Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

Hassan, Syed Rizwan; Ahmad, İshtiaq; Nebhen, Jamel; Rehman, Ateeq Ur; Shafiq, Muhammad; Choi, Jihye

doi:10.32604/cmc.2022.020428

Cited by 10 publications

(1 citation statement)

References 24 publications

(29 reference statements)

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“…A task assignment approach that allocates modules according to processing resources available at the network devices present in the system is presented in [22]. A module allocation approach for heterogeneous fog-cloud computing environments is presented in [23]. The presented algorithm efficiently allocates the application modules to the fog nodes while con-sidering connection latency, computational power, and volume of sensed information.…”

Section: Related Workmentioning

confidence: 99%

Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments

Hassan

Ahmad

Rehman

et al. 2022

Wireless Communications and Mobile Computing

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The architecture employed by most of the researchers for the deployment of latency-sensitive Internet of Things (IoT) applications is fog computing. Fog computing architecture offers less delay as compared to the cloud computing paradigm by providing resource constraint fog devices close to the edge of the network. Fog nodes process the incoming data by utilizing available resources which reduces the volume of data to be sent to the cloud server. Fog devices having dissimilar processing capabilities are present in a system. The connection of suitable sensor nodes to the parent fog node plays an essential role in achieving the optimum performance of the system. In this paper, we have designed an algorithm that dynamically assigns appropriate sensor devices to fog nodes to achieve a reduction in network utilization and latency. The proposed algorithm estimates the volume of information detected by an edge device from the rate of sensing frequency of the sensor attached to the edge device. The proposed policy while connecting the network nodes takes into account the heterogeneity and processing capability of the devices. Several evaluations are performed on multiple scales for the evaluation of the proposed algorithm. The outcomes of the evaluations confirm the effectiveness of the proposed algorithm in achieving a reduction in network consumption and end-to-end delay.

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Section: Related Workmentioning

confidence: 99%

Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments

Hassan

Ahmad

Rehman

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

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Uncovering Effective Roles and Tasks for Fog Systems

Blume,

Lins,

Sunyaev

2023

Service-Oriented and Cloud Computing

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An energy-aware module placement strategy in fog-based healthcare monitoring systems

Hossam,

Abdel-Galil,

Belal

2024

Cluster Comput

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Fog computing and the Internet of Things (IoT) have revolutionized healthcare monitoring systems, enabling real-time health data collection and transmission while overcoming cloud computing limitations. However, efficiently selecting fog nodes for application modules with varying deadline requirements and ensuring adherence to quality of service (QoS) criteria pose significant challenges due to resource constraints and device limitations. In this paper, we present a novel two-layered hierarchical design for fog devices, leveraging cluster aggregation to optimize the selection of fog nodes for healthcare applications. We introduce three efficient algorithms to minimize system latency and reduce energy consumption in fog computing environments. Our proposed model is rigorously evaluated using the iFogSim toolkit and compared with cloud-based and latency-aware model [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology.19, 2018, 10.1145/3186592]. In four distinct network topologies, our model exhibits an average latency reduction of at least 87% and energy consumption reduction of at least 76% when compared to the Cloud-based model. Similarly, when compared to the Latency-aware model proposed in [Mahmud R, Ramamohanarao K, Buyya R in ACM Transactions on Internet Technology. 19, 2018, 10.1145/3186592], our model showcases a minimum reduction of 43% in average latency and 27% in energy consumption. Our contribution lies in addressing the complexity of selecting fog nodes for application modules with diverse deadline requirements, while ensuring QoS. This work advances the field of real-time healthcare monitoring systems, promising substantial improvements in efficiency and effectiveness.

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Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks

Cited by 10 publications

References 24 publications

Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments

Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments

Uncovering Effective Roles and Tasks for Fog Systems

An energy-aware module placement strategy in fog-based healthcare monitoring systems

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