.e.,arm groups)and DNN partitioning decisions(i.e.,arms)for efficient collaborative inference.Building upon the neural upper confidence bound algorithm,GN-MAB dynamically balances the exploration and exploitation,enabling continuous adaptation of offloading strategies across sequential inference tasks.Extensive experimental results show that GN-MAB outperforms baseline approaches,achieving superior inference performance while exhibiting robust adaptability to the fluctuating conditions of maritime environments.
