A dynamic access-point transmission power minimization method using PI feedback control in elastic WLAN system for IoT applications

Islam, Md. Manowarul; Funabiki, Nobuo; Sudibyo, Rahardhita Widyatra; Munene, Kwenga Ismael; Kao, Wen-Chung

doi:10.1016/j.iot.2019.100089

Cited by 16 publications

(4 citation statements)

References 12 publications

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“…In this section, we compare the optimal UAV location and transmit time allocations by proposed algorithms with those from an exhaustive search, and demonstrate that the UDTA algorithm achieves optimality while significantly reducing computational complexity. For simulations, we assume that

= 30 [dBm],

= 25 [dBm],

, and

= 20 [dB] [ 25 , 26 , 27 , 28 ]. An urban environment is assumed with

and

[ 8 ].…”

Section: Numerical Resultsmentioning

confidence: 99%

Optimal UAV Deployment and Resource Management in UAV Relay Networks

Han

Baek

2021

Sensors

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UAV equipped three-dimensional (3D) wireless networks can provide a solution for the requirements of 5G communications, such as enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC). Especially, the introduction of an unmanned aerial vehicle (UAV) as a relay node can improve the connectivity, extend the terrestrial base station (BS) coverage and enhance the throughput by taking advantage of a strong air-to-ground line of sight (LOS) channel. In this paper, we consider the deployment and resource allocation of UAV relay network (URN) to maximize the throughput of user equipment (UE) within a cell, while guaranteeing a reliable transmission to UE outside the coverage of BS. To this end, we formulate joint UAV deployment and resource allocation problems, whose analytical solutions can be hardly obtained, in general. We propose a fast and practical algorithm to provide the optimal solution for the number of transmit time slots and the UAV relay location in a sequential manner. The transmit power at BS and UAV is determined in advance based on the availability of channel state information (CSI). Simulation results demonstrate that the proposed algorithms can significantly reduce the computational effort and complexity to determine the optimal UAV location and transmit time slots over an exhaustive search.

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= 30 [dBm],

= 25 [dBm],

, and

= 20 [dB] [ 25 , 26 , 27 , 28 ]. An urban environment is assumed with

and

[ 8 ].…”

Section: Numerical Resultsmentioning

confidence: 99%

Optimal UAV Deployment and Resource Management in UAV Relay Networks

Han

Baek

2021

Sensors

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show abstract

“…These different types of access methods result in incompatibility issues and devices cannot efficiently communicate with each other [8]. Solving this heterogeneous nature of IoT devices [9] is the biggest challenge researchers are trying to solve.…”

Section: Heterogeneous Technologies Used In Iotmentioning

confidence: 99%

Implementation and Evaluation of IPv6 with Compression and Fragmentation for Throughput Improvement of Internet of Things Networks over IEEE 802.15.4

Haggag

2023

Wireless Pers Commun

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There is a large number of connected devices in the Internet of Things (IoT) networks that are typically several orders of magnitude bigger than enterprise networks and campus networks. The exponential increase in the number of interconnected smart devices is expected to exceed 60 billion smart objects shortly. The requirements needed for IoT networks are scalability, low power consumption, and simplified routing and security protocols. IoT networks are also heterogeneous, composing different types of networks together. Legacy network protocols like IPv4 has deemed to be inefficient for IoT networks. As the number of IPv4 addresses is almost consumed with regular network devices, we propose the use of IPv6 addressing for IoT-connected devices as IPv4 cannot accommodate the scalability requirement of IoT. IPv6 provides extended address space and enhanced mobility which are very essential for IoT networks. In this research, we apply IPv6 to IoT networks to avoid the scalability bottleneck of the IPv4 subnet. IPv6 accommodates a large number of connected devices and solves issues resulting from the heterogeneous nature and access methods of IoT devices. However, IPv6 is a large protocol that does not suit itself well in the IoT world. The Maximum Transmission Unit (MTU) permitted for IEEE 802.15.4 MAC data frames with their encapsulated IPv6 packet is limited to 127 bytes. We need 40 bytes for the uncompressed IPv6 header and 8 bytes are needed for the uncompressed UDP header. As a result, there are either 54 bytes left for the payload when security is not considered or 33 bytes when security is considered. We investigate throughput improvement for IoT networks by applying adaptation to IPv6 through header compression with UDP header compression. We also apply fragmentation for MAC frames that exceed the 127 bytes MTU limit. Simulation results showed that IPv6 compression with or without fragmentation serves toward adapting IPv6 packets to IoT networks. In the case of applying fragmentation, the technique of fragment forwarding greatly enhances the performance. Network traffic within the same network can be compressed to 2 bytes. On leaving the 802.15.4 network, the header increases to 12 bytes if the network prefix is known or to 20 bytes if the network prefix is unknown. Two evaluation metrics, namely, Compression Gain and Packet Delivery Ratio were applied to our proposed implementation method to prove the validity and the efficiency of our proposal.

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“…Convolutional Neural Networks (CNNs) can efficiently reduce computation time by using the advantage of the graphics processing unit (GPU) for computation. Several fields, that is, Medical, Agriculture, and Communication, are being developed and managed by utilizing DL techniques [9][10][11][12]. Researchers employ imaging techniques like MRI scans using deep learning to classify Alzheimer's disease and aid in the hunt for better treatments.…”

Section: Introductionmentioning

confidence: 99%

A robust and clinically applicable deep learning model for early detection of Alzheimer's

Rana,

Islam,

Talukder

et al. 2023

IET Image Processing

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Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score.

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A dynamic access-point transmission power minimization method using PI feedback control in elastic WLAN system for IoT applications

Cited by 16 publications

References 12 publications

Optimal UAV Deployment and Resource Management in UAV Relay Networks

Optimal UAV Deployment and Resource Management in UAV Relay Networks

Implementation and Evaluation of IPv6 with Compression and Fragmentation for Throughput Improvement of Internet of Things Networks over IEEE 802.15.4

A robust and clinically applicable deep learning model for early detection of Alzheimer's

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