Hybrid mist-cloud systems for large scale geospatial big data analytics and processing: opportunities and challenges

Barik, Rabindra K.; Misra, Chinmaya; Lenka, Rakesh Kumar; Dubey, Harishchandra; Mankodiya, Kunal

doi:10.1007/s12517-018-4104-3

Cited by 41 publications

(8 citation statements)

References 44 publications

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“…For real-time visualization and near real-time analysis of streaming data within GIS environments, technologies such as the Internet of Things are required to be interoperable with CyberGIS. The real-time output also depends on reliable "geospatial big data" computation algorithms to deal with volume, variety, and velocity of data [12][13][14][15]. There remains geospatial big data challenges that should be investigated in terms of availability of data on multi-cloud models, data integrity, data standards, and heterogeneity.…”

Section: Integration Of Gis and Bimmentioning

confidence: 99%

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Shirowzhan

Tan

Sepasgozar

2020

IJGI

128

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Smart technologies are advancing, and smart cities can be made smarter by increasing the connectivity and interactions of humans, the environment, and smart devices. This paper discusses selective technologies that can potentially contribute to developing an intelligent environment and smarter cities. While the connectivity and efficiency of smart cities is important, the analysis of the impact of construction development and large projects in the city is crucial to decision and policy makers, before the project is approved. This raises the question of assessing the impact of a new infrastructure project on the community prior to its commencement—what type of technologies can potentially be used for creating a virtual representation of the city? How can a smart city be improved by utilizing these technologies? There are a wide range of technologies and applications available but understanding their function, interoperability, and compatibility with the community requires more discussion around system designs and architecture. These questions can be the basis of developing an agenda for further investigations. In particular, the need for advanced tools such as mobile scanners, Geospatial Artificial Intelligence, Unmanned Aerial Vehicles, Geospatial Augmented Reality apps, Light Detection, and Ranging in smart cities is discussed. In line with smart city technology development, this Special Issue includes eight accepted articles covering trending topics, which are briefly reviewed.

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Section: Integration Of Gis and Bimmentioning

confidence: 99%

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Shirowzhan

Tan

Sepasgozar

2020

IJGI

128

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“…Mist computing can play a vital role in improving the data collection for IoT devices as the processing is in the Edge computing environment. Barik et al, 2017 [ 11 ] developed a new platform called MistGiS for geospatial big data and applied it in two cases: tourism information infrastructure management and a faculty information retrial system. The Raspberry Pi microprocessor was utilized to build the framework.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Almudayni,

Soh,

2023

Sensors

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In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.

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“…Mist computing minimises latency and boosts autonomy. Cloud, fog, and mist computing are complementary because the fog layer's gateway can run computationally complex application tasks, while edge devices can run less intensive ones [23].…”

Section: Geospatial Mist Computingmentioning

confidence: 99%

“…The paradigm of cloud computing allows for the pooling of resources and the provision of services on demand. You are able to do data analysis and visualization with the help of this computing method [17,23,35].…”

Section: Geospatial Cloud Computingmentioning

confidence: 99%

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PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

Panigrahi,

Goswami,

Apat

et al. 2023

Mathematics

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The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements.

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Hybrid mist-cloud systems for large scale geospatial big data analytics and processing: opportunities and challenges

Cited by 41 publications

References 44 publications

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Digital Twin and CyberGIS for Improving Connectivity and Measuring the Impact of Infrastructure Construction Planning in Smart Cities

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

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