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The fast-moving world relies on intelligent connected networks to support the numerous applications of the expanded Internet-of-Things (IoT). The evolving communication requirements of this connected world require a new sixth generation (6G) radio to enable intelligent interaction with the massive number of connected objects. The energy management of billions of connected devices supporting massive Internet-of-Things (IoT) applications is the main challenge. These IoT devices and connected nodes are energy limited, and hence, energy-aware solutions are needed to enable seamless information flow between these communicating nodes. This paper presents an intelligent network solution for improved energy efficiency in a 6G-enabled expanded IoT network. A cell-free massive multiple input multiple output (mMIMO) technology is utilized for maximum energy efficiency with optimum network resource allocation. A practical power consumption model is proposed for the designed network topology which contains all the power components related to data transmission and circuit power. The proposed scheme aims to achieve maximum energy efficiency by the optimal allocation of pilot reuse factor and access point (AP) density for a given number of antennas at each AP and number of users. It is observed that the maximum energy efficiency of 5.2362 Mbit/Joule is achieved at the AP density of 29 and pilot reuse factor of 4 with PMMSE receive combining. In the end, the role of energy efficiency and area throughput tradeoff on the system performance is also evaluated, which suggests that both the energy efficiency and area throughput can be jointly increased until maximum energy efficiency is reached at a point.

In recent days, the IoT along with wireless sensor networks (WSNs), have been widely deployed for various healthcare applications. Nowadays, healthcare industries use electronic sensors to reduce human errors while analysing illness more accurately and effectively. This paper proposes an IoT-based health monitoring system to investigate body weight, temperature, blood pressure, respiration and heart rate, room temperature, humidity, and ambient light along with the synchronised clock model. The system is divided into two phases. In the first phase, the system compares the observed parameters. It generates advisory to parents or guardians through SMS or e-mails. This cost-effective and easy-to-deploy system provides timely intimation to the associated medical practitioner about the patient’s health and reduces the effort of the medical practitioner. The data collected using the proposed system were accurate. In the second phase, the proposed system was also synchronised using a linear quadratic regression clock synchronisation technique to maintain a high synchronisation between sensors and an alarm system. The observation made in this paper is that the synchronised technology improved the performance of the proposed health monitoring system by reducing the root mean square error to 0.379% and the R-square error by 0.71%.

Integrating physical, computational, and networking resources are the goal of cyber‐physical systems, also known as smart‐embedded systems. By investing in a solid foundation, we can improve the usefulness and timeliness of the services we rely on in every facet of our lives and ultimately live more elegantly. Regarding modern technology, data security is a significant factor that must be considered. The complexity of cyber‐physical systems' interacting components and middleware presents serious hurdles when it comes to protecting them from cyber‐attacks without negatively impacting their performance. This article proposes a unique, efficient encryption technique for anticipating cyber assaults in cyber‐physical systems, which addresses these concerns. The suggested method uses Bayesian optimization techniques to fine‐tune the LightGBM algorithm's hyper‐parameters. This proposed algorithm has been implemented on the intrusion detection dataset (UNR‐IDD) from the University of Nevada. Reno has been used to test the suggested approach. The proposed system achieved 99.17% accuracy, 0.9918 precision, and 0.9922 recall values. Our empirical evaluation demonstrates that the algorithm successfully increases accuracy and AUC value, making the cyber‐physical system more secure. In turn, the suggested methodology may offer robust assurance for user data safety.

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With the emergence of cloud technologies, the services of healthcare systems have grown. Simultaneously, machine learning systems have become important tools for developing matured and decision-making computer applications. Both cloud computing and machine learning technologies have contributed significantly to the success of healthcare services. However, in some areas, these technologies are needed to provide and decide the next course of action for patients suffering from diabetic kidney disease (DKD) while ensuring privacy preservation of the medical data. To address the cloud data privacy problem, we proposed a DKD prediction module in a framework using cloud computing services and a data control scheme. This framework can provide improved and early treatment before end-stage renal failure. For prediction purposes, we implemented the following machine learning algorithms: support vector machine (SVM), random forest (RF), decision tree (DT), naïve Bayes (NB), deep learning (DL), and k nearest neighbor (KNN). These classification techniques combined with the cloud computing services significantly improved the decision making in the progress of DKD patients. We applied these classifiers to the UCI Machine Learning Repository for chronic kidney disease using various clinical features, which are categorized as single, combination of selected features, and all features. During single clinical feature experiments, machine learning classifiers SVM, RF, and KNN outperformed the remaining classification techniques, whereas in combined clinical feature experiments, the maximum accuracy was achieved for the combination of DL and RF. All the feature experiments presented increased accuracy and increased F-measure metrics from SVM, DL, and RF.