Energy-aware and adaptive fog storage mechanism with data replication ruled by spatio-temporal content popularity

Vales, Ruben; Moura, José; Marinheiro, Rui

doi:10.1016/j.jnca.2019.03.001

Cited by 20 publications

(9 citation statements)

References 55 publications

(68 reference statements)

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“…Spatio‐fog framework 28 proposes an energy‐efficient and delay‐aware fog computing model for processing geospatial query. Energy‐aware popular spatio‐temporal data storage in fog devices is described in Reference 50. However, this work does not consider the mobility aspect of the users, which is an important factor in provisioning QoS‐aware services.…”

Section: Related Workmentioning

confidence: 99%

RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments

Das

Ghosh

Mukherjee³

et al. 2022

Softw Pract Exp

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Internet of Things (IoT) has a pivotal role in developing intelligent and computational solutions to facilitate varied real-life applications. To execute high-end computations and data analytics, IoT and cloud-based solutions play the most significant role. However, frequent communication with long distant cloud servers is not a delay-aware and energy-efficient solution while providing time-critical applications such as healthcare. This article explores the possibilities and opportunities of integrating cloud technology with fog and edge-based computing to provide healthcare services to users in exigency. Here, we propose an end-to-end framework named RESCUE (enabling green healthcare services using integrated iot-edge-fog-cloud computing environments), consisting efficient spatio-temporal data analytics module for efficient information sharing, spatio-temporal data analysis to predict the path for users to reach the destination (healthcare center or relief camps) with minimum delay in the time of exigency (say, natural disaster). This module analyzes the collected information through crowd-sourcing and assists the user by extracting optimal path postdisaster when many regions are nonreachable. Our work is different from the existing literature in varied aspects: it analyses the context and semantics by augmenting real-time volunteered geographical information (VGI) and refines it. Furthermore, the novel path prediction module incorporates such VGI instances and predicts routes in emergencies avoiding all possible risks. Also, the design of development of a latency-aware, power-aware data-driven analytics system helps to resolve any spatio-temporal query more efficiently compared to the existing works for any time-critical application. The experimental and simulation results outperform the baselines in terms of accuracy, delay, and power consumption. K E Y W O R D Scloud computing, edge computing, geospatial query processing, green computing, healthcare service, internet of things, spatio-temporal data Jaydeep Das and Shreya Ghosh contributed equally to this study.

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Section: Related Workmentioning

confidence: 99%

RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments

Das

Ghosh

Mukherjee³

et al. 2022

Softw Pract Exp

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“…To manage DRP on fog clusters in fog infrastructure, Vales et al 10 proposed a hybrid DRP system based on an adaptive distance measure. The adaptive distance is determined by three factors: spatiotemporal data prominence, consumer‐to‐data distance, and self‐ruled battery‐powered node.…”

Section: Related Workmentioning

confidence: 99%

A metaheuristic‐based data replica placement approach for data‐intensive IoT applications in the fog computing environment

2021

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Over the past few years, Internet of Things (IoT) applications have grown rapidly.The data-intensive IoT applications that take advantage of cloud servers for computations and data storage will result in higher latency and other network traffic in the Internet core. IoT applications are characterized by their sensitivity to latency. As an example, delays will result in irreparable damage in the medical and healthcare industries. Cloud servers are no longer necessary because cloud computing utilizes fog nodes that are closer to users. Nodes with different hardware capabilities pose a significant challenge since they differ significantly in latency and traffic reduction. This article presented a metaheuristic-based method using the non-dominated sorting genetic algorithm II for data-intensive IoT applications in fog infrastructure. Besides, we provide a new automatic method for managing data replica transmissions, including deploying them in a fog cloud environment. The proposed solution was evaluated in the iFogSim simulator and compared with two other data replica placement methods in different scenarios. The results showed a decrease in latency and cost for data access and an increase in data availability.

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“…Vales et al [71] propose an energy aware and adaptive distance-based data replication decision policy. The decision is taken based on data popularity, distance to the consumer node from the data node and battery of the node.…”

Section: B Data Managementmentioning

confidence: 99%

Fog Computing Algorithms: A Survey and Research Opportunities

Malukani

Bhensdadia

2021

Applied Computer Systems

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The classic Internet of Things-Cloud Computing model faces challenges like high response latency, high bandwidth consumption, and high storage requirement with increasing velocity and volume of generated data. Fog computing offers better services to end users by bringing processing, storage, and networking closer to them. Recently, there has been significant research addressing architectural and algorithmic aspects of fog computing. In the existing literature, a systematic study of architectural designs is widely conducted for various applications. Algorithms are seldom examined. Algorithms play a crucial role in fog computing. This survey aims to performing a comparative study of existing algorithms. The study also presents a systematic classification of the current fog computing algorithms and highlights the key challenges and research issues associated with them.

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Energy-aware and adaptive fog storage mechanism with data replication ruled by spatio-temporal content popularity

Abstract: Energy-aware and adaptive fog storage mechanism with data replication ruled by spatio-temporal content popularity. Journal of Network and Computer Applications.

Cited by 20 publications

References 55 publications

RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments

RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments

A metaheuristic‐based data replica placement approach for data‐intensive IoT applications in the fog computing environment

Fog Computing Algorithms: A Survey and Research Opportunities

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