Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing

Yannuzzi, M.; Milito, Rodolfo A.; Serral-Gracià, R.; Montero, Diego; Nemirovsky, Mario

doi:10.1109/camad.2014.7033259

Cited by 265 publications

(157 citation statements)

References 5 publications

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“…Raw data that is transformed into meaningful information at the edge reduces the cost of using cloud services, while heavier processing and decision making can be performed in the cloud to aggregate data and perform stronger analytics [16]. Gateway devices have the potential to enhance data processing on the edge but limited studies have been conduct on the specific trade-offs that can be made between performance and responsiveness.…”

Section: A Smart Cities Applications Using Video Processingmentioning

confidence: 99%

Performance of video processing at the edge for crowd-monitoring applications

Bailas

Marsden

Zhang

et al. 2018

2018 IEEE 4th World Forum on Internet of Things (WF-IoT)

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Abstract-Video analytics has a key role to play in smart cities and connected community applications such as crowd counting, activity detection, event classification, traffic counting etc. Using a cloud-centric approach where data is funnelled to a central processor presents a number of key problems such as available bandwidth, real-time responsiveness and personal data privacy issues. With the development of edge computing, a new paradigm for smart data management is emerging. Raw video feeds can be pre-processed at the point of capture while integration and deeper analytics is performed in the cloud. In this paper we explore the capacity of video processing at the edge and shown that basic image processing can be achieved in near real-time on lowpowered gateway devices. We have also investigated deep learning model capabilities for crowd counting in this context showing that its performance is highly dependent on the input size and rescaling video frames can optimise processing and performance. Increased edge processing resolves a number of issues in video analytics for crowd monitoring applications.

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Section: A Smart Cities Applications Using Video Processingmentioning

confidence: 99%

Performance of video processing at the edge for crowd-monitoring applications

Bailas

Marsden

Zhang

et al. 2018

2018 IEEE 4th World Forum on Internet of Things (WF-IoT)

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“…The hop count matrix H between comp. nodes and end devices is a random number between [2,3] if the node n and the end device ed are on the same location l or between [4,8] if the node n and the end device ed are on different locations, l 1 and l 2 , respectively. Different sets of model configurations for each scenario have been evaluated.…”

Section: Evaluation Setupmentioning

confidence: 99%

“…Therefore, it is necessary to adapt existing cloud and network architectures to future needs and design and develop new management functionalities to help meet the strict requirements of future Smart City IoT applications. Fog Computing extends the Cloud Computing paradigm by bringing cloud services closer to the end devices, thus reducing the communication latency [4], [5]. However, there is still a large number of research challenges associated with this approach since Fog Computing is in its early stages and needs more time to evolve.…”

Section: Introductionmentioning

confidence: 99%

Resource provisioning for IoT application services in smart cities

Santos¹,

Wauters²,

Volckaert³

et al. 2017

2017 13th International Conference on Network and Service Management (CNSM)

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Abstract-In the last years, traffic over wireless networks has been increasing exponentially, due to the impact of Internet of Things (IoT) and Smart Cities. Current networks must adapt to and cope with the specific requirements of IoT applications since resources can be requested on-demand simultaneously by multiple devices on different locations. One of these requirements is low latency, since even a small delay for an IoT application such as health monitoring or emergency service can drastically impact their performance. To deal with this limitation, the Fog computing paradigm has been introduced, placing cloud resources on the edges of the network to decrease the latency. However, deciding which edge cloud location and which physical hardware will be used to allocate a specific resource related to an IoT application is not an easy task. Therefore, in this paper, an Integer Linear Programming (ILP) formulation for the IoT application service placement problem is proposed, which considers multiple optimization objectives such as low latency and energy efficiency. Solutions for the resource provisioning of IoT applications within the scope of Antwerp's City of Things testbed have been obtained. The result of this work can serve as a benchmark in future research related to placement issues of IoT application services in Fog Computing environments since the model approach is generic and applies to a wide range of IoT use cases.

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“…This improves the performance, by minimizing latency and availability, since resources are accessible even if Internet access is not available [10]. Fog-based solutions aim at introducing an intermediate architectural layer where resources and applications are made available in the proximity of end devices, thus avoiding continuous access to the Cloud.…”

Section: Related Workmentioning

confidence: 99%

Virtual Replication of IoT Hubs in the Cloud: A Flexible Approach to Smart Object Management

Cirani¹,

Ferrari

Mancin

et al. 2018

JSAN

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Abstract:In future years, the Internet of Things is expected to interconnect billions of highly heterogeneous devices, denoted as "smart objects", enabling the development of innovative distributed applications. Smart objects are constrained sensor/actuator-equipped devices, in terms of computational power and available memory. In order to cope with the diverse physical connectivity technologies of smart objects, the Internet Protocol is foreseen as the common "language" for full interoperability and as a unifying factor for integration with the Internet. Large-scale platforms for interconnected devices are required to effectively manage resources provided by smart objects. In this work, we present a novel architecture for the management of large numbers of resources in a scalable, seamless, and secure way. The proposed architecture is based on a network element, denoted as IoT Hub, placed at the border of the constrained network, which implements the following functions: service discovery; border router; HTTP/Constrained Application Protocol (CoAP) and CoAP/CoAP proxy; cache; and resource directory. In order to protect smart objects (which cannot, because of their constrained nature, serve a large number of concurrent requests) and the IoT Hub (which serves as a gateway to the constrained network), we introduce the concept of virtual IoT Hub replica: a Cloud-based "entity" replicating all the functions of a physical IoT Hub, which external clients will query to access resources. IoT Hub replicas are constantly synchronized with the physical IoT Hub through a low-overhead protocol based on Message Queue Telemetry Transport (MQTT). An experimental evaluation, proving the feasibility and advantages of the proposed architecture, is presented.

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Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing

Cited by 265 publications

References 5 publications

Performance of video processing at the edge for crowd-monitoring applications

Performance of video processing at the edge for crowd-monitoring applications

Resource provisioning for IoT application services in smart cities

Virtual Replication of IoT Hubs in the Cloud: A Flexible Approach to Smart Object Management

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