We propose a federated edge-computing architecture for management of data. Our vision is to enable a service provider model for "data-services", where a user can enter into economic agreements with an infrastructure maintainer to provide storage and communication of data, without necessarily trusting the infrastructure provider. Toward this vision, we present cryptographically hardened cohesive collections of data items called DataCapsules, and an overview of the underlying federated architecture, called Global Data Plane.

The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and reliability. In this paper, we present a `Fog Robotics' approach to deep robot learning that distributes compute, storage and networking resources between the Cloud and the Edge in a federated manner. Deep models are trained on non-private (public) synthetic images in the Cloud; the models are adapted to the private real images of the environment at the Edge within a trusted network and subsequently, deployed as a service for low-latency and secure inference/prediction for other robots in the network. We apply this approach to surface decluttering, where a mobile robot picks and sorts objects from a cluttered floor by learning a deep object recognition and a grasp planning model. Experiments suggest that Fog Robotics can improve performance by sim-to-real domain adaptation in comparison to exclusively using Cloud or Edge resources, while reducing the inference cycle time by 4× to successfully declutter 86% of objects over 213 attempts.

Localization services available on today's mobile devices are proprietary and leverage a limited set of sources of location information. Integration of new location estimation methods is therefore cumbersome, requiring adaptation to the specific interfaces of the proprietary location service. In addition, location-based applications are tightly interwoven with the location service that is typically provided by the operating system, hence these applications require significant restructuring to be able to run with another location service. To address these problems, we propose a modular localization service architecture that consists of location-based applications, an integrated location service enabling a fusion of so-called elementary location services, and resources for generating location information. A unified style of interaction among these components is enabled by a set of well-defined Application Programming Interfaces (APIs). The practicability and advantages of the proposed design is demonstrated by outlining how the APIs can be realized using modern types of component interactions.

Adaptive Resource-Centric Computing (ARCC) enables a simultaneous mix of high-throughput parallel, real-time, and interactive applications through automatic discovery of the correct mix of resource assignments necessary to achieve application requirements. This approach, embodied in the Tessellation manycore operating system, distributes resources to QoS domains called cells. Tessellation separates global decisions about the allocation of resources to cells from application-specific scheduling of resources within cells. We examine the implementation of ARCC in the Tessellation OS, highlight Tessellation's ability to provide predictable performance, and investigate the performance of Tessellation services within cells.