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A Smart City Lighting Case Study on an OpenStack-Powered Infrastructure.

Merlino G, Bruneo D, Distefano S, Longo F, Puliafito A, Al-Anbuky A - Sensors (Basel) (2015)

Bottom Line: Existing solutions mainly focus on core mechanisms and do not allow one to scale by leveraging infrastructure or adapt to a variety of scenarios, especially if actuators are involved in the loop.In this sense, a widely-used and competitive framework for infrastructure as a service, such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks to fit the bill, replacing current application-specific approaches with an innovative application-agnostic one.This work thus describes the rationale, efforts and results so far achieved for an integration of IoT paradigms and resource ecosystems with such a kind of cloud-oriented device-centric environment, by focusing on a smart city scenario, namely a park smart lighting example, and featuring data collection, data visualization, event detection and coordinated reaction, as example use cases of such integration.

View Article: PubMed Central - PubMed

Affiliation: Mobile and Distributed Systems Lab, Dipartimento di Ingegneria, Università di Messina, Contrada di Dio, 98166 Messina, Italy. gmerlino@unime.it.

ABSTRACT
The adoption of embedded systems, mobile devices and other smart devices keeps rising globally, and the scope of their involvement broadens, for instance, in smart city-like scenarios. In light of this, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad hoc solutions, while at the same time taking into account valid options with regard to infrastructure management and other more advanced functionalities. Existing solutions mainly focus on core mechanisms and do not allow one to scale by leveraging infrastructure or adapt to a variety of scenarios, especially if actuators are involved in the loop. A new, more flexible, cloud-based approach, able to provide device-focused workflows, is required. In this sense, a widely-used and competitive framework for infrastructure as a service, such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks to fit the bill, replacing current application-specific approaches with an innovative application-agnostic one. This work thus describes the rationale, efforts and results so far achieved for an integration of IoT paradigms and resource ecosystems with such a kind of cloud-oriented device-centric environment, by focusing on a smart city scenario, namely a park smart lighting example, and featuring data collection, data visualization, event detection and coordinated reaction, as example use cases of such integration.

No MeSH data available.


Data collection and inference/reaction subsystem: architecture.
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f4-sensors-15-16314: Data collection and inference/reaction subsystem: architecture.

Mentions: As depicted in Figure 4, the whole Stack4Things data collection architecture comprises both additional modules for Ceilometer and external components needed for higher-level functions, e.g., event processing. Framing the discussion in terms of the devices involved and of compute-based IaaS, the board may be considered an instance of a machine, e.g., a VM or even just a cloud-provisioned physical machine. Along the same lines, a Compute node is just any machine, which, on the one hand, may host standard computing VMs and, on the other, may be IoT-enabled in order to supervise and track the lifecycle of one or more boards. The Controller, in turn, is expected to host a Collector (for the Ceilometer), as a centralized component for data collection, storage or further processing, if needed.


A Smart City Lighting Case Study on an OpenStack-Powered Infrastructure.

Merlino G, Bruneo D, Distefano S, Longo F, Puliafito A, Al-Anbuky A - Sensors (Basel) (2015)

Data collection and inference/reaction subsystem: architecture.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541880&req=5

f4-sensors-15-16314: Data collection and inference/reaction subsystem: architecture.
Mentions: As depicted in Figure 4, the whole Stack4Things data collection architecture comprises both additional modules for Ceilometer and external components needed for higher-level functions, e.g., event processing. Framing the discussion in terms of the devices involved and of compute-based IaaS, the board may be considered an instance of a machine, e.g., a VM or even just a cloud-provisioned physical machine. Along the same lines, a Compute node is just any machine, which, on the one hand, may host standard computing VMs and, on the other, may be IoT-enabled in order to supervise and track the lifecycle of one or more boards. The Controller, in turn, is expected to host a Collector (for the Ceilometer), as a centralized component for data collection, storage or further processing, if needed.

Bottom Line: Existing solutions mainly focus on core mechanisms and do not allow one to scale by leveraging infrastructure or adapt to a variety of scenarios, especially if actuators are involved in the loop.In this sense, a widely-used and competitive framework for infrastructure as a service, such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks to fit the bill, replacing current application-specific approaches with an innovative application-agnostic one.This work thus describes the rationale, efforts and results so far achieved for an integration of IoT paradigms and resource ecosystems with such a kind of cloud-oriented device-centric environment, by focusing on a smart city scenario, namely a park smart lighting example, and featuring data collection, data visualization, event detection and coordinated reaction, as example use cases of such integration.

View Article: PubMed Central - PubMed

Affiliation: Mobile and Distributed Systems Lab, Dipartimento di Ingegneria, Università di Messina, Contrada di Dio, 98166 Messina, Italy. gmerlino@unime.it.

ABSTRACT
The adoption of embedded systems, mobile devices and other smart devices keeps rising globally, and the scope of their involvement broadens, for instance, in smart city-like scenarios. In light of this, a pressing need emerges to tame such complexity and reuse as much tooling as possible without resorting to vertical ad hoc solutions, while at the same time taking into account valid options with regard to infrastructure management and other more advanced functionalities. Existing solutions mainly focus on core mechanisms and do not allow one to scale by leveraging infrastructure or adapt to a variety of scenarios, especially if actuators are involved in the loop. A new, more flexible, cloud-based approach, able to provide device-focused workflows, is required. In this sense, a widely-used and competitive framework for infrastructure as a service, such as OpenStack, with its breadth in terms of feature coverage and expanded scope, looks to fit the bill, replacing current application-specific approaches with an innovative application-agnostic one. This work thus describes the rationale, efforts and results so far achieved for an integration of IoT paradigms and resource ecosystems with such a kind of cloud-oriented device-centric environment, by focusing on a smart city scenario, namely a park smart lighting example, and featuring data collection, data visualization, event detection and coordinated reaction, as example use cases of such integration.

No MeSH data available.