Improving the Convergence Period of Adaptive Data Rate in a Long Range Wide Area Network for the Internet of Things Devices

Anwar, Khola; Rahman, Taj; Zeb, Asim; Saeed, Yousaf; Khan, Muhammad Adnan; Khan, Inayat; Ahmad, Shafiq; Abdelgawad, Abdelaty E.; Abdollahian, Mali

doi:10.3390/en14185614

Cited by 15 publications

(10 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ATS has many uses, such as short read generation, passage reduction, compaction, extracting, and the most important information from sensitive reports, including legal reports produced by legal authorities [19]. ATS can also be used in news text summarizers to assist readers in fnding the most interesting and important content in less time [20][21][22]. Other applications of ATS include sentiment summarization, legal text summarization, scientifc document summarization, tweet summarization, book summarization, story/novel summarization, e-mail summarization, and bio-medical document summarization [23].…”

Section: Text Summarizationmentioning

confidence: 99%

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

Umair

Alam

Khan

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

The extractive summarization approach involves selecting the source document’s salient sentences to build a summary. One of the most important aspects of extractive summarization is learning and modelling cross-sentence associations. Inspired by the popularity of Transformer-based Bidirectional Encoder Representations (BERT) pretrained linguistic model and graph attention network (GAT) having a sophisticated network that captures intersentence associations, this research work proposes a novel neural model N-GPETS by combining heterogeneous graph attention network with BERT model along with statistical approach using TF-IDF values for extractive summarization task. Apart from sentence nodes, N-GPETS also works with different semantic word nodes of varying granularity levels that serve as a link between sentences, improving intersentence interaction. Furthermore, proposed N-GPETS becomes more improved and feature-rich by integrating graph layer with BERT encoder at graph initialization step rather than employing other neural network encoders such as CNN or LSTM. To the best of our knowledge, this work is the first attempt to combine the BERT encoder and TF-IDF values of the entire document with a heterogeneous attention graph structure for the extractive summarization task. The empirical outcomes on benchmark news data sets CNN/DM show that the proposed model N-GPETS gets favorable results in comparison with other heterogeneous graph structures employing the BERT model and graph structures without the BERT model.

show abstract

Section: Text Summarizationmentioning

confidence: 99%

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

Umair

Alam

Khan

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nowadays, IoT-enabled application has gained the attention in diverse fields including healthcare, underwater sensor network, and body area network [ 48 ]. The performance of the proposed algorithm, namely DT-MAC, is compared with well-known algorithms using simulation experiments.…”

Section: Discussionmentioning

confidence: 99%

Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network

Zeb

Wakeel

Rahman

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Wireless sensor network is widely used in different IoT-enabled applications such as health care, underwater sensor networks, body area networks, and various offices. A sensor node may face operational difficulties due to low computing capacity. Moreover, mobility has become an open challenge in the healthcare wireless body area network that is highly affected by message loss due to topological manipulation. In this article, an enhanced version of the well-known algorithm MT-MAC is proposed, namely DT-MAC, to ensure successful message delivery. It considers node handover mechanism among virtual clusters to ensure network integrity and also uses the concept of minimum connected dominating set for network formation to achieve efficient energy utilization. It is then compared with well-known algorithms such as MT-MAC. The simulation results show that an increase in little latency of roughly 3 percent in using the proposed protocol improves the MT-MAC's packet delivery by 13–17 percent and the response time by around 15 percent. Therefore, the algorithm is best fitted for real-time applications where the high packet delivery and response time are required.

show abstract

“…The ADR suffers from a long convergence time in Figure 10 because it is unable to adjust to the fluctuating channel condition produced by the ED movement [ 37 ].…”

Section: Experimental Analysis Of the Proposed Rm-adrmentioning

confidence: 99%

RM-ADR: Resource Management Adaptive Data Rate for Mobile Application in LoRaWAN

Anwar

Rahman

Zeb

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

LoRaWAN is renowned and a mostly supported technology for the Internet of Things, using an energy-efficient Adaptive Data Rate (ADR) to allocate resources (e.g., Spreading Factor (SF)) and Transmit Power (TP) to a large number of End Devices (EDs). When these EDs are mobile, the fixed SF allocation is not efficient owing to the sudden changes caused in the link conditions between the ED and the gateway. As a result of this situation, significant packet loss occurs, increasing the retransmissions from EDs. Therefore, we propose a Resource Management ADR (RM-ADR) at both ED and Network Sides (NS) by considering the packet transmission information and received power to address this issue. Through simulation results, RM-ADR showed improved performance compared to the state-of-the-art ADR techniques. The findings indicate a faster convergence time by minimizing packet loss ratio and retransmission in a mobile LoRaWAN network environment.

show abstract

Improving the Convergence Period of Adaptive Data Rate in a Long Range Wide Area Network for the Internet of Things Devices

Cited by 15 publications

References 24 publications

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

N-GPETS: Neural Attention Graph-Based Pretrained Statistical Model for Extractive Text Summarization

Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network

RM-ADR: Resource Management Adaptive Data Rate for Mobile Application in LoRaWAN

Contact Info

Product

Resources

About