J. Jean Justus^1,*, M. Thirunavukkarasan², K. Dhayalini³, G. Visalaxi⁴, Adel Khelifi⁵, Mohamed Elhoseny^6,7

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 801-816, 2022, DOI:10.32604/cmc.2022.019122 - 07 September 2021

Abstract Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster… More >

Sami Ullah^1,*, Nurul Hidayah Mohd Nor¹, Hanita Daud¹, Nooraini Zainuddin¹, Hadi Fanaee-T², Alamgir Khalil³

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1945-1953, 2022, DOI:10.32604/cmc.2022.019029 - 07 September 2021

Abstract Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies. The state-of-the-art method for this kind of problem is the Space-time Scan Statistics (SaTScan) which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson or Gaussian counts. Addressing this problem, an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution. However, it is based on the population counts data which are not always available in the least developed countries. In addition, the population counts are difficult to approximate for some surveillance data such… More >

Kanwal Imran^1,*, Nasreen Anjum², Abdullah Alghamdi³, Asadullah Shaikh³, Mohammed Hamdi³, Saeed Mahfooz¹

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1033-1052, 2022, DOI:10.32604/cmc.2022.018589 - 07 September 2021

Abstract IPv6 over Low Power Wireless Personal Area Network (6LoWPAN) provides IP connectivity to the highly constrained nodes in the Internet of Things (IoTs). 6LoWPAN allows nodes with limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard, thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network. The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES (Advanced Encryption Standard), but the 6LoWPAN standard lacks and has omitted the security and privacy requirements at higher… More >

J. V. Anchitaalagammai¹, T. Jayasankar^2,*, P. Selvaraj³, Mohamed Yacin Sikkandar⁴, M. Zakarya^5,6, Mohamed Elhoseny⁷, K. Shankar⁸

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1247-1261, 2022, DOI:10.32604/cmc.2022.017910 - 07 September 2021

Abstract Internet of Things (IoT) is a technological revolution that redefined communication and computation of modern era. IoT generally refers to a network of gadgets linked via wireless network and communicates via internet. Resource management, especially energy management, is a critical issue when designing IoT devices. Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment. In this point of view, the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e., EECBRM in IoT environment. The proposed EECBRM model has three stages namely, fuzzy logic-based More >

R. Kanthavel^1,*, R. Dhaya²

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 649-659, 2022, DOI:10.32604/iasc.2022.019779 - 03 September 2021

Abstract Wireless Underground Sensor Networks (WUSNs) refer to a group of nodes working underneath the earth plane that has been predicted to render concurrent observation capability in the hostile subversive and underwater surroundings. The accurate monitoring in places like underground earth, water, lubricates so on called non-conventional media need high accuracy of tiny sized sensors with antennas at a similar size. Therefore, an investigation is needed to study the opportunities and drawbacks of utilizing WUSNs without compromising the effectiveness of real-time monitoring procedures. With this, the major confrontation is to institute a trustworthy underground communication regardless… More >

Ting Wang¹, Zhiqiang Wang^1,*, Yin Yang¹, Jianhui Zhou^2,*

Journal of Renewable Materials, Vol.10, No.2, pp. 527-540, 2022, DOI:10.32604/jrm.2022.017955 - 30 August 2021

Abstract The acoustic emission (AE) technique can perform non-destructive monitoring of the internal damage development of bamboo and wood materials. In this experiment, the mechanical properties of different bamboo and wood (bamboo scrimber, bamboo plywood and SPF (Spruce-pine-fir) dimension lumber) during four-point loading tests were compared. The AE activities caused by loadings were investigated through the single parameter analysis and K-means cluster analysis. Results showed that the bending strength of bamboo scrimber was 3.6 times that of bamboo plywood and 2.7 times that of SPF dimension lumber, respectively. Due to the high strength and toughness of… More > Graphic Abstract

Chintakindi Balaram Murthy¹, Mohammad Farukh Hashmi¹, Ghulam Muhammad^2,3,*, Salman A. AlQahtani^2,3

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3015-3031, 2021, DOI:10.32604/cmc.2021.018781 - 24 August 2021

Abstract Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature… More >

Mahmood ul Hassan^1,*, Amin Al-Awady¹, Khalid Mahmood², Shahzad Ali³, Ibrahim Algamdi¹, Muhammad Kashif Saeed⁴, Safdar Zaman⁵

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3413-3427, 2021, DOI:10.32604/cmc.2021.018544 - 24 August 2021

Abstract Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving… More >

A. Sumathi^1,*, S. Meganathan¹, B. Vijila Ravisankar²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612 - 24 August 2021

Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the… More >

Vikram Rajpoot¹, Vivek Tiwari², Akash Saxena³, Prashant Chaturvedi⁴, Dharmendra Singh Rajput⁵, Mohammed Alkahtani^6,7, Mustufa Haider Abidi^7,*

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3917-3930, 2021, DOI:10.32604/cmc.2021.015826 - 24 August 2021

Abstract Wireless sensor networks (WSN) are designed to monitor the physical properties of the target area. The received signal strength (RSS) plays a significant role in reducing sensor node power consumption during data transmission. Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span. This paper introduces the RSS-based energy-efficient selective clustering technique using a master node (RESCM) to improve energy utilization using a master node. The master node positioned at the center of the network area and base station (BS) is placed More >