Assistive IoT: Deployment Scenarios and Challenges

Shahrestani, Seyed

doi:10.1007/978-3-319-60164-9_5

Cited by 12 publications

(2 citation statements)

References 46 publications

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“…There are several methods for grouping unlabeled data; for instance, Blowers and Williams' density-based spatial clustering of applications with noise clustering algorithms [14] was developed to combine regular network packets with abnormal ones. The grouping based on hierarchy [15] and the Kmeans [16] are two instances of clustering. Guided learning is covered in IDS literature as well, for instance, assistance for vectors.…”

Section: Literature Reviewmentioning

confidence: 99%

Anomaly Detection of Attempt Through Genetic Algorithm and ANN

Chavan¹,

Jagadisha²,

Mahalle³

et al. 2023

Advances in Computer Science Research

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Vehicular ad-hoc network, commonly known as VANET, is an enabling technology for supplying security and useful information in modern transport systems but subject to a multitude of attacks, ranging from auditing passively to hostile interfering. When suspicious actions are discovered, intrusion detection systems (IDS) are essential instruments for risk reduction. Additionally, by sharing interactions among their nodes, VANET vehicle collaborations improve detection accuracy. Because of this, the machine learning distribution system is efficient, scalable, and useful for developing cooperative detection methods over VANETs. Because data is exchanged between nodes during collaborative learning, privacy concerns are a basic barrier. Through the data that is observed, a rogue node may be able to obtain sensitive information about nodes other than itself. This research suggests cooperative IDS for VANETs that protects machine learning privacy. Additionally, an intrusion detection classifier is trained on the VANET and the proposed alternating multiplier direction approach is employed to solve a class of empirical risk minimization issues. In order to apply a vector approach of dual disturbance to dynamically varying privacy and provide secure network communication, the usage of privacy differential is done to capture the notation of privacy.

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Section: Literature Reviewmentioning

confidence: 99%

Anomaly Detection of Attempt Through Genetic Algorithm and ANN

Chavan¹,

Jagadisha²,

Mahalle³

et al. 2023

Advances in Computer Science Research

View full text Add to dashboard Cite

show abstract

“…Identifying and meeting the needs of people with cognitive disabilities is crucial for anyone, but it is particularly necessary for those who live alone, with the increased number of aged population around the world [ 17 ], the less support and stand from the family members, and a higher cost of formal look after of the patient. Such needs can be addressed by IoT-based wearable systems and enabling technologies and have the potential to do better far beyond them.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT‐Based Wearable Devices for Patients Suffering from Alzheimer Disease

Salehi

Gupta

Bhatia

et al. 2022

Contrast Media & Molecular Imaging

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The disorder of Alzheimer’s (AD) is defined as a gradual deterioration of cognitive functions, such as the failure of spatial cognition and short-term memory. Besides difficulties in memory, a person with this disease encounters visual processing difficulties and even awareness and identifying of their beloved ones. Nowadays, recent technologies made this possible to connect everything that exists around us on Earth through the Internet, this is what the Internet of Things (IoT) made possible which can capture and save a massive amount of data that are considered very important and useful information which then can be valuable in training of the various state-of-the-art machine and deep learning algorithms. Assistive mobile health applications and IoT-based wearable devices are helping and supporting the ongoing health screening of a patient with AD. In the early stages of AD, the wearable devices and IoT approach aim to keep AD patients mentally active in all of life’s daily activities, independent from their caregivers or any family member of the patient. These technological solutions have great potential in improving the quality of life of an AD patient as this helps to reduce pressure on healthcare and to minimize the operational cost. The purpose of this study is to explore the State-of-the-Art wearable technologies for people with AD. Significance, challenges, and limitations that arise and what will be the future of these technological solutions and their acceptance. Therefore, this study also provides the challenges and gaps in the current literature review and future directions for other researchers working in the area of developing wearable devices.

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UWB‐Based De‐densification of Spaces and Work Environments

2021

High‐Density and De‐Densified Smart Campus Communications

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Assistive IoT: Deployment Scenarios and Challenges

Cited by 12 publications

References 46 publications

Anomaly Detection of Attempt Through Genetic Algorithm and ANN

Anomaly Detection of Attempt Through Genetic Algorithm and ANN

IoT‐Based Wearable Devices for Patients Suffering from Alzheimer Disease

UWB‐Based De‐densification of Spaces and Work Environments

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