This systematic review investigates AI-ready data engineering pipelines by analyzing 106 studies published between 2010 and 2022, focusing on Medallion Architecture, cloud-native integration models, metadata management, and lakehouse infrastructure. Following PRISMA guidelines, sources were retrieved from IEEE Xplore, Scopus, Web of Science, ScienceDirect, and Google Scholar. The review examines key architectural strategies, integration patterns, and governance mechanisms that support scalable and explainable AI workflows. Medallion Architecture was discussed in 42 studies, highlighting its tiered bronze-silver-gold design that supports modular transformations and data traceability. Case studies demonstrated reduced redundancy, enhanced reproducibility, and compatibility with MLOps practices, making it well-suited for use cases in fintech, retail, and predictive maintenance. Cloud-native tools such as AWS Glue, Azure Data Factory, and GCP Dataflow appeared in 58 articles. These platforms support real-time orchestration, autoscaling, and serverless execution. Studies reported a 30% reduction in deployment time when pipelines leveraged containerization, low-code orchestration, and cloud-native storage systems. Multi-cloud and hybrid models were noted for addressing data sovereignty, latency, and vendor lock-in concerns. Metadata and data lineage were central to 39 studies, which emphasized the importance of schema versioning, transformation tracking, and audit readiness. Tools like Apache Atlas, Amundsen, and Microsoft Purview were shown to enhance model explainability and reproducibility, reducing audit time and enabling ethical AI deployment. Thirty-six studies focused on lakehouse platforms such as Delta Lake and Apache Hudi. These systems combined the scalability of data lakes with the reliability of warehouses, enabling schema-on-read, real-time feature updates, and versioned data snapshots across training and serving pipelines. However, 31 studies noted challenges including metadata inconsistency in multi-region setups, storage overhead from versioning, and organizational gaps in MLOps responsibilities. These findings underscore the need for integrated governance, standardized roles, and cross-functional collaboration.

The reliable and safe operation of industrial systems needs to detect and diagnose bearing faults as early as possible. Intelligent fault diagnostic systems that use deep learning convolutional neural network (CNN) techniques have achieved a great deal of success in recent years. In a traditional CNN, the fully connected layer is located in the final three layers, and such a layer consists of multiple layers that are all connected. However, the fully connected layer of the CNN has the disadvantage of too many training parameters, which makes the model training and testing time longer and incurs overfitting. Additionally, because the working load is constantly changing and noise from the place of operation is unavoidable, the efficiency of intelligent fault diagnosis techniques suffers great reductions. In this research, we propose a novel technique that can effectively solve the problem of traditional CNN and accurately identify the bearing fault. Firstly, the best pre-trained CNN model is identified by considering the classification’s success rate for bearing fault diagnosis. Secondly, the selected CNN model is modified to effectively reduce the parameter quantities, overfitting, and calculating time of this model. Finally, the best classifier is identified to make a hybrid model concept to achieve the best performance. It is found that the proposed technique performs well under different load conditions, even in noisy environments, with variable signal-to-noise ratio (SNR) values. Our experimental results confirm that this proposed method is highly reliable and efficient in detecting and classifying bearing faults.

The growing world population necessitates more efficient food production, particularly in agriculture. Traditional irrigation techniques usually result in overwatering or underwatering, which wastes energy and water and reduces agricultural productivity. Smart agriculture optimizes food production, resource management, and labor. This study introduces an intelligent irrigation and monitoring system that uses the Internet of Things (IoT) to automate water pump management and monitor sunlight, temperature, and humidity levels without human interaction. The system’s hardware components include a soil moisture sensor, a sunlight sensor, temperature and humidity (DHT11) sensors, an ESP32 microcontroller, and a pump motor. The sensors are in charge of gathering the information that the ESP32 microcontroller needs in order to properly operate the pump motor. To operate and monitor data from the sensors remotely, the ESP32 is also integrated with the well-known Ubidots server via a wireless sensor network. Initially, sensors such as the DHT11, soil moisture sensors, and sunlight level sensors collect data from the field and send it to the ESP32 microcontroller. The microcontroller then compares the received data to the previously stored data. If the values are greater than the threshold, the associated devices turn on and update the sensor value and pump motor condition to the Ubidots server.

Computer network communication is quickly growing in this pandemic situation. Phone conferencing, video streaming and sharing file/printing are all made easier with communications technologies. Data transmitted in time with little interruption become a significant achievement of wireless sensor networks (WSNs). A massive network is interconnection computer networks in the globe connected by the Internet, and the Internet plays a critical role in WSNs. Data access is a key element of any enterprise network, and the routing protocol is used to transmit data or access data. Due to the growing use of WSNs, it is essential to know about the network structure, the routing protocol. The routing protocols must be used to route all data sent over the Internet between the source and the destination. Which chooses the optimum routes between any two nodes in an enterprise network. This research focused on how the routing table will determine the optimum path/route of data packets to be transmitted from source to destination. The performance of three routing protocols, Routing Information Protocol (RIP), Enhanced Interior Gateway Routing Protocol (EIGRP) and Open Shortest Path First (OSPF), is investigated in this research for the massive mesh based enterprise wireless sensor network. We also investigated the behaviors of end-to-end packet latency, convergence time on flapping connections and average point-to-point throughput (bits/sec) between network links. Finally, the simulation results are compared to the efficacy and performance of these protocols implemented in the wireless LAN and internet-based wireless sensor network.

Early and accurate detection of bearing faults is essential for the safe and reliable working of industrial machinery units. The main problem of the traditional fault diagnosis method is manually extracting the features which require the experimenter’s experience and expert knowledge. Therefore, the shallow diagnostic model’s classification rate does not produce good results. To address this issue, this research proposes a technique to detect and classify bearing faults based on an effective convolutional neural network (CNN) model, which is capable of performing complex vibration signals and removing the impact of expert expertise on the feature extraction process. A time-moving segmentation window is used to segment the vibration raw signal and the segmented signals are decomposed up to two levels using DWT. After that, decomposed signals are converted into grayscale images to train and test the proposed CNN model. To verify the performance of the model, CWRU bearing dataset and MFPT dataset are used. The proposed CNN model achieves the highest accuracy in terms of performance both under different load conditions as well as under noisy situations with varying SNR values. The experimental findings show that the proposed system is effective and extremely dependable in detecting bearing faults.