Samsul Huda^1,*, Yasuyuki Nogami², Maya Rahayu², Takuma Akada², Md. Biplob Hossain², Muhammad Bisri Musthafa², Yang Jie², Le Hoang Anh²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3165-3187, 2024, DOI:10.32604/cmc.2024.058144 - 18 November 2024

Abstract Global food security is a pressing issue that affects the stability and well-being of communities worldwide. While existing Internet of Things (IoT) enabled plant monitoring systems have made significant strides in agricultural monitoring, they often face limitations such as high power consumption, restricted mobility, complex deployment requirements, and inadequate security measures for data access. This paper introduces an enhanced IoT application for agricultural monitoring systems that address these critical shortcomings. Our system strategically combines power efficiency, portability, and secure access capabilities, assisting farmers in monitoring and tracking crop environmental conditions. The proposed system includes a… More >

Wen-Tsai Sung¹, Indra Griha Tofik Isa^1,2, Sung-Jung Hsiao^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3733-3759, 2024, DOI:10.32604/cmc.2024.053634 - 12 September 2024

Abstract Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income. The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity. In this study, a Wireless Sensor Networks (“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning (DRL) technology in carrying out prediction tasks based on three classifications: “optimal,” “sub-optimal,” or “not-optimal” conditions based on three parameters including humidity, temperature, and soil moisture. The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.… More >

Sangeeta Yadav¹, Preeti Gulia^1,*, Nasib Singh Gill^1,*, Piyush Kumar Shukla², Arfat Ahmad Khan³, Sultan Alharby⁴, Ahmed Alhussen⁴, Mohd Anul Haq⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1253-1274, 2024, DOI:10.32604/cmc.2023.042869 - 30 January 2024

Abstract Inpatient falls from beds in hospitals are a common problem. Such falls may result in severe injuries. This problem can be addressed by continuous monitoring of patients using cameras. Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient. Along with fall detection, monitoring of different activities of the patients is also of significant concern to assess the improvement in their health. High computation-intensive models are required to monitor every action of the patient precisely. This requirement limits the applicability of such networks. Hence, to keep the… More >

Hayder Makki Shakir, Jaber Karimpour^*, Jafar Razmara

Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545 - 26 January 2024

Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource… More >

Mingyu Lee¹, Jiho Shin², Jung Taek Seo^3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2427-2442, 2023, DOI:10.32604/cmc.2023.041774 - 29 November 2023

Abstract Cyberattacks targeting industrial control systems (ICS) are becoming more sophisticated and advanced than in the past. A programmable logic controller (PLC), a core component of ICS, controls and monitors sensors and actuators in the field. However, PLC has memory attack threats such as program injection and manipulation, which has long been a major target for attackers, and it is important to detect these attacks for ICS security. To detect PLC memory attacks, a security system is required to acquire and monitor PLC memory directly. In addition, the performance impact of the security system on the… More >

Kenedy A. Greyson^*

Journal on Internet of Things, Vol.5, pp. 1-11, 2023, DOI:10.32604/jiot.2023.040255 - 14 September 2023

Abstract Rainwater harvesting (RWH) systems have been the source of domestic water for many years and still becoming essential in many communities of developing countries. However, due to various reasons, there are several sources of contamination in the rainwater cistern systems. Dissolved chemicals from the roofing, storage, and conveyance materials, together with the suspended particulate matter from the airborne, are examples of water contamination. In this work, the water quality monitoring system has been designed and implemented. Chemical and physical parameters of water samples were collected from three locations using a data acquisition (DAQ) system and More >

Wen-Tsai Sung¹, Indra Griha Tofik Isa^1,2, Sung-Jung Hsiao^3,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2179-2200, 2023, DOI:10.32604/cmc.2023.041022 - 30 August 2023

Abstract Indonesia is a producer in the fisheries sector, with production reaching 14.8 million tons in 2022. The production potential of the fisheries sector can be optimally optimized through aquaculture management. One of the most important issues in aquaculture management is how to efficiently control the fish pond water conditions. IoT technology can be applied to support a fish pond aquaculture monitoring system, especially for catfish species (Siluriformes), in real-time and remotely. One of the technologies that can provide this convenience is the IoT. The problem of this study is how to integrate IoT devices with Firebase’s… More >

Anas Bilal^1,*, Xiaowen Liu¹, Haixia Long^1,*, Muhammad Shafiq², Muhammad Waqar³

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2401-2426, 2023, DOI:10.32604/cmc.2023.037857 - 30 August 2023

Abstract Farming is cultivating the soil, producing crops, and keeping livestock. The agricultural sector plays a crucial role in a country’s economic growth. This research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop yield. In the first stage, machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast crops. The recommended crops are based on various factors such as weather conditions, soil analysis, and the amount of fertilizers and pesticides required. In the second stage, a transfer learning-based model for plant seedlings, pests, and plant leaf disease More >

Madallah Alruwaili^1,*, Muhammad Hameed Siddiqi¹, Kamran Farid², Mohammad Azad¹, Saad Alanazi¹, Asfandyar Khan², Abdullah Khan²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2293-2311, 2023, DOI:10.32604/csse.2023.040312 - 28 July 2023

Abstract Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate, ECG, nasal/oral airflow, temperature, light sensor, and fall detection sensor. Different researchers have done different work in the field of health monitoring with sensor networks. Different researchers used built-in apps, such as some used a small number of parameters, while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate, and outdated tools for study development. While no efficient, cheap, and updated work is proposed in the field of sensor-based health monitoring… More >

Yuna Han¹, Hangbae Chang^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 221-237, 2023, DOI:10.32604/cmc.2023.039463 - 08 June 2023

Abstract Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous… More >