Blockchains are a new approach to creating distributed networks that were first introduced in 2008. It allows the formation of peer-to-peer networks based on consensus, forming chains from accepted blocks without requiring a central authority or centralized controller. A prominent application of this technology is its use in decentralized storage systems. Individuals in decentralized storage networks rent unused hardware storage space to other individuals. A decentralized network utilizing end-to-end encryption eliminates the risk of data loss associated with centralized data control by enabling clients to transmit their files securely. The storage providers must prove that they have kept unaltered files in this network for this time. Many studies have been conducted in this specific domain, most targeting storage capacity and efficiency, but a security, integrity and privacy loophole need to be addressed. This paper presents an overview of blockchain-based storage systems and how they work, followed by a comparison with cloud-based storage networks and a survey of various decentralized storage networks like SIA, File coin, and Storj available on the market. Next, we will discuss the advantages and disadvantages of blockchain-based storage. In our final discussion, we will examine the security problems of decentralized storage networks and explore potential solutions and research directions for the future.

The feasibility of artificial intelligence (AI) as a predictive model for thorough efficacy analysis on environmental pollution applied on mangrove forests are discussed. Mangrove forests are among the most productive and biological diverse ecosystems on the planet. However, due to environmental pollution and climate change, mangrove forests are in serious decline. Despite crucial issues pertaining mangrove forests, the law enforcement on the ecosystem is still dubious due to the lack of evidence and data that could provide accurate analysis and prediction. The main highlight of this review elaborates on pollutant markers in soil, water, and air, by correlating these three aspects to the sustainability of mangrove ecosystem. The research gap identified from this review suggests the application of an integrated environmental prediction system for practical environmental insights. A predictive model for environmental decision-making could be developed by integrating meteorological, climatological, hydrological, atmospheric, and heavy metal concentration to understand the interaction between each factor for an efficient solution of pollutant reduction scheme involving mangrove ecosystems.

Industry 4.0 as a driving force is making huge strides, particularly in the manufacturing sector, where all integral components involved in the production processes are getting digitally interconnected. Fused with improved automation and robotics, machine learning, artificial intelligence, big data, cloud computing, and the Internet of Things (IoT), this open network interconnectivity makes industrial systems increasingly vulnerable to cyber-attacks. While the impacts and intentions of cyber-attacks vary, they always have a detrimental effect on manufacturers, including financial losses, supply chain disruption, loss of reputation and competitiveness, and theft of corporate secrets. Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) is a legacy Machine-to-Machine (M2M) communication protocol used profoundly in the semiconductor and other manufacturing industries. It is mainly designed to be utilized in a controlled and regulated factory environment separated from external networks. Industry 4.0 has revolutionized the manufacturing industry and has brought SECS/GEM back to the limelight as it lacks security safeguards to protect against cyber-attacks. This paper proposes a digital signature-based security mechanism that offers authentication, integrity, and protection against cyber-attacks. The proposed mechanism is compared with the industry-standard SECS/GEM implementation in terms of processing time, payload overhead, and resilience against cyber-attacks. The results indicate that SECS/GEMsec effectively prevented untrusted entities from establishing communication links with legit industrial equipment while maintaining message integrity by discarding forged messages. Additionally, it protected SECS/GEM communications against Denial-of-Service (DoS) attacks, Replay attacks, and False-Data-Injection-Attack (FDIA) attacks.

Medical device failure and maintenance records are essential information, as some nations lack dedicated systems for capturing this valuable data. In addition to making healthcare more intelligent and individualized, machine learning has the potential to transform the conventional healthcare system. Optimizing AI models in decision-making could mitigate the effects of three research issues: malfunctioning medical devices, high maintenance costs, and the lack of a strategic maintenance framework. This study proposes a data-driven machine-learning model for predicting medical device failure. The proposed predictive model is developed using multimodal data of structured maintenance and unstructured text narrative of maintenance reports to predict the failure of 8,294 critical medical devices. In developing the model, 44 varieties of essential medical devices from 15 healthcare institutions in Malaysia are utilized. A classification problem is addressed by classifying failure into three prediction classes: (i) class 1, unlikely to fail within the first three years, (ii) class 2, likely to fail within three years; and (iii) class 3, likely to fail after three years from the date of commissioning. The topic modelling and synthesis strategy: Latent Dirichlet Allocation is applied to unstructured data in order to uncover concealed patterns in maintenance notes captured during failures. In addition, sensitivity analysis is performed to select only the most significant parameters affecting the failure performance of the medical device. Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. Based on the performance evaluation, the Ensemble Classifier is further optimized and demonstrates improved accuracy of 88.80%, specificity of 94.41%, recall of 88.82%, precision of 88.46%, and F1 Score of 88.84%. The study proves a reduction in intervention from 18 to 8 features and a reduction in training time from 1660.5 to 901.66 seconds for comprehensive model development.

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 central servers. Therefore, the combined objective case studies are examined as minimization and maximization criteria, thus increasing the efficiency of the proposed method. Finally, four scenarios are chosen to investigate the projected design’s effectiveness. In all simulated metrics, the proposed approach provides better operational outcomes for an average percentage of 97, thereby reducing the amount of traffic in real-time conditions.

Industry 4.0, as a driving force, is making massive achievements, notably in the manufacturing sector, where all key components engaged in the production processes are being digitally interconnected. However, when combined with enhanced automation and robotics, machine learning, artificial intelligence, big data, cloud computing, and the Internet of Things (IoT), this open network interconnectivity renders industrial systems more vulnerable to cyberattacks. Cyberattacks may have a variety of different impacts and goals, but they always have negative repercussions for manufacturers. These repercussions include financial losses, disruption of supply chains, loss of reputation and competitiveness, and theft of corporate secrets. Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) is a legacy Machine-to-Machine (M2M) communication protocol used profoundly in the semiconductor and other manufacturing industries. SECS/GEM is mainly designed to be utilized in a trusted, controlled, and regulated factory environment separated from external networks. Industry 4.0 has revolutionized the manufacturing industry and has brought SECS/GEM back to the limelight, as SECS/GEM is completely devoid of security features. This research proposes ES-SECS/GEM, an Efficient Security mechanism that provides authentication, integrity, and protection against cyberattacks. The proposed mechanism is compared to other security mechanisms in terms of processing time, control overhead, and resilience against cyber-attacks. The ES-SECS/GEM demonstrated promising results, suggesting that it allowed SECS/GEM devices to only connect with authorized industrial equipment, maintained message integrity, discarded forged messages, and prevented cyberattacks on SECS/GEM communications. In terms of processing time and control, ES-SECS/GEM likewise outperformed other mechanisms and incurred the lowest values for these metrics.