Fog Computing for Next Generation Transport- a Battery Swapping System Case Study

Hussain, Md. Muzakkir; Alam, Mohammad Saad; Beg, M. M. Sufyan

doi:10.1007/s40866-018-0043-z

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…The essential nodes in the system include set of cloud servers D, fog nodes F, and data users N. The architecture is virtually deployed on the essential nodes in an arbitrary SG network. For simulating the pilot SG topology, the 100 most populated places around the world are considered (i.e., |F| = 100), the corresponding population for representing the number of consumer/data generation nodes and the corresponding geographical coordinates are used to determine the relative Euclidian distance [48]. The consumer endpoints within a particular city are logically grouped to form a cluster and are associated to an FCN.…”

Section: Simulation and Algorithmic Set-upmentioning

confidence: 99%

Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures

Hussain

Beg

2019

BDCC

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The fast-paced development of power systems necessitates the smart grid (SG) to facilitate real-time control and monitoring with bidirectional communication and electricity flows. In order to meet the computational requirements for SG applications, cloud computing (CC) provides flexible resources and services shared in network, parallel processing, and omnipresent access. Even though CC model is considered to be efficient for SG, it fails to guarantee the Quality-of-Experience (QoE) requirements for the SG services, viz. latency, bandwidth, energy consumption, and network cost. Fog Computing (FC) extends CC by deploying localized computing and processing facilities into the edge of the network, offering location-awareness, low latency, and latency-sensitive analytics for mission critical requirements of SG applications. By deploying localized computing facilities at the premise of users, it pre-stores the cloud data and distributes to SG users with fast-rate local connections. In this paper, we first examine the current state of cloud based SG architectures and highlight the motivation(s) for adopting FC as a technology enabler for real-time SG analytics. We also present a three layer FC-based SG architecture, characterizing its features towards integrating massive number of Internet of Things (IoT) devices into future SG. We then propose a cost optimization model for FC that jointly investigates data consumer association, workload distribution, virtual machine placement and Quality-of-Service (QoS) constraints. The formulated model is a Mixed-Integer Nonlinear Programming (MINLP) problem which is solved using Modified Differential Evolution (MDE) algorithm. We evaluate the proposed framework on real world parameters and show that for a network with approximately 50% time critical applications, the overall service latency for FC is nearly half to that of cloud paradigm. We also observed that the FC lowers the aggregated power consumption of the generic CC model by more than 44%.

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Section: Simulation and Algorithmic Set-upmentioning

confidence: 99%

Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures

Hussain

Beg

2019

BDCC

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Intelligent Battery-Aware Energy Management System for Electric Vehicles

Mahmoud

Elkhouly

Azzazy

et al. 2019

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Big Data Analytics Platforms for Electric Vehicle Integration in Transport Oriented Smart Cities

Hussain

Beg

Alam

et al. 2020

Cyber Warfare and Terrorism

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Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics.

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Fog Computing for Next Generation Transport- a Battery Swapping System Case Study

Cited by 5 publications

References 19 publications

Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures

Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures

Intelligent Battery-Aware Energy Management System for Electric Vehicles

Big Data Analytics Platforms for Electric Vehicle Integration in Transport Oriented Smart Cities

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