MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications

Arkian, Hamid Reza; Diyanat, Abolfazl; Pourkhalili, Atefe

doi:10.1016/j.jnca.2017.01.012

Cited by 203 publications

(106 citation statements)

References 33 publications

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“…Various works solely considered latency among the metrics related with the network, 26,42,49,56,57,59 often but not always along with bandwidth. 24,[28][29][30][31][32]43,58 Bandwidth was also considered by Taneja and Davy 36 and Arkian et al 38 along with hardware constraints. On the contrary, the studies that included link reliability also included other network constraints, such as bandwidth, 34,55 latency, and topology, 53 or latency, bandwidth, and topology.…”

Section: Network Constraintsmentioning

confidence: 99%

“…The resource capacity is usually modelled as a vector (or set) of elements, one for each of the hardware elements considered in the fog devices or the network. Examples of those vectors for the case of the fog devices are: considering CPU 24,58 ; considering CPU and storage 38 ; CPU, RAM, and storage 30,31,40,47 ; considering only storage. 39,41,45 Examples of resource capacity vectors both for fog nodes and network are: considering CPU, RAM, and network bandwidth 36,55 ; considering CPU, RAM, storage, and network bandwidth 33,50 ; considering processing and transmission capacities.…”

Section: Node Constraintsmentioning

confidence: 99%

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How to place your apps in the fog: State of the art and open challenges

et al. 2019

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Summary Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency‐sensitive and bandwidth‐hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open‐source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.

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Section: Network Constraintsmentioning

confidence: 99%

Section: Node Constraintsmentioning

confidence: 99%

How to place your apps in the fog: State of the art and open challenges

et al. 2019

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“…For example, Arkian et al [51] tackle resource issues in vehicular clouds by considering all three resource types. Elsewhere, crowdsensing is tackled with the same resource considerations [60].…”

Section: Computation Communication and Storagementioning

confidence: 99%

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

101

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Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

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“…To measure the effectiveness of an approach involving embedded systems, it is necessary to have a mathematical model to validate the distributed architecture. To the best of the authors' knowledge, there is no current proposed architecture to develop UAV cognitive systems that incorporate fog concepts in itself as part of the processing stack [30,33,34] neither a specific mathematical model to validate all necessary requirements in this context [8][9][10][11][12][13][14][15].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The latency is modelled in slightly different ways in the literature. In [10], the latency is calculated by combining the time required for transfer data between nodes, the processing time, and the period related to balance the services among nodes. In [45], these previous factors are considered along with a nonlinear component that accounts for differences in transmission channels such as the queuing order along nodes.…”

Section: Latency Throughput and Power Constraintsmentioning

confidence: 99%

A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

Pinto

Marcato

Melo

et al. 2019

Wireless Communications and Mobile Computing

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Unmanned aerial vehicles (UAVs) are a relatively new technology. Their application can often involve complex and unseen problems. For instance, they can work in a cooperative-based environment under the supervision of a ground station to speed up critical decision-making processes. However, the amount of information exchanged among the aircraft and ground station is limited by high distances, low bandwidth size, restricted processing capability, and energy constraints. These drawbacks restrain large-scale operations such as large area inspections. New distributed state-of-the-art processing architectures, such as fog computing, can improve latency, scalability, and efficiency to meet time constraints via data acquisition, processing, and storage at different levels. Under these amendments, this research work proposes a mathematical model to analyze distribution-based UAVs topologies and a fog-cloud computing framework for large-scale mission and search operations. The tests have successfully predicted latency and other operational constraints, allowing the analysis of fog-computing advantages over traditional cloud-computing architectures.

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MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications

Cited by 203 publications

References 33 publications

How to place your apps in the fog: State of the art and open challenges

How to place your apps in the fog: State of the art and open challenges

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

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