Blockchain multi-objective optimization approach-enabled secure and cost-efficient scheduling for the Internet of Medical Things (IoMT) in fog-cloud system

Lakhan, Abdullah; Mohammed, Mazin Abed; Elhoseny, Mohamed; Alshehri, Mohammad Dahman; Abdulkareem, Karrar Hameed

doi:10.1007/s00500-022-07167-9

Cited by 38 publications

(16 citation statements)

References 25 publications

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“…Refs. [12,13,16,17] suggested blockchain-based offloading in the fog cloud network, as blockchain is a technology that records and validates each connected node's transactions in the distributed network. Different blockchain frameworks, such as Ethereum, Fabric, Corda, and others, have public and private types.…”

Section: Related Workmentioning

confidence: 99%

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises

Lakhan

Grønli

Majumdar

et al. 2022

Sensors

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Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client–server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.

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Section: Related Workmentioning

confidence: 99%

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises

Lakhan

Grønli

Majumdar

et al. 2022

Sensors

Self Cite

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“…The initial input data are difficult to process due to the large volume of network traffic to detect the attacks with all the features which have different forms either numeric or non-numeric data. to solve the constraint issues of non-numerical features and enhance the quality of the datasets, this paper used four approaches for preprocessing which include data cleaning, log processing, Normalization, and one-hot encoding [15].…”

Section: Data Preprocessingmentioning

confidence: 99%

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

Padmashree¹,

Krishnamoorthi²

2022

ITC

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The industrial revolution in recent years made massive uses of Internet of Things (IoT) applications like smart cities’ growth. This leads to automation in real-time applications to make human life easier. These IoT-enabled applications, technologies, and communications enhance the quality of life, quality of service, people’s well-being, and operational efficiency. The efficiency of these smart devices may harm the end-users, misuse their sensitive information increase cyber-attacks and threats. This smart city expansion is difficult due to cyber attacks. Consequently, it is needed to develop an efficient system model that can protect IoT devices from attacks and threats. To enhance product safety and security, the IoT-enabled applications should be monitored in real-time. This paper proposed an efficient feature selection with a feature fusion technique for the detection of intruders in IoT. The input IoT data is subjected to preprocessing to enhance the data. From the preprocessed data, the higher-order statistical features are selected using the proposed Decision tree-based Pearson Correlation Recursive Feature Elimination (DT-PCRFE) model. This method efficiently eliminates the redundant and uncorrelated features which will increase resource utilization and reduces the time complexity of the system. Then, the request from IoT devices is converted into word embedding using the feature fusion model to enhance the system robustness. Finally, a Deep Neural network (DNN) has been used to detect malicious attacks with the selected features. This proposed model experiments with the BoT-IoT dataset and the result shows the proposed model efficiency which outperforms other existing models with the accuracy of 99.2%.

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“…The Internet of Medical Things (IoMT) refers to the ability to connect medical devices to monitor vital medical signals and diagnose patients’ diseases [ 14 ]. Additionally, it interconnects medical devices with healthcare providers such as hospitals, private companies, or medical researchers [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…IoMT combines IoT with conventional medical equipment and expands sensing and capabilities [ 16 ]. IoMT has the ability to transmit data across a network without demanding human-to-human or human-to-computer interaction [ 14 ]. The emergence of IoMT is mainly due to the increased use and development of connected and distributed medical devices [ 15 ], as well as the emergence of many health applications.…”

Section: Introductionmentioning

confidence: 99%

“…IoMT plays an essential and vital role in remote healthcare monitoring. One of the most important types of health monitoring is related to the heart because the measured vital signs can detect numerous diseases that are hidden in nature, such as arrhythmia [ 14 ]. Due to the critical nature of healthcare-related systems, IoMT still faces various difficulties, especially with regard to reliability, safety, and security [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

Alatoun

Matrouk

Mohammed

et al. 2022

Sensors

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In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.

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Blockchain multi-objective optimization approach-enabled secure and cost-efficient scheduling for the Internet of Medical Things (IoMT) in fog-cloud system

Cited by 38 publications

References 25 publications

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises

Decision Tree with Pearson Correlation-based Recursive Feature Elimination Model for Attack Detection in IoT Environment

A Novel Low-Latency and Energy-Efficient Task Scheduling Framework for Internet of Medical Things in an Edge Fog Cloud System

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