FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments

Rajagopal, Shinu M.; Supriya, M.; Buyya, Rajkumar

doi:10.1016/j.iot.2023.100784

Cited by 49 publications

(20 citation statements)

References 42 publications

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“…F i is the quantity of pheromone or attraction that a member of the BOA population produces, and r is a random number in the range of [0, 1]. (9) As a result, as indicated in Eq (9), extract the absolute or binary values using a transition function, such as a Gaussian or V-shaped function.…”

Section: Minimize Regularized Local Lossmentioning

confidence: 99%

“…Utilizing machine learning (ML) techniques [6,7], with a variety of RA strategies have recently been investigated which reduces the wireless networks becoming increasingly complex [8]. Particularly for difficult decision-making issues, deep reinforcement learning (DRL) has been applied extensively [9]. They can be used to train a DL model with a large representation capacity to develop a RA strategy for complicated networks.…”

Section: Introductionmentioning

confidence: 99%

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Fedresource

G. Satheesh,

Sasikala

2023

Int. j. electr. comput. eng. syst. (Online)

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Deep reinforcement learning can effectively deal with resource allocation (RA) in wireless networks. However, more complex networks can have slower learning speeds, and a lack of network adaptability requires new policies to be learned for newly introduced systems. To address these issues, a novel federated learning-based resource allocation (FEDRESOURCE) has been proposed in this paper which efficiently performs RA in wireless networks. The proposed FEDRESOURCE technique uses federated learning (FL) which is a ML technique that shares the DRL-based RA model between distributed systems and a cloud server to describe a policy. The regularized local loss that occurs in the network will be reduced by using a butterfly optimization technique, which increases the convergence of the FL algorithm. The suggested FL framework speeds up policy learning and allows for adoption by employing deep learning and the optimization technique. Experiments were conducted using a Python-based simulator and detailed numerical results for the wireless RA sub-problems. The theoretical results of the novel FEDRESOURCE algorithm have been validated in terms of transmission power, convergence of algorithm, throughput, and cost. The proposed FEDRESOURCE technique achieves maximum transmit power up to 27%, 55%, and 68% energy efficiency compared to Scheduling policy, Asynchronous FL framework, and Heterogeneous computation schemes respectively. The proposed FEDRESOURCE technique can increase discrimination accuracy by 1.7%, 1.2%, and 0.78% compared to the scheduling policy framework, Asynchronous FL framework, and Heterogeneous computation schemes respectively.

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Section: Minimize Regularized Local Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fedresource

G. Satheesh,

Sasikala

2023

Int. j. electr. comput. eng. syst. (Online)

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“…Table 3 highlights a detailed breakdown of the infrastructure and device configuration values, where the busy and idle power consumption plays a crucial role in the energy consumption simulation outputs. The asterisks are not part of the value but represent the multiplication of the energy consumption of the nodes within the simulator system [16,[54][55][56]. The client application receives the data generated from sensor nodes and then transfers the data to the mobile device [34,40].…”

Section: Simulation Implementationmentioning

confidence: 99%

“…In this study, the iFogSim2 network simulation tool uses the following metrics to measure energy consumption, latency, and network bandwidth usage. The mathematical equations presented in this section represent the metrics fully described in greater detail below; referenced by [55] and summarised here in this study.…”

Section: Evaluation Of the Metrics Used In Ifogsim2mentioning

confidence: 99%

“…In a real-world data centre, energy consumption typically consists of infrastructure data communication, power supply for backup systems, HVAC, and other environmental regulatory controls such as technological security controls, emergency fire controls, and temperature regulation systems. Energy consumption is essential as it affects the operational cost of infrastructure; therefore, this study is beneficial as it highlights some of the benefits of Fog computing, such as environmental protection and the availability, efficiency, and dependability of infrastructure devices, which aims to address the high energy consumption issue in Cloud computing [55]. According to [16], iFogSim2, microservices can be introduced to simulate the full potential of edgeward computation processing, moving away from the traditional monolithic architecture in Cloud computing.…”

Section: Energy Consumptionmentioning

confidence: 99%

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Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

Lawal,

Olaniyi,

Gibson

2023

Applied Sciences

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It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices.

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Fog Computing in Healthcare

Datta,

Datta

2024

Fog Computing for Intelligent Cloud IoT Systems

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FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments

Cited by 49 publications

References 42 publications

Fedresource

Fedresource

Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

Fog Computing in Healthcare

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