  IIoT services focused on industry-oriented services often require objects run
more than one task. IIoT objects poses the challenge of distributing and
managing task allocation among them. The fairness of task allocation brings
flexible network reconfiguration and maximizes the tasks to be performed.
Although existing approaches optimize and manage the dynamics of objects, not
all them consider both co-relationship between tasks and object capabilities
and the distributed allocation over the cluster service. This paper introduces
the ACADIA mechanism for task allocation in IIoT networks in order to
distribute task among objects. It relies on relational consensus strategies to
allocate tasks and similarity capabilities to determine which objects can play
in accomplishing those tasks. Evaluation on NS-3 showed that ACADIA achieved
98% of allocated tasks in an IIoT-Health considering all scenarios, average
more than 95% of clusters apt to performed tasks in a low response time, and
achieved 50% more effectiveness in task allocation compared to the literature
solution CONTASKI.
