Femtolet: A novel fifth generation network device for green mobile cloud computing

Mukherjee, Anwesha; De, Debashis

doi:10.1016/j.simpat.2016.01.014

Cited by 26 publications

(27 citation statements)

References 22 publications

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“…Here, Femtolet is used in the health care centers for faster reporting of the number patients affected, deceased and cured, because this information is also very important to monitor the current status of the pandemic in a geographic region. Femtolet is small cell base station which has storage and processing ability [23].…”

Section: Spatial Data Infrastructure (Sdi): An Enabler Of Ioshtmentioning

confidence: 99%

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STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management

Ghosh¹,

Mukherjee²,

Ghosh³

et al. 2021

Preprint

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Several researches and evidence show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease) which has far reaching sequels in all aspects of human lives ranging from rapid mortality rates to economic and social disruption across the world. In the recent time, COVID-19 (Coronavirus Disease 2019) pandemic disrupted normal human lives, and motivated by the urgent need of combating COVID-19, researchers have put significant efforts in modelling and analysing the disease spread patterns for effective preventive measures (in addition to developing pharmaceutical solutions, like vaccine). In this regards, it is absolutely necessary to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous datasources to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. Specifically, human mobility, travel history and other transport statistics have significant impacts on the spread of any infectious disease. In this direction, this paper proposes a spatio-temporal knowledge mining framework, named STOPPAGE to model the impact of human mobility and other contextual information over large geographic area in different temporal scales. The framework has two major modules: (i) Spatio-temporal data and computing infrastructure using fog/edge based architecture; and (ii) Spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. Typically, we develop a Pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hot-spot zones; and provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods.

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Section: Spatial Data Infrastructure (Sdi): An Enabler Of Ioshtmentioning

confidence: 99%

“…IoSHT is capable of collecting BAN (Body area network) information and other contextual data such as environment-temperature, location data, mobility information etc. Femtolet (a small cell base station with storage and computation ability) [23] is used for faster reporting of patient information of hospitals.…”

Section: Introductionmentioning

confidence: 99%

STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management

Ghosh¹,

Mukherjee²,

Ghosh³

et al. 2021

Preprint

Self Cite

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“…Reduction of latency improves the quality of service (QoS). For improving the QoS selection of a cloudlet in an energy‐efficient way, a fifth‐generation mobile network device femtolet has been proposed, which has been used as a fog device in another work . A femtolet is a device that possesses the features of a femtocell and a cloudlet .…”

Section: Related Workmentioning

confidence: 99%

“…A cloudlet, by working as an agent between the mobile device and cloud servers, solves this problem . The femtolet has been proposed for providing both communication and computation to the mobile devices under its coverage . It is an indoor base station containing large storage and having processing power.…”

Section: Introductionmentioning

confidence: 99%

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E²R‐F²N: Energy‐efficient retailing using a femtolet‐based fog network

Mukherjee

Buyya

2018

Softw Pract Exp

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Energy-efficient smart retail system design is a challenging research area. In this paper, we propose an automated retail system using a femtolet-based fog network. A femtolet is an indoor base station providing computation and storage. Femtolets in our system work as indoor base stations and maintain databases of the products located in their respective coverage areas. The femtolets switch to active or idle mode according to the user's presence in its coverage. A smart trolley is proposed for our retailing system, which guides the user to the particular product type selected by the user. The user, after entering the shopping mall, carries the smart trolley. The customer selects and purchases products using this trolley. On the basis of product purchasing, the respective databases maintained inside the femtolets are updated. An Android application for the proposed retailing is developed. We compare the power consumption and delay of the proposed retail system with the existing retail system. Simulation analyses illustrate that the proposed approach reduces power by approximately 89% and 94%, respectively, in comparison to the local cloud server-based and remote cloud server-based retail systems. Thus, we refer to the proposed system as a green retail system. The performance of the proposed system through experimental analysis is also evaluated.

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STOPPAGE: Spatio‐temporal data driven cloud‐fog‐edge computing framework for pandemic monitoring and management

Ghosh

Mukherjee²,

Ghosh

et al. 2022

Softw Pract Exp

Self Cite

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Several global health incidents and evidences show the increasing likelihood of pandemics (large‐scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data‐sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio‐temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio‐temporal data and computing infrastructure using fog/edge based architecture; and (ii) spatio‐temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic‐knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home‐health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real‐life datasets related to COVID‐19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by prefix≈$$ \approx $$(18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio‐temporal datasets and reduces the delay in predicting health status compared to the existing studies.

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Femtolet: A novel fifth generation network device for green mobile cloud computing

Cited by 26 publications

References 22 publications

STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management

STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management

E²R‐F²N: Energy‐efficient retailing using a femtolet‐based fog network

STOPPAGE: Spatio‐temporal data driven cloud‐fog‐edge computing framework for pandemic monitoring and management

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Femtolet: A novel fifth generation network device for green mobile cloud computing

Cited by 26 publications

References 22 publications

STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management

STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management

E2R‐F2N: Energy‐efficient retailing using a femtolet‐based fog network

STOPPAGE: Spatio‐temporal data driven cloud‐fog‐edge computing framework for pandemic monitoring and management

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E²R‐F²N: Energy‐efficient retailing using a femtolet‐based fog network