A Novel Architecture for Efficient Fog to Cloud Data Management in Smart Cities

Mahmud

2020

Preprint

Novel coronavirus disease 2019 (COVID-19) has found as a climacteric pandemic that spreads from human-to-human through contiguity. It is not possible to control the virus properly because its vaccine has not been discovered yet. As of now, various contact tracing approaches are being taken to contain the spread of the virus by breaking its transmission chain. Recently, few technologies are being exploited for contact tracing such as geological location, Bluetooth and google maps. Governments of some countries have already launched contact tracing applications to identify the close contact people of COVID-19 positive patients.However, these methods may not be effective without a huge amount of testing facility since the tracing action starts when a COVID-19 testing result is found positive. In this paper, we have proposed a novel AI-powered data management framework for smart city named IoT-to-Edge-to-Fog-to-cloudlet-to-Cloud (IEF2C) data management framework which will be exploited to identify the COVID-19 suspected patients in smart cities and also be used to inform the surrounding people about the suspected patient. The proposed framework is isolated into five layers which have different tasks such as data collection, processing, authentication and storage. The framework will bring all the citizens under a common platform as well as will ensure the user and device mobility. It will mitigate the information gap by helping concerned authorities and users to take proper steps in such a pandemic era.

Section: Literature Reviewmentioning

confidence: 96%

“…The proposed data management architecture ensures security and quick data accessibility. Later, the authors of the COSA-DLC model have proposed a new smart city comprehensive data life cycle (SCC-DLC) [27]. It is more efficient than the COSA-DLC model.…”

Section: Literature Reviewmentioning

confidence: 99%

IEF2C: A novel AI-powered framework for suspected COVID-19 patient detection and contact tracing in smart cities

Mahmud

2020

Preprint

“…As smart devices allow decisions and autonomous operations locally, this decreases the amount of data on-demand to enhance network reliability. FC applications in the smart city are the following [30]:  Smart homes (home security, fire detection, and temperature control).  Weather and water system (weather condition, and water quality control and leakage detection).…”

Section: G Decisions and Local Autonomous Operationsmentioning

confidence: 99%

Fog computing scheduling algorithm for smart city

Al-smadi

Aloglah

Abu-darwish

et al. 2021

IJECE

With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of object) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT infrastructure for smart cities. Then, spanning-tree routing (STP) protocol is used for data collection and routing. Further, 5G networks are proposed to establish fast transmission and communication between users. Finally, the performance of our proposed system is evaluated in terms of response time, latency, and amount of data used.

2018 International Conference on Information and Communication Technology Convergence (ICTC)

“…As a summary, in this paper, we have proposed a data management architecture for the ZEN center [5] with respect to the idea of the F2C data management architecture in the context of a smart city [6,7]. In addition, we also have studied the experience of the ZEB center [8] in terms of data flow and movement as a previous research by the ZEN center.…”

Section: Introductionmentioning

confidence: 99%

A Zero Emission Neighbourhoods Data Management Architecture for Smart City Scenarios: Discussions toward 6Vs challenges

Sinaeepourfard

Krogstie

Petersen

et al. 2018

Self Cite

A huge volume of data are being generated from multiple sources, including smart cities, the IoT devices, scientific modeling, or different big data simulations; but also from users' daily activities. These daily new data are added to historical repositories, providing the huge and complex universe of the digital data. Recently, the Fog-to-Cloud (F2C) data management architecture is envisioned to handle all big data complexities, from IoT devices (the closest layer to the users) to cloud technologies (the farthest layer to the IoT devices), as well as different data phases from creation to usage from fog to cloud scenario. Moreover, the F2C data management architecture can have several benefits from the combined advantages of fog (distributed) and cloud (centralized) technologies including reducing network traffic, reducing latencies drastically while improving security. In this paper, we have several novel contributions. First, we described the previous studies of the Zero Emission Buildings (ZEB) in the context of the data flow and movement architecture. Second, we have proposed Zero Emission Neighbourhoods (ZEN) data management architecture for smart city scenarios based on a distributed hierarchical F2C data management. Indeed, we used the 6Vs big data challenges (Volume, Variety, Velocity, Variability, Veracity, and Value) for evaluating the data management architectures (including ZEB and ZEN). The result of the evaluation shows that our proposed ZEN data management architecture can address 6Vs challenges and is able to manage the data lifecycle from its production up to its usage.