Angle Adjustment for Vertical and Diagonal Communication in Underwater Sensor

Anum, Aimen; Ali, Tariq; Akbar, Shuja; Obaid, Iqra; Junaid, Muhammad; Draz, Umar; Shaheen, Momina

doi:10.14569/ijacsa.2020.0110175

Cited by 2 publications

(1 citation statement)

References 39 publications

(65 reference statements)

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“…erefore, there is an imperative need of a suitable and operationally effective disaster management system to alleviate the issues that arise from such disasters. Over the last decade, several investigations have been performed to overcome these concerns using UAV [20,32], sensor networks [144], and SAGIN [56]. However, remote areas where ground networks are unable to reach can be responsible for more issues related to the provision of emergency response.…”

Section: Discussionmentioning

confidence: 99%

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

Anjum

Anees

Tariq

et al. 2023

Applied Computational Intelligence and Soft Computing

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The occurrence of any kind of natural disaster will eventually lead to the loss of life and property. Countries where such disasters occur make every effort to monitor such disasters and aid as quickly as possible. However, in some cases, a rescue cannot be sent because no information is available to initiate any type of rescue operation. This is usually because common disaster management systems (DMS) use on board or ground networks to route information from the disaster scene to rescue headquarters (HQ), which in most cases cannot provide the information efficiently. One effective approach is to use satellites in conjunction with existing air-to-ground systems. This study provides a comprehensive and systematic overview of the complexities of the space-air-ground integrated network (SAGIN) in disaster management applications, including different architectures and protocols. The main rationale behind this review is to provide an extensive analysis of existing disaster management systems that are making use of SAGIN. This paper also presents the taxonomy for disaster management systems and challenges. Moreover, this research work also highlights open research issues and challenges for any type of disaster scenario. Our results indicate that several challenges are faced by disaster management systems such as hardware-based challenges, network-based characteristics and communication protocols related challenges, availability and accuracy of imagery data, and security and privacy issues.

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Section: Discussionmentioning

confidence: 99%

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

Anjum

Anees

Tariq

et al. 2023

Applied Computational Intelligence and Soft Computing

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Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model

Dalal,

Lilhore,

Faujdar

et al. 2024

IET Smart Cities

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In smart cities, air pollution is a critical issue that affects individual health and harms the environment. The air pollution prediction can supply important information to all relevant parties to take appropriate initiatives. Air quality prediction is a hot area of research. The existing research encounters several challenges that is, poor accuracy and incorrect real‐time updates. This research presents a hybrid model based on long‐short term memory (LSTM), recurrent neural network (RNN), and Curiosity‐based Motivation method. The proposed model extracts a feature set from the training dataset using an RNN layer and achieves sequencing learning by applying an LSTM layer. Also, to deal with the overfitting issues in LSTM, the proposed model utilises a dropout strategy. In the proposed model, input and recurrent connections can be dropped from activation and weight updates using the dropout regularisation approach, and it utilises a Curiosity‐based Motivation model to construct a novel motivational model, which helps in the reconstruction of long short‐term memory recurrent neural network. To minimise the prediction error, particle swarm optimisation is implemented to optimise the LSTM neural network's weights. The authors utilise an online Air Pollution Monitoring dataset from Salt Lake City, USA with five air quality indicators for comparison, that is, SO2, CO, O3, and NO2, to predict air quality. The proposed model is compared with existing Gradient Boosted Tree Regression, Existing LSTM, and Support Vector Machine based Regression Model. Experimental analysis shows that the proposed method has 0.0184 (Root Mean Square Error (RMSE)), 0.0082 (Mean Absolute Error), 2002*109 (Mean Absolute Percentage Error), and 0.122 (R2‐Score). The experimental findings demonstrate that the proposed LSTM model had RMSE performance in the prescribed dataset and statistically significant superior outcomes compared to existing methods.

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Angle Adjustment for Vertical and Diagonal Communication in Underwater Sensor

Cited by 2 publications

References 39 publications

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

Space-Air-Ground Integrated Network for Disaster Management: Systematic Literature Review

Optimising air quality prediction in smart cities with hybrid particle swarm optimization‐long‐short term memory‐recurrent neural network model

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