Viability of Fog Methodologies in IoT aware Smart Grid Architectures

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

doi:10.4108/eai.7-8-2017.152995

Cited by 4 publications

(9 citation statements)

References 8 publications

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“…However, sound mathematical foundation was not drawn to support the model. Besides, a range of literatures [3,5,12,17,18] exemplify the sprouting opportunities, challenges, and research directions in fog-based SG, and emphasize the significance of software platforms as well as SOAs for big data analytics in SG.…”

Section: Work Related To Fog Computing In Smart Gridmentioning

confidence: 99%

“…The underlying notion of FC is the distribution of store, communicate, control and compute resources from the edge to the remote cloud continuum [3]. The fog architectures may be either fully distributed, mostly centralized, or somewhere in between.…”

Section: Fog Computing For Smart Grid Architecturementioning

confidence: 99%

“…Further, the FC will provide a common lifecycle management framework for all applications, offering capabilities for composing, configuring, dispatching, activating and deactivating, adding and removing, and updating applications [42]. It will further provide a secure execution environment for fog services and applications [3].…”

Section: Fog Computing For Smart Grid Architecturementioning

confidence: 99%

“…However, due to the current energy computing bandwidth limitations of the wireless domain, to date, a system of this complexity has been unfeasible [1]. Through the notion of Internet of Things (IoT), the future Internet is bringing SG machines, devices, and sensors connected to the internet [2,3]. By interconnecting the machines with the Internet they become smart, with the ability to react and make decisions on their own.…”

Section: Introductionmentioning

confidence: 99%

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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: Work Related To Fog Computing In Smart Gridmentioning

confidence: 99%

Section: Fog Computing For Smart Grid Architecturementioning

confidence: 99%

Section: Fog Computing For Smart Grid Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hussain

Beg

2019

BDCC

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“…Fog Nodes (FN), implemented at the edge of network, provide highly virtualized platform for compute, storage, and networking services between IoT end points and cloud data centers. In contrast to centralized cloud services, FNs are targeted to serve the BSS consumers with geodistributed deployments [31].…”

Section: Architecture Of Cloud-fog Based Battery Swap Stations (Bss)mentioning

confidence: 99%

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

Hussain

Alam

Beg

2018

Technol Econ Smart Grids Sustain Energy

Self Cite

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Electric vehicle (EV) is a promising technology for reducing environmental impacts of road transport. Efficient EV charging control strategies that can affect the impacts and benefits is a potential research problem. Adopting the notion of IoT, in this paper, we present a Cloud-Fog based Battery Swapping Topology (BSS). A QoS ensuring timing model is proposed for defining the charging management of EV batteries across the BSS. For optimal BSS infrastructure planning, we also present a cost optimization framework, considering the timing and architectural constraints. The potential solution approaches for the given optimization formulation is also discussed.

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IoT/cloud‐enabled smart services: A review on QoS requirements in fog environment and a proposed approach based on priority classification technique

Ksentini

Jebalia

Tabbane

2019

Int J Communication

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Summary Smart applications are increasingly emerged in our daily professional and personal lives, ranging from augmented reality and gaming applications to E‐health and telesurgery services. In fact, the growing development of Internet of Things (IoT) and its integration with cloud and fog paradigms have encouraged infrastructures and services providers to perform intelligence in advanced technologies. However, the deployment of IoT/cloud‐enabled smart services still suffers from several quality of services (QoSs) management issues such as latency sensitivity for interactive applications and real‐time big data analytics. In this paper, we propose to present a novel approach for the management of smart services QoS. Accordingly, we introduce an overview of QoS requirements in fog computing environment for several IoT/cloud‐enabled use cases, to identify QoS metrics. The synthesis of the requirements review allows us to present a solution for QoS enhancements on the basis of a priority classification (PC) technique. Simulated with iFogSim, PC shows a significant enhancement of the system performance, notably for latency, energy consumption, and network usage.

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Viability of Fog Methodologies in IoT aware Smart Grid Architectures

Cited by 4 publications

References 8 publications

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

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

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

IoT/cloud‐enabled smart services: A review on QoS requirements in fog environment and a proposed approach based on priority classification technique

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