Jayaraj Thankappan¹, Delphin Raj Kesari Mary², Dong Jin Yoon³, Soo-Hyun Park^4,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1053-1079, 2023, DOI:10.32604/cmc.2023.038437

Abstract Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world. Therefore, timely and accurate decision-making is essential for mitigating flood-related damages. The traditional flood prediction techniques often encounter challenges in accuracy, timeliness, complexity in handling dynamic flood patterns and leading to substandard flood management strategies. To address these challenges, there is a need for advanced machine learning models that can effectively analyze Internet of Things (IoT)-generated flood data and provide timely and accurate flood predictions. This paper proposes a novel approach-the Adaptive Momentum and Backpropagation (AM-BP) algorithm-for flood prediction and management in… More >

R. Surendran^1,*, Youseef Alotaibi², Ahmad F. Subahi³

Computer Systems Science and Engineering, Vol.46, No.1, pp. 835-851, 2023, DOI:10.32604/csse.2023.036757

Abstract Recently, wireless sensor networks (WSNs) find their applicability in several real-time applications such as disaster management, military, surveillance, healthcare, etc. The utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and governments. Real-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor nodes. Therefore, the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the network. In this aspect, this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering (LOWGO-EAC) scheme for WSN-assisted real-time… More >

Riya Kumarasamy Santhanaraj¹, Surendran Rajendran^2,*, Carlos Andres Tavera Romero³, Sadish Sendil Murugaraj⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1561-1576, 2023, DOI:10.32604/csse.2023.029463

Abstract Wireless Sensor Networks (WSNs) are a major element of Internet of Things (IoT) networks which offer seamless sensing and wireless connectivity. Disaster management in smart cities can be considered as a safety critical application. Therefore, it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN. Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks. This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management (EAMCR-RTDM). The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration… More >

Rami Sameer Ahmad Al Kloub^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2801-2820, 2022, DOI:10.32604/cmc.2022.027847

Abstract Mainland China has a poor distribution of meteorological stations. Existing models’ estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature, and low for relative humidity and wind speed (few studies reported). This study compared the typical generalized additive model (GAM) and autoencoder-based residual neural network (hereafter, residual network for short) in terms of predicting three meteorological parameters, namely air temperature, relative humidity, and wind speed, using data from 824 monitoring stations across China’s mainland in 2015. The performance of the two models was assessed using a 10-fold cross-validation procedure. The air temperature models employ basic… More >

Ajmal Khan¹, Mubashir Mukhtar¹, Farman Ullah¹, Muhammad Bilal², Kyung-Sup Kwak^3,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1725-1746, 2021, DOI:10.32604/cmc.2021.017952

Abstract In post disaster scenarios such as war zones floods and earthquakes, the cellular communication infrastructure can be lost or severely damaged. In such emergency situations, remaining in contact with other rescue response teams in order to provide inputs for both headquarters and disaster survivors becomes very necessary. Therefore, in this research work, a design, implementation and evaluation of energy aware rapidly deployable system named EA-RDSP is proposed. The proposed research work assists the early rescue workers and victims to transmit their location information towards the remotely located servers. In EA-RDSP, two algorithms are proposed i.e., Hop count Assignment (HCA) algorithm… More >