An Effective Fuzzy Logic Based Clustering Scheme for Edge-Computing Based Internet of Medical Things Systems

The disclosure of personal and private information is one of the main challenges of the Internet of Medical Things (IoMT). Most IoMT-based services, applications, and platforms follow a common architecture where wearables or other medical devices capture data that are forwarded to the cloud. In this scenario, edge computing brings new opportunities to enhance the operation of IoMT. However, despite the benefits, the inherent characteristics of edge computing require countermeasures to address the security and privacy issues that IoMT gives rise to. The restrictions of IoT devices in terms of battery, memory, hardware resources, or computing capabilities have led to a common agreement for the use of elliptic curve cryptography (ECC) with hardware or software implementations. As an example, the elliptic curve digital signature algorithm (ECDSA) is widely used by IoT devices to compute digital signatures. On the other hand, it is well known that dual signature has been an effective method to provide consumer privacy in classic e-commerce services. This article joins both approaches. It presents a novel solution to enhanced security and the preservation of data privacy in communications between IoMT devices and the cloud via edge computing devices. While data source anonymity is achieved from the cloud perspective, integrity and origin authentication of the collected data is also provided. In addition, computational requirements and complexity are kept to a minimum.

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Efficient Load Management in Multi-Access Edge Computing Using Fuzzy Logic

KIISE Transactions on Computing Practices ◽

10.5626/ktcp.2020.26.11.482 ◽

2020 ◽

Vol 26 (11) ◽

pp. 482-492

Author(s):

Md. Delowar Hossain ◽

Tangina Sultana ◽

Md. Alamgir Hossain ◽

Ga-Won Lee ◽

Eui-Nam Huh

Keyword(s):

Fuzzy Logic ◽

Edge Computing ◽

Load Management ◽

Multi Access

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Improving the Security and Confidentiality in the Internet of Medical Things Based on Edge Computing Using Clustering

Computational Intelligence and Neuroscience ◽

10.1155/2021/6509982 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Anita Hatamian ◽

Mohammad Bagher Tavakoli ◽

Masoud Moradkhani

Keyword(s):

Data Storage ◽

Large Scale ◽

Data Communication ◽

User Interaction ◽

Medical Data ◽

Edge Computing ◽

Remote Access ◽

Physiological Data ◽

The Internet ◽

Internet Of Medical Things

Families, physicians, and hospital environments use remote patient monitoring (RPM) technologies to remotely monitor a patient’s vital signs, reduce visit time, reduce hospital costs, and improve the quality of care. The Internet of Medical Things (IoMT) is provided by applications that provide remote access to patient’s physiological data. The Internet of Medical Things (IoMT) tools basically have a user interface, biosensor, and Internet connectivity. Accordingly, it is possible to record, transfer, store, and process medical data in a short time by integrating IoMT with the data communication infrastructure in edge computing. (Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. A common misconception is that edge and IoT are synonymous.) But, this approach faces problems with security and intrusion into users’ medical data that are confidential. Accordingly, this study presents a secure solution in order to be used in the IoT infrastructure in edge computing. In the proposed method, first the clustering process is performed effectively using information about the characteristics and interests of users. Then, the people in each cluster evaluated by using edge computing and people with higher scores are considered as influential people in their cluster, and since users with high user interaction can publish information on a large scale, it can be concluded that, by increasing user interaction, information can be disseminated on a larger scale without any intrusion and thus in a safe way in the network. In the proposed method, the average of user interactions and user scores are used as a criterion for identifying influential people in each cluster. If there is a desired number of people who are considered to start disseminating information, it is possible to select people in each cluster with a higher degree of influence to start disseminating information. According to the research results, the accuracy has increased by 0.2 and more information is published in the proposed method than the previous methods.

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A Multi-Message Multi-Receiver Signcryption Scheme with Edge Computing for Secure and Reliable Wireless Internet of Medical Things Communications

Sustainability ◽

10.3390/su132313184 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13184

Author(s):

Insaf Ullah ◽

Muhammad Asghar Khan ◽

Ali Alkhalifah ◽

Rosdiadee Nordin ◽

Mohammed H. Alsharif ◽

...

Keyword(s):

Public Key Cryptography ◽

Healthcare Services ◽

Edge Computing ◽

Security And Privacy ◽

Wireless Internet ◽

Cost Study ◽

Biomedical Sensors ◽

Related Data ◽

Internet Of Medical Things ◽

Signcryption Scheme

Thanks to recent advancements in biomedical sensors, wireless networking technologies, and information networks, traditional healthcare methods are evolving into a new healthcare infrastructure known as the Internet of Medical Things (IoMT). It enables patients in remote areas to obtain preventative or proactive healthcare services at a cheaper cost through the ease of time-independent interaction. Despite the many benefits of IoMT, the ubiquitously linked devices offer significant security and privacy concerns for patient data. In the literature, several multi-message and multi-receiver signcryption schemes have been proposed that use traditional public-key cryptography, identity-based cryptography, or certificateless cryptography methods to securely transfer patient health-related data from a variety of biomedical sensors to healthcare professionals. However, certificate management, key escrow, and key distribution are all complications with these methods. Furthermore, in terms of IoMT performance and privacy requirements, they are impractical. This article aims to include edge computing into an IoMT with secure deployment employing a multi-message and multi-receiver signcryption scheme to address these issues. In the proposed method, certificate-based signcryption and hyperelliptic curve cryptography (HECC) have been coupled for excellent performance and security. The cost study confirms that the proposed scheme is better than the existing schemes in terms of computational and communication costs.

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Context-based Reasoning through Fuzzy Logic for Edge Intelligence

Journal of Ubiquitous Systems and Pervasive Networks ◽

10.5383/juspn.15.01.003 ◽

2021 ◽

Vol 15 (01) ◽

pp. 17-25

Author(s):

Ramin Firouzi ◽

Rahim Rahmani ◽

Theo Kanter

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Edge Computing ◽

Learning System ◽

Process Data ◽

Real Time Processing ◽

Low Level ◽

Context Reasoning ◽

Novel Approach ◽

Rich Data

With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

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An Effective Training Scheme for Deep Neural Network in Edge Computing Enabled Internet of Medical Things (IoMT) Systems

IEEE Access ◽

10.1109/access.2020.3000322 ◽

2020 ◽

Vol 8 ◽

pp. 107112-107123 ◽

Cited By ~ 10

Author(s):

Irina Valeryevna Pustokhina ◽

Denis Alexandrovich Pustokhin ◽

Deepak Gupta ◽

Ashish Khanna ◽

K. Shankar ◽

...

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Edge Computing ◽

Training Scheme ◽

Effective Training ◽

Internet Of Medical Things

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Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing

Applied Sciences ◽

10.3390/app10093115 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3115

Author(s):

Md Delowar Hossain ◽

Tangina Sultana ◽

VanDung Nguyen ◽

Waqas ur Rahman ◽

Tri D. T. Nguyen ◽

...

Keyword(s):

Fuzzy Logic ◽

Failure Rate ◽

Small Cell ◽

Edge Computing ◽

Small Cell Networks ◽

Task Failure ◽

Collaborative Task ◽

Multi Access ◽

Cell Networks ◽

Task Offloading

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

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