Abstract
In deploying the Internet of Things (IoT) and Internet of Medical Things (IoMT)-based applications and infrastructures, the researchers faced many sensors and their output’s values, which have transferred between service requesters and servers. Some case studies addressed the different methods and technologies, including machine learning algorithms, deep learning accelerators, Processing-In-Memory (PIM), and neuromorphic computing (NC) approaches to support the data processing complexity and communication between IoMT nodes. With inspiring human brain structure, some researchers tackled the challenges of rising IoT- and IoMT-based applications and neural structures’ simulation. A defective device has destructive effects on the performance and cost of the applications, and their detection is challenging for a communication infrastructure with many devices. We inspired astrocyte cells to map the flow (AFM) of the Internet of Medical Things onto mesh network processing elements (PEs), and detect the defective devices based on a phagocytosis model. This study focuses on an astrocyte’s cholesterol distribution into neurons and presents an algorithm that utilizes its pattern to distribute IoMT’s dataflow and detect the defective devices. We researched Alzheimer’s symptoms to understand astrocyte and phagocytosis functions against the disease and employ the vaccination COVID-19 dataset to define a set of task graphs. The study improves total runtime and energy by approximately 60.85% and 52.38% after implementing AFM, compared with before astrocyte-flow mapping, which helps IoMT’s infrastructure developers to provide healthcare services to the requesters with minimal cost and high accuracy.
Highlights
This article is an open access articleThe complexity of the relationship between service providers and requesters and balancing the flow between them is challenging for Internet of Things (IoT)- and IoMT-based infrastructures.When sharing a patient’s private information on the Internet of Medical Things, intruders may be able to access their records, leading to security and confidentiality risks
The network size depends on the total number of tasks, which are mapped onto the nodes based on the pattern of the relationship between astrocyte, neuron, and dendrite
The Internet of Medical Things-infrastructures supports a huge number of sensors, patients, different healthcare service providers that are led to increase the complexity of the relationship between them and their computational operations
Summary
This article is an open access articleThe complexity of the relationship between service providers and requesters and balancing the flow between them is challenging for IoT- and IoMT-based infrastructures.When sharing a patient’s private information on the Internet of Medical Things, intruders may be able to access their records, leading to security and confidentiality risks. Some studies presented different approaches and protocols to follow doctor authentication, client privacy, and creating security purposes for the various IoMT-based applications [1,2,3]. The researchers aimed their case studies on dependability and availability and provided different fault detection methods to protect people’s confidential information and improve the quality of service. Some studies inspired the human brain’s neural cells to present the different approaches for improving the performance and cost of the IoT and IoMT applications in facing the increasing complexity of their computational operations and the relationship between their components and layers [4,5,6]. The researcher simulated the body language (such as hand sign language) through the neuromorphic computing that was inspired by the human neural structures [16]
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