Towards adaptive actors for scalable iot applications at the edge

Traditional device-cloud architectures are not scalable to the size of future IoT deployments. While edge and fog-computing principles seem like a tangible solution, they increase the programming effort of IoT systems, do not provide the same elasticity guarantees as the cloud and are of much greater hardware heterogeneity. Future IoT applications will be highly distributed and place their computational tasks on any combination of end-devices (sensor nodes, smartphones, drones), edge and cloud resources in order to achieve their application goals. These complex distributed systems require a programming model that allows developers to implement their applications in a simple way (i.e., focus on the application logic) and an execution framework that runs these applications resiliently with a high resource efficiency, while maximizing application utility. Towards such distributed execution runtime, we propose Nandu, an actor based system that adapts and migrates tasks dynamically using developer provided hints as seed information. Nandu allows developers to focus on sequential application logic and transforms their application into distributed, adaptive actors. The resulting actors support fine-grained entry points for the execution environment. These entry points allow local schedulers to adapt actors seamlessly to the current context, while optimizing the overall application utility according to developer provided requirements

Towards Knowledge Infusion for Robust and Transferable Machine Learning in IoT

Machine learning (ML) applications in Internet of Things (IoT) scenarios face the issue that supervision signals, such as labeled data, are scarce and expensive to obtain. For example, it often requires a human to manually label events in a data stream by observing the same events in the real world. In addition, the performance of trained models usually depends on a specific context: (1) location, (2) time and (3) data quality. This context is not static in reality, making it hard to achieve robust and transferable machine learning for IoT systems in practice. In this paper, we address these challenges with an envisioned method that we name Knowledge Infusion. First, we present two past case studies in which we combined external knowledge with traditional data-driven machine learning in IoT scenarios to ease the supervision effort: (1) a weak-supervision approach for the IoT domain to auto-generate labels based on external knowledge (e.g., domain knowledge) encoded in simple labeling functions. Our evaluation for transport mode classification achieves a micro-F1 score of 80.2%, with only seven labeling functions, on par with a fully supervised model that relies on hand-labeled data. (2) We introduce guiding functions to Reinforcement Learning (RL) to guide the agents' decisions and experience. In initial experiments, our guided reinforcement learning achieves more than three times higher reward in the beginning of its training than an agent with no external knowledge. We use the lessons learned from these experiences to develop our vision of knowledge infusion. In knowledge infusion, we aim to automate the inclusion of knowledge from existing knowledge bases and domain experts to combine it with traditional data-driven machine learning techniques during setup/training phase, but also during the execution phase

Embracing the future Internet of Things

All of the objects in the real world are envisioned to be connected and/or represented, through an infrastructure layer, in the virtual world of the Internet, becoming Things with status information. Services are then using the available data from this Internet-of-Things (IoT) for various social and economical benefits which explain its extreme broad usage in very heterogeneous fields. Domain administrations of diverse areas of application developed and deployed their own IoT systems and services following disparate standards and architecture approaches that created a fragmentation of things, infrastructures and services in vertical IoT silos. Coordination and cooperation among IoT systems are the keys to build “smarter” IoT services boosting the benefits magnitude. This article analyses the technical trends of the future IoT world based on the current limitations of the IoT systems and the capability requirements. We propose a hyper-connected IoT framework in which “things” are connected to multiple interdependent services and describe how this framework enables the development of future applications. Moreover, we discuss the major limitations in today’s IoT and highlight the required capabilities in the future. We illustrate this global vision with the help of two concrete instances of the hyper-connected IoT in smart cities and autonomous driving scenarios. Finally, we analyse the trends in the number of connected “things” and point out open issues and future challenges. The proposed hyper-connected IoT framework is meant to scale the benefits of IoT from local to global

Toward understanding crowd mobility in smart cities through the Internet of Things

