A Scalable Framework for Provisioning Large-Scale IoT Deployments

Vögler, Michael; Schleicher, Johannes M.; Inzinger, Christian; Dustdar, Schahram

doi:10.1145/2850416

Cited by 53 publications

(16 citation statements)

References 19 publications

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“…Other tailored solutions, e.g. [18,58,69], rely on centralized control to distribute application execution to the edge devices and resource-rich end devices. In [18], interactions are based on Web technologies.…”

Section: Device Layer Distributed Computingmentioning

confidence: 99%

Energy efficient opportunistic edge computing for the Internet of Things

Leppänen

Riekki

2019

WEB

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Edge computing in Internet of Things enhances application execution by retrieving cloud resources to the close proximity of resource-constrained end devices at the edge and by enabling task offloading from end devices to the edge. In this paper, edge computing platforms are extended into the data producing end devices, including wireless sensor network nodes and smartphones, with mobile agents. Mobile agents operate as a multi-agent system on the opportunistic network of heterogeneous end devices, where the benefits include autonomous, asynchronous and the adaptive execution and relocation of application-specific tasks, while taking into account local resource availability. In addition to the vertical edge connectivity, mobile agents enable horizontal sharing of the information between end devices. Use cases are presented, where mobile agents address challenges in current edge computing platforms. An edge application is evaluated, where mobile agents as a multi-agent system process sensor data in a heterogeneous set of end devices, control the operation of the devices and share their results with system components. Mobile agents operate atop a REST-based mobile agent software framework that relies on embedded Web services for interoperability. A real-world evaluation and large-scale simulations show that energy consumption is reduced significantly in the edge application execution.

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Section: Device Layer Distributed Computingmentioning

confidence: 99%

Energy efficient opportunistic edge computing for the Internet of Things

Leppänen

Riekki

2019

WEB

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“…Voegler et al [144] propose a novel infrastructure to provide application packages on resource-constrained heterogeneous edge devices elastically in large-scale IoT deployments. It enables push-based (commands down to the tactical units) as well as pull-based (from ground to decision-making) deployments supporting different topologies and infrastructure requirements.…”

Section: Building Iot For Tactical and Emergency Environmentsmentioning

confidence: 99%

A Review on Internet of Things for Defense and Public Safety

Fraga‐Lamas

Fernández‐Caramés

Suárez-Albela

et al. 2016

Sensors

195

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The Internet of Things (IoT) is undeniably transforming the way that organizations communicate and organize everyday businesses and industrial procedures. Its adoption has proven well suited for sectors that manage a large number of assets and coordinate complex and distributed processes. This survey analyzes the great potential for applying IoT technologies (i.e., data-driven applications or embedded automation and intelligent adaptive systems) to revolutionize modern warfare and provide benefits similar to those in industry. It identifies scenarios where Defense and Public Safety (PS) could leverage better commercial IoT capabilities to deliver greater survivability to the warfighter or first responders, while reducing costs and increasing operation efficiency and effectiveness. This article reviews the main tactical requirements and the architecture, examining gaps and shortcomings in existing IoT systems across the military field and mission-critical scenarios. The review characterizes the open challenges for a broad deployment and presents a research roadmap for enabling an affordable IoT for defense and PS.

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“…However, modeling the components of IoT applications directly in TOSCA still requires some efforts, since the available TOSCA tools are not yet completely integrated with IoT concepts. This work was extended by Vögler et al [32], which present LEONORE, a scalable deployment framework to deploy and execute custom application logic directly on IoT gateways. However, in order to know which IoT gateways are available for the provisioning, the IoT gateways must have a pre-installed local provisioning agent.…”

Section: Related Workmentioning

confidence: 99%

Automating the Provisioning and Configuration of Devices in the Internet of Things

Hirmer

Breitenbücher

Silva

et al. 2016

CSIMQ

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Abstract. The Internet of Things benefits from an increasing number of interconnected technical devices. This has led to the existence of so-called smart environments, which encompass one or more devices sensing, acting, and automatically performing different tasks to enable their self-organization. Smart environments are divided into two parts: the physical environment and its digital representation, oftentimes referred to as digital twin. However, the automated binding and monitoring of devices of smart environments are still major issues. In this article we present a method and system architecture to cope with these challenges by enabling (i) easy modeling of sensors, actuators, devices, and their attributes, (ii) dynamic device binding based on their type, (iii) the access to devices using different paradigms, and (iv) the monitoring of smart environments in regard to failures or changes. We furthermore provide a prototypical implementation of the introduced approach.

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A Scalable Framework for Provisioning Large-Scale IoT Deployments

Cited by 53 publications

References 19 publications

Energy efficient opportunistic edge computing for the Internet of Things

Energy efficient opportunistic edge computing for the Internet of Things

A Review on Internet of Things for Defense and Public Safety

Automating the Provisioning and Configuration of Devices in the Internet of Things

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