Enabling Smart Cloud Services Through Remote Sensing: An Internet of Everything Enabler

Abdelwahab, Sherif; Hamdaoui, Bechir; Guizani, Mohsen; Rayes, Ammar

doi:10.1109/jiot.2014.2325071

Cited by 133 publications

(54 citation statements)

References 54 publications

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“…Cloud computing has been historically used as enable for a wide number of applications. It can naturally offer distributed sensory data collection, global resource and data sharing, remote and real-time data access, elastic resource provisioning and scaling, and pay-as-you-go pricing models [3].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

Orgerie

Rodero

et al. 2017

2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

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Abstract-The emergence of Internet of Things (IoT) is participating to the increase of data-and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We propose an analytic model to decide whether to offload computation from the objects to the edge or to the core Cloud, depending on the renewable energy availability and the desired application QoS. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras.

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Section: Introductionmentioning

confidence: 99%

Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

Orgerie

Rodero

et al. 2017

2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

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“…Cloud computing paradigm has empowered RS data processing and makes it possible than ever ( [50]). Unlike conventional ways of processing that done by standalone server or software, the cloud-based RS data processing is enabled with revolutionary promise of unlimited computing resources.…”

Section: Requirements and Challenges: Rscloud For Rs Big Datamentioning

confidence: 99%

pipsCloud: High performance cloud computing for remote sensing big data management and processing

Wang

Yan

et al. 2018

Future Generation Computer Systems

169

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With the increasing requirement of accurate and up-to-date resource & environmental information for regional and global monitoring, large-region covered multi-temporal, multi-spectral massive remote sensing (RS) datasets are exploited for processing. The remote sensing data processing generally follows a complex multi-stage processing chain, which consists of several independent processing steps subject to types of RS applications. In general the RS data processing for regional environmental and disaster monitoring are recognized as typical both compute-intensive and data-intensive applications.To solve the aforementioned issues efficiently, we propose pipsCloud which combine recent Cloud computing and HPC techniques to enable large-scale RS data processing system as on-demand real-time services. Benefiting from the ubiquity, elasticity and high-level of transparency of Cloud computing model, the massive RS data managing and data processing for dynamic environmental monitoring are all encapsulate as Cloud with Web interfaces. Where, a Hilbert-R + based data indexing mechanism is employed for optimal query and access of RS imageries, RS data products as well as interim data. In the core platform beneath the Cloud services, we provide a parallel file system for massive high-dimensional RS data and offers interfaces for intensive irregular RS data accessing so as to provide improved data locality and optimized I/O performance. Moreover, we adopt an adaptive RS data analysis workflow manage system for on-demand workflow construction and collaborative execution of distributed complex chain of RS data processing, such as forest fire detection, mineral resources and coastline monitoring. Through the experimental analysis we have show the efficiency of the pipsCloud platform.

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“…This novel architecture referred as Fog Computing [6], provides the computing, storage, and network services between end devices and centralized cloud computing data centers, typically, but not exclusively at the edge of the network in order to efficiently support M2M § http://openiot.eu ** http://compose-project.eu † † http://xively.com ‡ ‡ http://www.libelium.com § § http://thingworx.com applications. Although decentralized approach is acknowledged as the long-term evolution to support M2M applications [7], only a few solutions have been proposed in literature. However, they are usually bounded on a specific technology, e.g.…”

Section: Current Approachesmentioning

confidence: 99%

BETaaS platform – a Things as a Service Environment for future M2M marketplaces

Anggorojati¹,

Kyriazakos²,

Prasad³

et al. 2015

EAI Endorsed Transactions on Cloud Systems

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Building the Environment for Things as a Service (BETaaS) is a novel platform for the deployment and execution of contentcentric Machine-to-Machine (M2M) applications, which relies on a local cloud of gateways. BETaaS platform provides a uniform interface and services to map content with things in a context-aware manner. Deployment of services for the execution of applications is dynamic and takes into account the computational resources of the low-end physical devices used. To this aim, BETaaS platform is based on a suitable defined Internet of Things (IoT) model, allowing the integration of the BETaaS components within the future Internet environment. In this paper we present the BETaaS concept, the high level platform architecture and application scenarios that extend the state-of-the-art in M2M communications and open the horizon for future M2M marketplaces.

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Enabling Smart Cloud Services Through Remote Sensing: An Internet of Everything Enabler

Cited by 133 publications

References 54 publications

Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

pipsCloud: High performance cloud computing for remote sensing big data management and processing

BETaaS platform – a Things as a Service Environment for future M2M marketplaces

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