| Title | FOCAN: A Fog-supported smart city network architecture for management of applications in the Internet of Everything environments |
|---|---|
| ID_Doc | 36915 |
| Authors | Naranjo, PGV; Pooranian, Z; Shojafar, M; Conti, M; Buyya, R |
| Title | FOCAN: A Fog-supported smart city network architecture for management of applications in the Internet of Everything environments |
| Year | 2019 |
| Published | |
| Abstract | Smart city vision brings emerging heterogeneous communication technologies such as Fog Computing (FC) together to substantially reduce the latency and energy consumption of Internet of Everything (IoE) devices running various applications. The key feature that distinguishes the FC paradigm for smart cities is that it spreads communication and computing resources over the wired/wireless access network (e.g., proximate access points and base stations) to provide resource augmentation (e.g., cyberforaging) for resource- and energy-limited wired/wireless (possibly mobile) things. Motivated by these considerations, this paper presents a Fog-supported smart city network architecture called Fog Computing Architecture Network (FOCAN), a multi-tier structure in which the applications are running on things that jointly compute, route, and communicate with one another through the smart city environment. FOCAN decreases latency and improves energy provisioning and the efficiency of services among things with different capabilities. In particular, three types of communications are defined between FOCAN devices - interprimary, primary, and secondary communication-to manage applications in a way that meets the quality of service standards for the Internet of Everything. One of the main advantages of the proposed architecture is that the devices can provide the services with low energy usage and in an efficient manner. Simulation results for a selected case study demonstrate the tremendous impact of the FOCAN energy efficient solution on the communication performance of various types of things in smart cities. (C) 2018 Elsevier Inc. All rights reserved. |
| https://arxiv.org/pdf/1710.01801 |
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