Data Integrity Preservation Schemes in Smart Healthcare Systems That Use Fog Computing Distribution

Alazeb, Abdulwahab; Panda, Brajendra; Almakdi, Sultan; Alshehri, Mohammed

doi:10.3390/electronics10111314

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…Alam et al [24] suggested Edge-of-Things Computation (EoTC) to reduce healthcare data processing costs. In addition, a distributed provisioning technique based on the Alternating Direction Method of Multipliers (ADMM) is described, along with a portfolio optimization strategy for choosing virtual machines (VMs) to handle healthcare data.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Health Care System in Fog Platform with Optimized Performance

Tripathy

Rath²,

Tripathy³

et al. 2023

Sustainability

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Cloud computing delivers services through the Internet and enables the deployment of a diversity of apps to provide services to many businesses. At present, the low scalability of these cloud frameworks is their primary obstacle. As a result, they are unable to satisfy the demands of centralized computer systems, which are based on the Internet of Things (IoT). Applications such as disease surveillance and tracking and monitoring systems, which are highly latency sensitive, demand the computation of the Big Data communicated to centralized databases and from databases to cloud data centers, resulting in system performance loss. Recent concepts, such as fog and edge computing, offer novel approaches to data processing by relocating the processing power and other resources closer to the end user, thereby reducing latency and maximizing energy efficiency. Existing fog models, on the other hand, have a number of limitations and tend to prioritize either the precision of their findings or a faster response time, but not both. For the purpose of applying a healthcare solution in the real world, we developed and implemented a one-of-a-kind architecture that integrates quartet deep learning with edge computing devices. The paradigm that has been developed delivers health management as a fog service through the Internet of Things (IoT) devices and efficiently organizes the data from patients based on the requirements of the user. FogBus, a fog-enabled cloud framework, is used to measure the effectiveness of the proposed structure in regards to resource usage, network throughput, congestion, precision, and runtime. To maximize the QoS or forecast the accuracy in different fog computing settings and for different user requirements, the suggested technique can be set up to run in a number of different modes.

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Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Health Care System in Fog Platform with Optimized Performance

Tripathy

Rath²,

Tripathy³

et al. 2023

Sustainability

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“…A physician treats more than 80% of patients with symptoms for which no diagnosis has been made. Only 10% of people seek help from a mental health expert [5,6]. Patients are not receiving the treatment they require, as 70% of people with depression go untreated.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Mental Stress Using Machine Learning Algorithms

Hossain¹,

Alazeb²,

Almudawi³

et al. 2022

Computers, Materials &Amp; Continua

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Depression is a crippling affliction and affects millions of individuals around the world. In general, the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts, which results in lower costs and improved patient outcomes. However, this strategy can necessitate a lot of buy-in from a large number of people, as well as additional training and logistical considerations. Thus, utilizing the machine learning algorithms, patients with depression based on information generally present in a medical file were analyzed and predicted. The methodology of this proposed study is divided into six parts: Proposed Research Architecture (PRA), Data Pre-processing Approach (DPA), Research Hypothesis Testing (RHT), Concentrated Algorithm Pipeline (CAP), Loss Optimization Stratagem (LOS), and Model Deployment Architecture (MDA). The Null Hypothesis and Alternative Hypothesis are applied to test the RHT. In addition, Ensemble Learning Approach (ELA) and Frequent Model Retraining (FMR) have been utilized for optimizing the loss function. Besides, the Features Importance Interpretation is also delineated in this research. These forecasts could help individuals connect with expert mental health specialists more quickly and easily. According to the findings, 71% of people with depression and 80% of those who do not have depression can be appropriately diagnosed. This study obtained 91% and 92% accuracy through the Random Forest (RF) and Extra Tree Classifier. But after applying the Receiver operating characteristic (ROC) curve, 79% accuracy was found on top of RF, 81% found on Extra Tree, and 82% recorded for the eXtreme Gradient Boosting (XGBoost) algorithm. Besides, several factors are identified in terms of predicting depression through statistical data analysis. Though the additional effort is needed to develop a more accurate model, this model can be adjustable in the healthcare sector for diagnosing depression.

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“…In particular, a fog computing paradigm is susceptible to different threats, such as malicious attacks and technical issues. In this context, the end-users' data traveling through fog computing nodes to the cloud is vulnerable to different violations, such as data integrity violation [13]. Therefore, it is necessary to design an approach to protect end-users' data confidentiality, availability, and integrity by ousting the rogue fog nodes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Blockchain-Based Encryption Model to Protect Fog Nodes from Behaviors of Malicious Nodes

et al. 2021

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The world has experienced a huge advancement in computing technology. People prefer outsourcing their confidential data for storage and processing in cloud computing because of the auspicious services provided by cloud service providers. As promising as this paradigm is, it creates issues, including everything from data security to time latency with data computation and delivery to end-users. In response to these challenges, the fog computing paradigm was proposed as an extension of cloud computing to overcome the time latency and communication overhead and to bring computing and storage resources close to both the ground and the end-users. However, fog computing inherits the same security and privacy challenges encountered by traditional cloud computing. This paper proposed a fine-grained data access control approach by integrating the ciphertext policy attribute-based encryption (CP-ABE) algorithm and blockchain technology to secure end-users’ data security against rogue fog nodes in case a compromised fog node is ousted. In this approach, we proposed federations of fog nodes that share the same attributes, such as services and locations. The fog federation concept minimizes the time latency and communication overhead between fog nodes and cloud servers. Furthermore, the blockchain idea and the CP-ABE algorithm integration allow for fog nodes within the same fog federation to conduct a distributed authorization process. Besides that, to address time latency and communication overhead issues, we equip each fog node with an off-chain database to store the most frequently accessed data files for a particular time, as well as an on-chain access control policies table (on-chain files tracking table) that must be protected from tampering by rogue fog nodes. As a result, the blockchain plays a critical role here because it is tamper-proof by nature. We assess our approach’s efficiency and feasibility by conducting a simulation and analyzing its security and performance.

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Data Integrity Preservation Schemes in Smart Healthcare Systems That Use Fog Computing Distribution

Cited by 4 publications

References 35 publications

An Intelligent Health Care System in Fog Platform with Optimized Performance

An Intelligent Health Care System in Fog Platform with Optimized Performance

Forecasting Mental Stress Using Machine Learning Algorithms

A Novel Blockchain-Based Encryption Model to Protect Fog Nodes from Behaviors of Malicious Nodes

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