Fog Computing: An Overview of Big IoT Data Analytics

Anawar, Muhammad Rizwan; Wang, Shangguang; Zia, Muhammad Azam; Jadoon, Ahmer Khan; Akram, Umair; Raza, Salman

doi:10.1155/2018/7157192

Cited by 152 publications

(78 citation statements)

References 120 publications

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“…To improve latency, fog computing [12] has been introduced to move the time-critical functions to the locations near clients. Fog computing has been widely discussed in both Internet of Things (IoT) [13] and mobile computing [14] to offload server burden [15], enable location awareness, and provide real-time interaction. Among its many applications, mobile gaming [16] and mobile reality [17] are two important examples.…”

Section: Fog Computingmentioning

confidence: 99%

“…Otherwise, events will be delivered from Qp. If all expected events have been collected (Line 6-8, [13][14], then they will be delivered to Qd in the sequence of γ for cycle c. The range [MinSeq(s, c), Seq(s, c)] specifies the deliverable sequence number for each sender s and cycle c. The calculation of γ (Line 16) which ensures that all replicas can deliver the concurrent events from different senders in the same sequence. If there is any event, a query message will be sent to the group leader.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

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Virtual Net: A Decentralized Architecture for Interaction in Mobile Virtual Worlds

Shen

Guo

2018

Wireless Communications and Mobile Computing

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With the development of mobile technology, mobile virtual worlds have attracted massive users. To improve scalability, a peer-to-peer virtual world provides the solution to accommodate more users without increasing hardware investment. In mobile settings, however, existing P2P solutions are not applicable due to the unreliability of mobile devices and the instability of mobile networks. To address the issue, a novel infrastructure model, called Virtual Net, is proposed to provide fault-tolerance in managing user content and object state. In this paper, the key problem, namely object state update, is resolved to maintain state consistency and high interaction responsiveness. This work is important in implementing a scalable mobile virtual world.

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Section: Fog Computingmentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Virtual Net: A Decentralized Architecture for Interaction in Mobile Virtual Worlds

Shen

Guo

2018

Wireless Communications and Mobile Computing

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“…In a hierarchical structure, the data processing architecture of the integrated system is based on three levels: consensus, edge computing [35], and cloud computing.…”

Section: Introductionmentioning

confidence: 99%

Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture

Popescu

Stoican

Stâmâtescu

et al. 2020

Sensors

183

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The growing need for food worldwide requires the development of a high-performance, high-productivity, and sustainable agriculture, which implies the introduction of new technologies into monitoring activities related to control and decision-making. In this regard, this paper presents a hierarchical structure based on the collaboration between unmanned aerial vehicles (UAVs) and federated wireless sensor networks (WSNs) for crop monitoring in precision agriculture. The integration of UAVs with intelligent, ground WSNs, and IoT proved to be a robust and efficient solution for data collection, control, analysis, and decisions in such specialized applications. Key advantages lay in online data collection and relaying to a central monitoring point, while effectively managing network load and latency through optimized UAV trajectories and in situ data processing. Two important aspects of the collaboration were considered: designing the UAV trajectories for efficient data collection and implementing effective data processing algorithms (consensus and symbolic aggregate approximation) at the network level for the transmission of the relevant data. The experiments were carried out at a Romanian research institute where different crops and methods are developed. The results demonstrate that the collaborative UAV–WSN–IoT approach increases the performances in both precision agriculture and ecological agriculture.

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“…Furthermore, ubiquitous computing and the development of 4G/5G technologies have provided computing services everywhere by connecting billions of new IoT applications and devices. By 2020, there will be about 5.4 billion connected devices in the world and about 1 trillion by 2025 . 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.…”

Section: Introductionmentioning

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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Fog Computing: An Overview of Big IoT Data Analytics

Cited by 152 publications

References 120 publications

Virtual Net: A Decentralized Architecture for Interaction in Mobile Virtual Worlds

Virtual Net: A Decentralized Architecture for Interaction in Mobile Virtual Worlds

Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture

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

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