An Asynchronous Consensus Method With Low Communication Traffic and High Efficiency for Distributed Multi-Agent Scheduling

The Artificial Internet of Things (AIoT) is growing into a new frontier field with broad development prospects, which essence is the collaborative enhancement of networked heterogeneous agent swarms. The market-based approach is an effective way for the cooperative scheduling of agent swarm, where networked agents need to distributedly select and arrange tasks meeting the spatio-temporal constraints. This paper proposes a new asynchronous consensus method aimed at substantially mitigating the communication traffic and decreasing the message transmission requirements associated with the market-based approach, ultimately leading to a reduction in scheduling time. Firstly, the method innovatively introduces timestamps of agent information updates, which are more informative, thereby reducing inter-agent communication volume to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ n/m$</tex-math></inline-formula> of that in the original protocol (where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ n$</tex-math></inline-formula> represents the number of agents and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ m$</tex-math></inline-formula> denotes the number of tasks, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ m\gt n$</tex-math></inline-formula>). Secondly, agent-centric asynchronous consensus protocols are designed based on the new timestamps, which can resolve inter-agent task conflicts more rapidly and efficiently. Additionally, a mechanism for avoiding message flooding is proposed to prevent endless broadcasts caused by communication issues such as packet loss, link disruptions, and node withdrawals. Finally, through a self-developed ad-hoc network simulation system, the swarm scheduling under real networking conditions is simulated. The validation results demonstrate that the algorithm can significantly reduce communication traffic and scheduling time.