Muhammad Shakeel Faridi¹, Saqib Ali^1,2,*, Guojun Wang^2,*, Salman Afsar Awan¹, Muhammad Zafar Iqbal³

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.041929

Abstract Trustworthiness and product traceability are essential factors in the apparel industry 4.0 for establishing successful business relationships among stakeholders such as customers, manufacturers, suppliers, and consumers. Each stakeholder has implemented different technology-based systems to record and track product transactions. However, these systems work in silos, and there is no intra-system communication, leading to a lack of complete supply chain traceability for all apparel stakeholders. Moreover, apparel stakeholders are reluctant to share their business information with business competitors; thus, they involve third-party auditors to ensure the quality of the final product. Furthermore, the apparel manufacturing industry faces challenges with counterfeit products,… More >

Medhat A. Tawfeek^1,2, Nacim Yanes^3,4, Leila Jamel^5,*, Ghadah Aldehim⁵, Mahmood A. Mahmood^1,6

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2545-2564, 2023, DOI:10.32604/cmc.2023.042179

Abstract The trend towards smart greenhouses stems from various factors, including a lack of agricultural land area owing to population concentration and housing construction on agricultural land, as well as water shortages. This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers. The proposed model uses a one-dimensional convolutional neural network (CNN) deep learning model to control the growth of strategic crops, including cucumber, pepper, tomato, and bean. The proposed model uses the Internet of Things (IoT) to collect data on agricultural operations and then uses this data… More >

Rui Qiao, Shi Dong^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2505-2527, 2023, DOI:10.32604/cmc.2023.043476

Abstract Effectively identifying and preventing the threat of Byzantine nodes to the security of distributed systems is a challenge in applying consortium chains. Therefore, this paper proposes a new consortium chain generation model, deeply analyzes the vulnerability of the consortium chain consensus based on the behavior of the nodes, and points out the effects of Byzantine node proportion and node state verification on the consensus process and system security. Furthermore, the normalized verification node aggregation index that represents the consensus ability of the consortium organization and the trust evaluation function of the verification node set is derived. When either of the… 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 >

Abdelwahed Berguiga^1,2,*, Ahlem Harchay^1,2, Ayman Massaoudi^1,2, Mossaad Ben Ayed³, Hafedh Belmabrouk⁴

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 379-402, 2023, DOI:10.32604/cmc.2023.041667

Abstract Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture… 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 >

Shadi Al-Sarawi¹, Mohammed Anbar^1,*, Basim Ahmad Alabsi², Mohammad Adnan Aladaileh³, Shaza Dawood Ahmed Rihan²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 491-515, 2023, DOI:10.32604/cmc.2023.041108

Abstract The Internet of Things (IoT) consists of interconnected smart devices communicating and collecting data. The Routing Protocol for Low-Power and Lossy Networks (RPL) is the standard protocol for Internet Protocol Version 6 (IPv6) in the IoT. However, RPL is vulnerable to various attacks, including the sinkhole attack, which disrupts the network by manipulating routing information. This paper proposes the Unweighted Voting Method (UVM) for sinkhole node identification, utilizing three key behavioral indicators: DODAG Information Object (DIO) Transaction Frequency, Rank Harmony, and Power Consumption. These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant… More >

Muhammad Ali Khan¹, Rao Naveed Bin Rais^2,*, Osman Khalid¹

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 827-843, 2023, DOI:10.32604/cmc.2023.040756

Abstract The Internet of Things (IoT) comprises numerous resource-constrained devices that generate large volumes of data. The inherent vulnerabilities in IoT infrastructure, such as easily spoofed IP and MAC addresses, pose significant security challenges. Traditional routing protocols designed for wired or wireless networks may not be suitable for IoT networks due to their limitations. Therefore, the Routing Protocol for Low-Power and Lossy Networks (RPL) is widely used in IoT systems. However, the built-in security mechanism of RPL is inadequate in defending against sophisticated routing attacks, including Sybil attacks. To address these issues, this paper proposes a centralized and collaborative approach for… More >

Farhan Amin, Gyu Sang Choi^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1165-1181, 2023, DOI:10.32604/cmc.2023.040693

Abstract In recent years, the Internet of Things (IoT) has played a vital role in providing various services to users in a smart city. However, searching for services, objects, data, and frameworks remains a concern. The technological advancements in Cyber-Physical Systems (CPSs) and the Social Internet of Things (SIoT) open a new era of research. Thus, we propose a Cyber-Physical-Social Systems (CPSs) for service search. Herein, service search and object discovery operation carries with the suitable selection of friends in the network. Our proposed model constructs a graph and performs social network analysis (SNA). We suggest degree centrality, clustering, and scale-free… More >

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 >