Large pervasive systems, deployed in dynamic environments, require flexible control mechanisms to meet the demands of chaotic state changes while accomplishing system goals. As centralized control approaches may falter in environments where centralized communication and knowledge may be impossible to implement, researchers have proposed decentralized control methods that leverage agent-driven, self-organizing behaviors, to achieve reliable, flexible systems. This article presents and compares the performance of three decentralized control approaches in the online multi-object k-assignment problem. In this domain, a set of sensors is tasked to detect and track an unknown and changing set of targets. Results show that a proposed hybrid approach that incorporates supervisory devices within the population while allowing semi-autonomous operations in non-supervisory devices produces a flexible and reliable system capable of both high detection and coverage rates.

部署在动态环境中的大型普及系统需要灵活的控制机制来满足混乱状态变化的需求，同时实现系统目标。由于集中控制方法可能在无法实施集中通信和知识的环境中动摇，研究人员提出了分散控制方法，利用代理驱动的自组织行为来实现可靠、灵活的系统。本文介绍并比较了三种分散控制方法在在线多对象k中的性能-分配问题。在该领域中，一组传感器的任务是检测和跟踪一组未知且不断变化的目标。结果表明，提出的混合方法在人群中结合了监督设备，同时允许在非监督设备中进行半自主操作，从而产生了一个灵活可靠的系统，能够同时具有高检测率和覆盖率。