MistGIS: Optimizing Geospatial Data Analysis Using Mist Computing

Barik, Rabindra K.; Tripathi, Ankita; Dubey, Harishchandra; Lenka, Rakesh Kumar; Pratik, Tanjappa; Sharma, Suraj; Mankodiya, Kunal; Kumar, Vinay; Das, Himansu

doi:10.1007/978-981-10-7871-2_70

Cited by 40 publications

(8 citation statements)

References 16 publications

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“…Fog framework added privacy benefit where we process the data locally at Fog devices and send only the analysis results to cloud layer. Figure 1 (b) shows the Conceptual diagram of the fog framework for geospatial health data storage and analysis [4,10,11]. From the above conceptual diagram of cloud and fog framework, it observed that the geospatial data as a key components for data analysis in cloud layer [9,8].…”

Section: Related Work 21 Fog and Cloud Computingmentioning

confidence: 98%

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

Barik

Dubey

Mankodiya

2017

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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The smart health paradigms employ Internet-connected wearables for telemonitoring, diagnosis for providing inexpensive healthcare solutions. Fog computing reduces latency and increases throughput by processing data near the body sensor network. In this paper, we proposed a secure serviceorientated edge computing architecture that is validated on recently released public dataset. Results and discussions support the applicability of proposed architecture for smart health applications. We proposed SoA-Fog i.e. a three-tier secure framework for efficient management of health data using fog devices. It discuss the security aspects in client layer, fog layer and the cloud layer. We design the prototype by using win-win spiral model with use case and sequence diagram. Overlay analysis was performed using proposed framework on malaria vector borne disease positive maps of Maharastra state in India from 2011 to 2014. The mobile clients were taken as test case. We performed comparative analysis between proposed secure fog framework and state-of-the art cloud-based framework.

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Section: Related Work 21 Fog and Cloud Computingmentioning

confidence: 98%

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

Barik

Dubey

Mankodiya

2017

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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“…In addition, attention can help to balance the tasks which need resources in resource‐constrained CPSs. Barik et al 39 introduce MistGIS, which is used to process geographical information system (GIS) data in Mist and Edge and store the data in the cloud. They use a K‐means model to cluster nodes in different geographical areas and evaluate how many Fog and Mist nodes are needed based on their resources to analyze various GIS datasets to reduce the overhead of the cloud.…”

Section: Related Workmentioning

confidence: 99%

A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks

Shahraki

Geitle

Haugen

2020

Trans Emerging Tel Tech

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Internet of Things (IoT) is a new technology that is driving the connection of billions of devices around the world. Because these devices are often resource-constrained and very heterogeneous, this presents unique challenges. To address some of these challenges, new paradigms of Edge and Fog are emerging to bring computational resources of the IoT networks from remote devices like cloud closer to the end-devices. Mist computing is a new paradigm that attempts to make use of the more resource-rich nodes that are closer than Edge nodes to end-users. Since these nodes might have enough resources to host services, execute tasks or even run containers, the utilization of network resources might be improved, and delay reduced by utilizing these nodes. The nodes must, therefore, be assessed to determine which nodes should offer resources to other nodes based on their situation. In this article, a new comparative assessment model for ranking Mist nodes in highly heterogeneous massive-scale IoT networks in order to discover nodes that can offer their resources is proposed. The Mist nodes are evaluated based on parameters like resources, connections, applications, and environmental parameters to heuristically compare the neighbors with a novel learning-to-rank method to predict a suitability score for each node. The most suitable neighbor is then selected based on the score, with load balancing accomplished by a second chance method. When evaluating the performance, the results show that the proposed method succeeds in identifying resource-rich nodes, while considering the selection of other nodes. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Cloud can interact with the IoT device via the fog node. This paper concentrates on the side of fog computing, which allows users a higher computing power at the instrument end.Reliance on fog will help cut the costs associated with the Cloud to an extent [18,19].…”

Section: Fog Computingmentioning

confidence: 99%

Smart fog: Fog computing framework for unsupervised clustering analytics in wearable Internet of Things

Borthakur

Dubey

Constant

et al. 2017

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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The increasing use of wearables in smart telehealth generates heterogeneous medical big data. Cloud and fog services process these data for assisting clinical procedures. IoT based ehealthcare have greatly benefited from efficient data processing. This paper proposed and evaluated use of low-resource machine learning on Fog devices kept close to the wearables for smart healthcare. In state-of-the-art telecare systems, the signal processing and machine learning modules are deployed in the cloud for processing physiological data. We developed a prototype of Fog-based unsupervised machine learning big data analysis for discovering patterns in physiological data. We employed Intel Edison and Raspberry Pi as Fog computer in proposed architecture. We performed validation studies on real-world pathological speech data from in-home monitoring of patients with Parkinson's disease (PD). Proposed architecture employed machine learning for analysis of pathological speech data obtained from smartwatches worn by the patients with PD. Results showed that proposed architecture is promising for low-resource clinical machine learning. It could be useful for other applications within wearable IoT for smart telehealth scenarios by translating machine learning approaches from the cloud backend to edge computing devices such as Fog.

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MistGIS: Optimizing Geospatial Data Analysis Using Mist Computing

Cited by 40 publications

References 16 publications

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks

Smart fog: Fog computing framework for unsupervised clustering analytics in wearable Internet of Things

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