Wearable Sensors and Systems in the IoT

Mukhopadhyay, Subhas Chandra; Suryadevara, Nagender Kumar; Nag, Anindya

doi:10.3390/s21237880

Cited by 14 publications

(6 citation statements)

References 26 publications

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“…The implementation of AI techniques in wearable sensor data-handling can be performed with two different computing paradigms [ 94 ].…”

Section: Data Analysis For Healthcarementioning

confidence: 99%

“…They can help in sending data to the monitoring unit with an adequate data transfer rate and size. The most recent phenomenon is the use of neural networks, where different kinds of functional networks are being employed in addition to the models for comparing their performances [ 94 ]. The use of these techniques circumvents the energy-harvesting issue to a great extent while maintaining a high quality of service for the attached sensors and actuators.…”

Section: Power Supplies For Wearable Healthcare System: Energy Harves...mentioning

confidence: 99%

“…Different kinds of prototypes are being developed that can be worn ubiquitously and at fixed intervals. These sensors are integrated with electronic conditioning circuits to form devices that can be used to determine the health information of a person [ 94 ]. In the current era of 3D printing technology, flexible sensors are being developed that can exhibit various exciting characteristics, such as being low cost and lightweight, and having high mechanical flexibility and high stretchability [ 95 , 96 , 97 ].…”

Section: Introductionmentioning

confidence: 99%

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Wearable Sensors for Healthcare: Fabrication to Application

Mukhopadhyay

Suryadevara

Nag

2022

Sensors

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This paper presents a substantial review of the deployment of wearable sensors for healthcare applications. Wearable sensors hold a pivotal position in the microelectronics industry due to their role in monitoring physiological movements and signals. Sensors designed and developed using a wide range of fabrication techniques have been integrated with communication modules for transceiving signals. This paper highlights the entire chronology of wearable sensors in the biomedical sector, starting from their fabrication in a controlled environment to their integration with signal-conditioning circuits for application purposes. It also highlights sensing products that are currently available on the market for a comparative study of their performances. The conjugation of the sensing prototypes with the Internet of Things (IoT) for forming fully functioning sensorized systems is also shown here. Finally, some of the challenges existing within the current wearable systems are shown, along with possible remedies.

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“…The implementation of AI techniques in wearable sensor data-handling can be performed with two different computing paradigms [ 94 ].…”

Section: Data Analysis For Healthcarementioning

confidence: 99%

Section: Power Supplies For Wearable Healthcare System: Energy Harves...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wearable Sensors for Healthcare: Fabrication to Application

Mukhopadhyay

Suryadevara

Nag

2022

Sensors

Self Cite

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“…As IoT networks become increasingly important, having a dataset that can be used for this purpose will be a valuable contribution. By 2025, it is anticipated that the global market for IoT end-user solutions will reach approximately $1.6 trillion, posing a new growing danger from exposed telnet ports, out-of-date Linux firmware, and numerous other related vulnerabilities [7]. The expansion of these insecure IoT devices opens up wide varieties of new cybersecurity attack factors, most notably IoT Botnet attacks.…”

Section: Introductionmentioning

confidence: 99%

Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms

2023

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The Internet of Things (IoT) shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones. Contrarily, storing and operating information and maintaining its confidentiality and security is a concerning issue in the IoT. Throughout the whole operational process, considering transparency in its privacy, data protection, and disaster recovery, it needs state-of-the-art systems and methods to tackle the evolving environment. This research aims to improve the security of IoT devices by investigating the likelihood of network attacks utilizing ordinary device network data and attack network data acquired from similar statistics. To achieve this, IoT devices dedicated to smart healthcare systems were utilized, and botnet attacks were conducted on them for data generation. The collected data were then analyzed using statistical measures, such as the Pearson coefficient and entropy, to extract relevant features. Machine learning algorithms were implemented to categorize normal and attack traffic with data preprocessing techniques to increase accuracy. One of the most popular datasets, known as BoT-IoT, was cross-evaluated with the generated dataset for authentication of the generated dataset. The research provides insight into the architecture of IoT devices, the behavior of normal and attack networks on these devices, and the prospects of machine learning approaches to improve IoT device security. Overall, the study adds to the growing body of knowledge on IoT device security and emphasizes the significance of adopting sophisticated strategies for detecting and mitigating network attacks.

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“…MANETs are often deployed in a range of domains, such as disaster rescue, graphics and healthcare and defensive systems [1]. A maximum QoS is required for MANETs because they endure external and internal problems such as connection failure, topology changes and energy depletion [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Topology Change Aware on Demand Routing Protocol for Improving Reliability and Stability of MANET

2022

IJIES

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In mobile adhoc networks (MANETs), the most essential problem is to ensure the quality-of-service (QoS) during data transmission via multiple paths from a source to the destination node. To tackle this problem, a topological change adaptive adhoc on-demand multipath distance vector (TA-AOMDV) routing protocol was developed, which ensures QoS according to the different parameters for high-speed node mobility. But, it did not consider the path stability related to the node density, which also affects the routing efficiency. Hence, this paper develops a reliable and stable TA-AOMDV (RSTA-AOMDV) routing protocol that applies a new forwarding strategy to enhance the path reliability and stability for data transmission. In this protocol, every origin node relays data packets to the target in a hop-by-hop manner depending on local information gathered from its one-hop adjacent during the forwarding phase. Also, two different metrics are considered: (i) destination region selection (DRS) and (ii) weighted closeness and connectivity (WCC) to find the stable path from the source to the destination node. During the adjacent finding phase, a forwarding node self-selection mechanism is introduced to reduce the communication overhead due to the high node density and severe congestion. Finally, this protocol is simulated for 2 different cases: (1) a varying node mobility and (2) varying number of nodes. In the case of high-speed node mobility, i.e. 50m/s, the RSTA-AOMDV protocol achieves a 50.7 % packet delivery ratio (PDR), 109.4ms end-toend delay (E2E-D), 158.51Kbps throughput, 73.4 % normalized routing overhead (NRO) and 11J mean energy consumption (MEC) compared to other existing protocols. Similarly, in the case of high node densities, i.e. when the number of nodes is 100, the RSTA-AOMDV protocol achieves a 66.5 % PDR, 1485.74ms mean E2E-D, 639.7Kbps throughput, 15 % NRO and 22.7J MEC compared to the existing protocols.

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Wearable Sensors and Systems in the IoT

Abstract: Wearable smart devices are widely used to determine various physico-mechanical parameters at chosen intervals. The proliferation of such devices has been driven by the acceptance of enhanced technology in society [...]

Cited by 14 publications

References 26 publications

Wearable Sensors for Healthcare: Fabrication to Application

Wearable Sensors for Healthcare: Fabrication to Application

Enhancing IoT Device Security through Network Attack Data Analysis Using Machine Learning Algorithms

Topology Change Aware on Demand Routing Protocol for Improving Reliability and Stability of MANET

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