A scalable, distributed algorithm for allocating workers in embedded systems

This paper presents a scalable threshold-based algorithm for allocating workers to a given task whose demand evolves dynamically over time. The algorithm is fully distributed and solely based on the local perceptions of the individuals. Each agent decides autonomously and deterministically to work only when it "feels" that some work needs to be done based on its sensory inputs. In this paper, we applied the worker allocation algorithm to a collective manipulation case study concerned with the gathering and clustering of initially scattered small objects. The aggregation experiment has been studied at three different experimental levels by using macroscopic and microscopic probabilistic models, and embodied simulations. Results show that teams using a number of active workers dynamically controlled by the allocation algorithm achieve similar or better performances in aggregation than those characterized by a constant team size, while using a considerably reduced number of agents over the whole aggregation process. Since this algorithm does not imply any form of explicit communication among agents, it represents a cost-effective solution for controlling the number of active workers in embedded systems consisting of a few to thousands of units.