Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities

He, Junjia; Wei, Jian; Chen, Kai; Tang, Zuoyin; Zhou, Yi; Zhang, Yan

doi:10.1109/jiot.2017.2724845

Cited by 208 publications

(109 citation statements)

References 10 publications

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“…Ni et al proposed a Priced Timed Petri Net (PTPN)‐based dynamic resource allocation and scheduling strategy for fog computing environments. He et al developed QoS‐aware admission control, offloading, and resource allocation schemes to support data analytics services aimed at maximizing the analytics service utilities. The proposed admission control function takes decisions based on the work models developed from offline execution of benchmarks.…”

Section: Fog Computing Aspectsmentioning

confidence: 99%

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

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Summary The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.

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Section: Fog Computing Aspectsmentioning

confidence: 99%

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

View full text Add to dashboard Cite

show abstract

“…In our case study, Raspberry Pi 2 model B is chosen as the system responsible for interacting with users (our lightweight node). The reason for using Raspberry is essentially due to its versatility, computational power (despite the reduced size), and low power consumption that makes it a viable solution also in IoT applications where scaling is a matter and some intelligence needs to be endowed inside the devices . The server is a PC with Windows 10×64 where the triple store runs.…”

Section: System Designmentioning

confidence: 99%

IoT and semantic web technologies for event detection in natural disasters

Greco

Ritrovato

Tiropanis

et al. 2018

Concurrency and Computation

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Summary Natural disasters cannot be predicted well in advance, but it is still possible to decrease the loss of life and mitigate the damages, exploiting some peculiarities that distinguish them. Smart collection, integration, and analysis of data produced by distributed sensors and services are key elements for understanding the context and supporting decision making process for disaster prevention and management. In this paper, we demonstrate how Internet of Things and Semantic Web technologies can be effectively used for abnormal event detection in the contest of an earthquake. In our proposal, a prototype system, which retrieves the data streams from IoT sensors and web services, is presented. In order to contextualize and give a meaning to the data, semantic web technologies are applied for data annotation. We evaluate our system performances by measuring the response time and other parameters that are important in a disaster detection scenario.

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“…It will result in the generation of a huge amount of data that will not be efficiently handled by Cloud, consequently resulting in long latencies and network congestion. Even now, some of the real‐time, latency‐sensitive, and geo‐spatially distributed IoT applications like smart healthcare monitoring, smart traffic surveillance, virtual reality, etc, cannot be efficiently served using Cloud computing as they need low latency . To overcome these limitations of CIoT, several approaches like Edge Computing, Mobile Computing, Fog Computing, etc, have been proposed to provide computational, storage, decision making, and networking services at close proximity to the source node or end‐user application …”

Section: Introductionmentioning

confidence: 99%

“…healthcare monitoring, smart traffic surveillance, virtual reality, etc, cannot be efficiently served using Cloud computing as they need low latency. 5 To overcome these limitations of CIoT, several approaches like Edge Computing, Mobile Computing, Fog Computing, etc, have been proposed to provide computational, storage, decision making, and networking services at close proximity to the source node or end-user application. 6 Among various proposed approaches, Fog Computing is the one that has recently gained the most attention.…”

mentioning

confidence: 99%

A job scheduling algorithm for delay and performance optimization in fog computing

Jamil

Shojafar

Ahmed

et al. 2019

Concurrency and Computation

107

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Summary Due to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.

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Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities

Cited by 208 publications

References 10 publications

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

IoT and semantic web technologies for event detection in natural disasters

A job scheduling algorithm for delay and performance optimization in fog computing

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