Nazik Alturki¹, Raed Alharthi², Muhammad Umer^3,*, Oumaima Saidani¹, Amal Alshardan¹, Reemah M. Alhebshi⁴, Shtwai Alsubai⁵, Ali Kashif Bashir^6,7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3387-3415, 2024, DOI:10.32604/cmes.2023.044700

Abstract The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets as “OFF” or “ON” based… More >

Huixiang Xu^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2309-2335, 2024, DOI:10.32604/cmc.2024.046253

Abstract The Internet of Things (IoT) has revolutionized how we interact with and gather data from our surrounding environment. IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights. The rapid proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented data generation and connectivity. These IoT devices, equipped with many sensors and actuators, continuously produce vast volumes of data. However, the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges. However, transmitting all this data to a… More >

Xiangmin Guo^1,2, Guangjun Liang^1,2,*, Jiayin Liu^1,2, Xianyi Chen^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3305-3323, 2023, DOI:10.32604/cmc.2023.044529

Abstract Cloud storage is widely used by large companies to store vast amounts of data and files, offering flexibility, financial savings, and security. However, information shoplifting poses significant threats, potentially leading to poor performance and privacy breaches. Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms, ensuring businesses can focus on business development. To ensure data security in cloud platforms, this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing (HD2C) model. However, the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things (IoT) in the cloud.… More >

Fengting Luo, Ruwei Huang^*, Yuyue Chen

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1815-1835, 2023, DOI:10.32604/cmc.2023.041676

Abstract Smart parks serve as integral components of smart cities, where they play a pivotal role in the process of urban modernization. The demand for cross-domain cooperation among smart devices from various parks has witnessed a significant increase. To ensure secure communication, device identities must undergo authentication. The existing cross-domain authentication schemes face issues such as complex authentication paths and high certificate management costs for devices, making it impractical for resource-constrained devices. This paper proposes a blockchain-based lightweight and efficient cross-domain authentication protocol for smart parks, which simplifies the authentication interaction and requires every device to maintain only one certificate. To… More >

Dana F. Doghramachi^1,*, Siddeeq Y. Ameen²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 717-732, 2023, DOI:10.32604/cmc.2023.041186

Abstract The rapid adoption of the Internet of Things (IoT) across industries has revolutionized daily life by providing essential services and leisure activities. However, the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences. Intrusion Detection Systems (IDS) are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic. The security research community has shown particular interest in leveraging Machine Learning (ML) approaches to develop practical IDS applications for general cyber networks and IoT environments. However, most available datasets related to Industrial IoT suffer from imbalanced class distributions. This study proposes a… More >

Oliva Debnath¹, Saptarshi Debnath¹, Sreyashi Karmakar², MD Tausif Mallick³, Himadri Nath Saha^4,*

Journal on Internet of Things, Vol.5, pp. 13-43, 2023, DOI:10.32604/jiot.2023.039391

Abstract The Internet of Things (IoT) will significantly impact our social and economic lives in the near future. Many Internet of Things (IoT) applications aim to automate multiple tasks so inactive physical objects can behave independently of others. IoT devices, however, are also vulnerable, mostly because they lack the essential built-in security to thwart attackers. It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment. As a result, the IoT designs that are now in use do not completely support all of the advancements that have been… More >

Ayman Khallel Al-Ani^1,*, Shams Ul Arfeen Laghari², Hariprasath Manoharan³, Shitharth Selvarajan⁴, Mueen Uddin⁵

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2261-2279, 2023, DOI:10.32604/cmc.2023.038534

Abstract In this paper, the application of transportation systems in real-time traffic conditions is evaluated with data handling representations. The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system. Compared to the existing approach, the design model in the proposed method is made by dividing the computing areas into several cluster regions, thereby reducing the complex monitoring system where control errors are minimized. Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate… More >

Mahmood Hussain Mir^1,*, Sanjay Jamwal¹, Ummer Iqbal², Abolfazl Mehbodniya³, Julian Webber³, Umar Hafiz Khan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2529-2565, 2023, DOI:10.32604/cmes.2023.027173

Abstract The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID- 19 since its outbreak. The Internet of Things (IoT) along with other technologies like Machine Learning can revolutionize the traditional healthcare system. Instead of reactive healthcare systems, IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services. In this study, a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection. The proposed framework is based on edge computing… More >

Zaid Abdulsalam Ibrahim^1,*, Muhammad Ilyas²

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 939-956, 2023, DOI:10.32604/cmc.2023.035798

Abstract Most research works nowadays deal with real-time Internet of Things (IoT) data. However, with exponential data volume increases, organizations need help storing such humongous amounts of IoT data in cloud storage systems. Moreover, such systems create security issues while efficiently using IoT and Cloud Computing technologies. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has the potential to make IoT data more secure and reliable in various cloud storage services. Cloud-assisted IoTs suffer from two privacy issues: access policies (public) and super polynomial decryption times (attributed mainly to complex access structures). We have developed a CP-ABE scheme in alignment with a Hidden Hierarchy Ciphertext-Policy… More >

Sana Parez¹, Naqqash Dilshad², Turki M. Alanazi³, Jong Weon Lee^1,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 515-536, 2023, DOI:10.32604/csse.2023.037992

Abstract A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative,… More >