GIS spatial information sharing of smart city based on cloud computing

Cai, Pengzhen; Jiang, Qijie

doi:10.1007/s10586-018-2311-z

Cited by 9 publications

(1 citation statement)

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“…Standardizing service interfaces and data models is possible with several well-known products such as WMS (web map service), WFS (web feature service), WCS (web coverage service), and CSW (catalogue service for the web). In the meantime, a web processing service (WPS) interface can be utilized to gain access to any environmental model or geospatial algorithm that is classified as a geoprocessing service [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%