Honey bee inspired resource allocation scheme for IoT-driven smart healthcare applications in fog-cloud paradigm

The Internet of Things (IoT) paradigm is a foundational and integral factor for the development of smart applications in different sectors. These applications are comprised over set of interconnected modules that exchange data and realize the distributed data flow (DDF) model. The execution of these modules on distant cloud data-center is prone to quality of service (QoS) degradation. This is where fog computing philosophy comes in to bridge this gap and bring the computation closer to the IoT devices. However, resource management in fog and optimal allocation of fog devices to application modules is critical for better resource utilization and achieve QoS. Significant challenge in this regard is to manage the fog network dynamically to determine cost effective placement of application modules on resources. In this study, we propose the optimal placement strategy for smart health-care application modules on fog resources. The objective of this strategy is to ensure optimal execution in terms of latency, bandwidth and earliest completion time as compared to few baseline techniques. A honey bee inspired strategy has been proposed for allocation and utilization of the resource for application module processing. In order to model the application and measure the effectiveness of our strategy, iFogSim Java-based simulation classes have been extended and conduct the experiments that demonstrate the satisfactory results.

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Publication Details

Journal
PeerJ Computer Science
Published
2024-11-19
DOI
https://doi.org/10.7717/peerj-cs.2484
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
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article

Honey bee inspired resource allocation scheme for IoT-driven smart healthcare applications in fog-cloud paradigm

Mohsin Nazir, Aasma Akram, Sajid Latif, Muhammad Zulfiqar et al.
PeerJ Computer Science
IoT and Edge/Fog Computing
article

Honey bee inspired resource allocation scheme for IoT-driven smart healthcare applications in fog-cloud paradigm

Mohsin Nazir, Aasma Akram, Sajid Latif, Muhammad Zulfiqar, Fatima Anjum
article en

Abstract

The Internet of Things (IoT) paradigm is a foundational and integral factor for the development of smart applications in different sectors. These applications are comprised over set of interconnected modules that exchange data and realize the distributed data flow (DDF) model. The execution of these modules on distant cloud data-center is prone to quality of service (QoS) degradation. This is where fog computing philosophy comes in to bridge this gap and bring the computation closer to the IoT devices. However, resource management in fog and optimal allocation of fog devices to application modules is critical for better resource utilization and achieve QoS. Significant challenge in this regard is to manage the fog network dynamically to determine cost effective placement of application modules on resources. In this study, we propose the optimal placement strategy for smart health-care application modules on fog resources. The objective of this strategy is to ensure optimal execution in terms of latency, bandwidth and earliest completion time as compared to few baseline techniques. A honey bee inspired strategy has been proposed for allocation and utilization of the resource for application module processing. In order to model the application and measure the effectiveness of our strategy, iFogSim Java-based simulation classes have been extended and conduct the experiments that demonstrate the satisfactory results.

PeerJ Computer ScienceVol. 10
Lahore College for Women University (PK), Pir Mehr Ali Shah Arid Agriculture University (PK), Nanjing University of Science and Technology (CN)
Decent work and economic growth
Openalex Percentile: Top 76%
IoT and Edge/Fog Computing
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Honey bee inspired resource allocation scheme for IoT-driven smart healthcare applications in fog-cloud paradigm — Mohsin Nazir, Aasma Akram, et al. · PeerJ Computer Science (2024) | TGRS Research Map | TGRS