ProjectCooperation: The Key to Unlock the True Potential of Edge Computing

Basic data

Cooperation: The Key to Unlock the True Potential of Edge Computing
01/03/2023 to 28/02/2026
Abstract / short description:
The explosive demand for computationally involved applications such as online gaming renders edge/fog computing an essential block of future networks. By bringing the computation close to the network, edge computing reduces delay, improves resource efficiency, mitigates backhaul traffic, and decreases infrastructure cost. Swift advances in artificial intelligence and wireless device-to-device communications enable the edge/fog architecture as a dense wireless network consisting of intelligent entities that can learn, make decisions, and communicate. It is well-known that cooperation enables cognitive entities to subtly divide the costs, share the risks, and distribute the utility. As such, it also unlocks the true potential of edge computing in terms of resource efficiency (self-optimization), stable distributed control (self-organization), and sustainability (self-diagnosis and self-healing). Despite great potential, the implementation of cooperation in wireless networks associates with several significant hurdles. These include information shortage, heterogeneity of edge/fog nodes, communication constraints, and randomness in crucial optimization parameters. In this project, we confine our attention to three main challenges in the autonomous edge/fog computing paradigm: distributed task management, efficient resource pooling, and strategic function placement. The objective is to address these problems in a real-world system despite the abovementioned constraints by developing cooperation methods. In a nutshell, the project bridges the theory of cooperation in multi-agent systems and the practical aspects of wireless communications to address some main challenges of the edge computing paradigm. The outcomes are computationally-efficient decision-making methods that enhance the efficiency and productivity of edge computing technology concerning crucial performance metrics such as energy efficiency and service delay.

Involved staff


Wilhelm Schickard Institute of Computer Science (WSI)
Department of Informatics, Faculty of Science

Local organizational units

Department of Informatics
Faculty of Science
University of Tübingen


Bonn, Nordrhein-Westfalen, Germany

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