Computational Viability of Fog Methodologies in IoT Enabled Smart City Architectures-A Smart Grid Case Study

Hussain, Md. Muzakkir and Saad Alam, Mohammad and Sufyan Beg, M.M. (2018) Computational Viability of Fog Methodologies in IoT Enabled Smart City Architectures-A Smart Grid Case Study. EAI Endorsed Transactions on Smart Cities, 2 (7): e3. ISSN 2518-3893

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The gradual evolution in the Information Communication Technology (ICT) support of Smart City (SC) architecture leveraged with meshes of Internet of Things (IoT) creates and welcomes research and investment efforts from academia, R&Ds and policymakers. The IoT utilities are being deployed at every layers of a typical SG backbone namely Application layer, Energy layer and Communication layer. The geo-distributed clusters of IoT “objects” produce galactic volume of data that exacerbates the need to make a paradigm shift from centralized data center based processing to a hybrid model that supports both in situ as well as cloud based storage and computational resources. To combat such SC issues, Fog Computing (FC) emerges as a promising solution, which pushes the computation resources onto the network edge nodes. This work investigates the high performance of Fog Computing over generic cloud computing in terms of metrics viz. latencies, power consumption etc, through a Smart Grid (SG) use-case. Through an operational cost optimization framework, the work comprehends the suitability of fog methodologies to make a synergistic interplay with the core centered clouds thus empowering a wide breed of real-time and latency free services. Finally, an overview of the core orchestration issues, challenges, and future research directions are presented for FC enabled SCs.

Item Type: Article
Uncontrolled Keywords: Smart Cities (SC), Smart Grid (SG), Internet of Things (IoT), Fog computing, Cloud computing, Differential Evolution (DE)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 09 Sep 2020 11:39
Last Modified: 09 Sep 2020 11:39

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