Understanding crowd mobility behaviors would be a key enabler for crowd management in smart cities, benefiting various sectors such as public safety, tourism and transportation. This article discusses the existing challenges and the recent advances to overcome them and allow sharing information across stakeholders of crowd management through Internet of Things (IoT) technologies. The article proposes the usage of the new federated interoperable semantic IoT platform (FIESTA-IoT), which is considered as "a system of systems". The platform can support various IoT applications for crowd management in smart cities. In particular, the article discusses two integrated IoT systems for crowd mobility: 1) Crowd Mobility Analytics System, 2) Crowd Counting and Location System (from the SmartSantander testbed). Pilot studies are conducted in Gold Coast, Australia and Santander, Spain to fulfill various requirements such as providing online and offline crowd mobility analyses with various sensors in different regions. The analyses provided by these systems are shared across applications in order to provide insights and support crowd management in smart city environments.The pilot study in Gold Coast is conducted in collaboration with NEC Australia. This work has been partially funded by the Spanish Government (MINECO) under Grant Agreement No. TEC2015-71329-C2-1-R ADVICE (Dynamic Provisioning of Connectivity in High Density 5G Wireless Scenarios) project and by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT (Automated Driving Progressed by Internet Of Things), 643943 FIESTAIoT (Federated Interoperable Semantic IoT Testbeds and Applications), and 643275 FESTIVAL (Federated Interoperable Smart ICT Services Development and Testing Platforms) projects and the joint project by NEC Laboratories Europe and Technische Universität Dortmund. The content of this paper does not reflect the official opinion of the Spanish Government or European Union. Responsibility for the information and views expressed therein lies entirely with the authors

Enabling data spaces : existing developments and challenges

This paper focuses on the concept of data spaces, which can serve as a basis for the future data economy. In data spaces, applicable to various business domains, stakeholders will be able to share data with each other in a controlled way. First, the paper describes the real motivations and needs for enabling data spaces. Second, it highlights the major technical developments in the area of data spaces in the light of open ecosystems and standards. Lastly, it focuses on two key challenges for enabling data spaces: 1) Data interoperability, 2) Data value generation. As a concrete data spaces solution example, this paper proposes the "Green Twin" use case that can be developed as a carbon neutrality solution in the domains of mobility and smart cities

MELODY - management environment for large open distributed systems : Projektbeschreibung

95 pagesMit der zunehmenden Größe und Komplexität offener verteilter Systeme wächst der Bedarf an einer geeigneten systemseitigen Unterstützung bei der Nutzung von Diensten. Eine solche Unterstützung muß sowohl Komponenten zur Vermittlung als auch zur Verwaltung von Diensten enthalten. Da davon auszugehen ist, daß sich verteilte Systeme über verschiedene, administrativ getrennte Domänen erstrecken, die jeweils eigene Strategien verfolgen, müssen die einzelnen Komponenten in Abhängigkeit von diesen Strategien über Domänengrenzen hinweg zusammenarbeiten können. Zur Lösung dieses Problems kann auf den bereits existierenden Ansätzen für Trading- und Managementdienste aufgesetzt werden. Der vorliegende Bericht stellt ein neues Modell für die Dienstnutzung vor und liefert darauf aufbauend eine ausführliche Klassifikation von Varianten eines Trading-Dienstes. Außerdem werden die Grundprinzipien des kooperativen Tradings eingeführt und die Bandbreite der möglichen Lösungen aufgezeigt. Abschließend wird das bisher im MELODY-Projekt (Management Environment for Large Open Distributed sYstems) verwirklichte System präsentiert, dessen Schwerpunkte auf der Zusammenarbeit zwischen Trading und Management zur Behandlung dynamischer Attribute und auf der Reservierungvon Serverressourcen liegen. Der Managementdienst wird nur soweit erläutert, wie er für das Gesamtverständnis notwendig ist. As distributed systems are getting larger and more complex, the importance of supporting service users and providers is increasing enormously. Such support has to contain mediating and managing components. Because distributed systems in general are composed of different administrative domains with specific policies, supporting components have to cooperate across domain boundaries by taking into account these policies. Suited tools are trading and management mechanisms. Up to now, these approaches have been treated more or less isolated. In the following we introduce a comprehensive approach, based on a general model for service usage and its support. This model is used to classify existing trading approaches and derive concepts for the interworking of all supporting components, especially for the cooperation of traders of different domains. These ideas have been partially implemented in the so-called MELODY system (Management Environment for Large Open Distributed sYstems). Special features of this system concern the interworking of trading and management to handle dynamic properties and the reservation of resources from servers