Remote Management of a Large Set of Heterogeneous Devices Using Existing IoT Interoperability Platforms

Vandaele, Heleen; Nelis, Jelle; Verbelen, Tim; Develder, Chris

doi:10.1007/978-3-319-47075-7_49

Cited by 4 publications

(6 citation statements)

References 12 publications

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“…DYAMAND (DYnamic, Adaptive MAnagement of Networks and Devices) is a framework that offers a solution to overcome interoperability issues amongst the fragmented landscape of heterogeneous IoT protocols in a plug‐and‐play manner 35 . It provides the necessary abstraction for applications to remotely communicate with devices, regardless of their technology 36 . The framework allows interoperability amongst virtually all protocols,provided that their management API offers Discovery, Eventing and Control.…”

Section: Obelisk the Iot Integration Platformmentioning

confidence: 99%

Design and evaluation of a scalable Internet of Things backend for smart ports

et al. 2021

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Internet of Things (IoT) technologies, when adequately integrated, cater for logistics optimisation and operations' environmental impact monitoring, both key aspects for today's EU ports management. This article presents Obelisk, a scalable and multi‐tenant cloud‐based IoT integration platform used in the EU H2020 PortForward project. The landscape of IoT protocols being particularly fragmented, the first role of Obelisk is to provide uniform access to data originating from a myriad of devices and protocols. Interoperability is achieved through adapters that provide flexibility and evolvability in protocol and format mapping. Additionally, due to ports operating in a hub model with various interacting actors, a second role of Obelisk is to secure access to data. This is achieved through encryption and isolation for data transport and processing, respectively, while user access control is ensured through authentication and authorisation standards. Finally, as ports IoTisation will further evolve, a third need for Obelisk is to scale with the data volumes it must ingest and process. Platform scalability is achieved by means of a reactive micro‐services based design. Those three essential characteristics are detailed in this article with a specific focus on how to achieve IoT data platform scalability. By means of an air quality monitoring use‐case deployed in the city of Antwerp, the scalability of the platform is evaluated. The evaluation shows that the proposed reactive micro‐service based design allows for horizontal scaling of the platform as well as for logarithmic time complexity of its service time.

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Section: Obelisk the Iot Integration Platformmentioning

confidence: 99%

Design and evaluation of a scalable Internet of Things backend for smart ports

et al. 2021

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“…Further details, about these components, are provided in the subsequent section. Communication with the IoT devices of a smart environment is abstracted by an IoT middleware, namely, DYnamic, Adaptive MAnagement of Networks and Devices (DYAMAND) [41]. DYAMAND enables all heterogeneous IoT sensors to communicate using a standard format and IoT actuators can be commanded by sending JSON strings (via a central-server) over HTTP.…”

Section: Architecture Overviewmentioning

confidence: 99%

“…We developed a proof of concept in our HomeLab () (smart house) with a Pepper (humanoid) robot. The HomeLab is a two-story house (600 m 2 ) equipped with sensors and a home automation system, which communicates via DYAMAND [41]. The components onboard the robot were developed as nodes for the Robot Operating System (ROS) [44].…”

Section: Proof Of Concept In a Smart Homementioning

confidence: 99%

Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

Harman

Chintamani

Simoens

2019

Sensors

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By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot’s onboard planner increases the computational load, and thus can delay the execution of a task. Moreover, tasks may be frequently replanned due to the unanticipated actions of humans. Our framework aims to mitigate these inadequacies. In this paper, we propose a continual planning framework, which incorporates the sensing and actuation capabilities of IoT devices into a robot’s state estimation, task planing and task execution. The robot’s onboard task planner queries a cloud-based framework for actuators, capable of the actions the robot cannot execute. Once generated, the plan is sent to the cloud back-end, which will inform the robot if any IoT device reports a state change affecting its plan. Moreover, a Hierarchical Continual Planning in the Now approach was developed in which tasks are split-up into subtasks. To delay the planning of actions that will not be promptly executed, and thus to reduce the frequency of replanning, the first subtask is planned and executed before the subsequent subtask is. Only information relevant to the current (sub)task is provided to the task planner. We apply our framework to a smart home and office scenario in which the robot is tasked with carrying out a human’s requests. A prototype implementation in a smart home, and simulator-based evaluation results, are presented to demonstrate the effectiveness of our framework.

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“…Since in most cases hardware devices must be configured directly, such systems require prior knowledge to create automation rules that effectively exploit features of the environment. Especially for users without a deep technical understanding, manual configuration of such rules often is overly complex [17] and conflicts with daily activities, for example family life [1].…”

Section: Alternative Approachesmentioning

confidence: 99%

“…Figure 1), provide the user with a comprehensive interface that can configure and control the majority of devices on the market. At the same time, such interfaces are still complex to handle [17] and imply an effortful configuration of static rules by the user. Due to an increasing density of smart features, this complexity is also rising rapidly.…”

Section: Introductionmentioning

confidence: 99%

Competitive Agents for Intelligent Home Automation

Michalski

Pohling

Holthaus

2017

Proceedings of the 5th International Conference on Human Agent Interaction

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Technologies that aim to achieve intelligent automation in smart homes typically involve either trigger-action pairs or machine learning. These, however, are often complex to configure or hard to comprehend for the user. To maximize automation efficiency while keeping the configuration simple and the effects comprehensible, we thus explore an alternative agent-based approach. With the help of a survey, we put together a set of intelligent agents that act autonomously in the environment. Conflicts between behaviors, identified with a secondary study, are thereby resolved with a competitive combination of agents. We finally present the draft of a user interface that allows for individual configuration of all agents.

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Remote Management of a Large Set of Heterogeneous Devices Using Existing IoT Interoperability Platforms

Cited by 4 publications

References 12 publications

Design and evaluation of a scalable Internet of Things backend for smart ports

Design and evaluation of a scalable Internet of Things backend for smart ports

Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

Competitive Agents for Intelligent Home Automation

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