Fog computing is a promising and challenging paradigm that enhances cloud computing by enabling efficient data processing and storage closer to data sources and users. This paper introduces a game-theoretic approach called GTRADPMFC (Game-Theoretic Resource Allocation and Dynamic Pricing Mechanism in Fog Computing) to address resource allocation and dynamic pricing challenges in fog computing environments with limited resources. The proposed model features non-cooperative competition among fog nodes for resources and dynamic pricing mechanisms to encourage efficient resource utilization. Theoretical analysis and simulations demonstrate that GTRADPMFC improves resource efficiency and overall fog computing system performance. Additionally, the paper discusses how to handle situations with insufficient samples and provide flexibility for users unable to meet completion time requirements. GTRADPMFC effectively manages resource allocation by establishing pricing in fog computing, considering potential delays in completion time. This is achieved through research, simulations, convergence analysis, complexity evaluation, and optimization guarantees.

For an extended period, a technological architecture known as cloud IoT links IoT devices to servers located in cloud data centers. Real-time data analytic are made possible by this, enabling better, data-driven decision making, optimization, and risk reduction. Since cloud systems are often located at a considerable distance from IoT devices, the rise of time-sensitive IoT applications has driven the requirement to extend cloud architecture for timely delivery of critical services. Balancing the allocation of IoT services to appropriate edge nodes while guaranteeing low latency and efficient resource utilization remains a challenging task. Since edge nodes have lower resource capabilities than the cloud. The primary drawback of current methods in this situation is that they only tackle the scheduling issue from one side. Task scheduling plays a pivotal role in various domains, including cloud computing, operating systems, and parallel processing, enabling effective management of computational resources. In this research, we provide a multiple-factor autonomous IoT-Edge scheduling method based on game theory to solve this issue. Our strategy involves two distinct scenarios. In the first scenario, we introduced an algorithm containing choices for the IoT and edge nodes, allowing them to evaluate each other using factors such as delay and resource usage. The second scenario involves both a centralized and a distributed scheduling approach, leveraging the matching concept and considering each other. In addition, we also introduced a preference-based stable mechanism (PBSM) algorithm for resource allocation. In terms of the execution time for IoT services and the effectiveness of resource consolidation for edge nodes, the technique we use achieves better results compared with the two commonly used Min-Min and Max-Min scheduling algorithms.

Parkinson’s disease (PD) is a neurological disorder affecting the nerve cells. PD gives rise to various neurological conditions, including gradual reduction in movement speed, tremors, limb stiffness, and alterations in walking patterns. Identifying Parkinson’s disease in its initial phases is crucial to preserving the well-being of those afflicted. However, accurately identifying PD in its early phases is intricate due to the aging population. Therefore, in this paper, we harnessed machine learning-based ensemble methodologies and focused on the premotor stage of PD to create a precise and reliable early-stage PD detection model named PDD-ET. We compiled a tailored, extensive dataset encompassing patient mobility, medication habits, prior medical history, rigidity, gender, and age group. The PDD-ET model amalgamates the outcomes of various ML techniques, resulting in an impressive 97.52% accuracy in early-stage PD detection. Furthermore, the PDD-ET model effectively distinguishes between multiple stages of PD and accurately categorizes the severity levels of patients affected by PD. The evaluation findings demonstrate that the PDD-ET model outperforms the SVR, CNN, Stacked LSTM, LSTM, GRU, Alex Net, [Decision Tree, RF, and SVR], Deep Neural Network, HOG, Quantum ReLU Activator, Improved KNN, Adaptive Boosting, RF, and Deep Learning Model techniques by the approximate margins of 37%, 30%, 20%, 27%, 25%, 18%, 19%, 27%, 25%, 23%, 45%, 40%, 42%, and 16%, respectively.

Air pollution is a critical global concern, demanding precise air quality forecasting to mitigate its severe consequences. Our study introduces Future Air Quality Prediction using Long Short-Term Memory based on Hyper Heuristic Multi-Chain Model (H ² MCM) to project future air quality, considering various meteorological factors (MFs) and pollution-related variables like atmospheric pressure, temperature, humidity, and wind patterns. Leveraging 12 units of Long Short-Term Memory neural networks (LSTMs), H ² MCM accurately predicts forthcoming air pollutants (APs) concentrations such as particulate matter with diameter 2.5 μm (PM _2.5 ), carbon monoxide (CO), and nitrogen dioxide (NO ₂ ). Additionally, it accounts for spatiotemporal correlations between these APs and MFs, which significantly influence the air quality prediction for the next immediate time interval. H ² MCM utilizes a multi-chain mechanism, employing 1-hour prediction models to forecast air quality hourly, enabling approximations for the next 12 hours. Also, for an efficient model selection, Akaike Information Criterion (AIC), Schwarz Bayesian Information Criterion (SBIC), Hannan-Quinn Information Criterion (HQIC), and corrected AIC (AICc) tools are used based on their ability to balance model fit and complexity. Furthermore, it demonstrates the ability to enhance the performance of any predictor. Experimental results substantiate H ² MCM’s superiority over various models, including the Support Vector Regressor (SVR), Multi-Layer Perceptron (MLP), Recurrent Air Quality Predictor (RAQP), and Valchogianni models. H ² MCM achieves impressive up to 75% better accuracy and consistency compared to SVR, 60% better than MLP, 38% better than RAQP, and 70% better than Valchogianni models.

The metaverse is an upcoming computing paradigm aiming towards blending reality seamlessly with the artificially generated 3D worlds of deep cyberspace. This giant interactive mesh of three-dimensional reconstructed realms has recently received tremendous attention from both an academic and commercial point of view owing to the curiosity instilled by its vast possible use cases. Every virtual world in the metaverse is controlled and maintained by a virtual service provider (VSP). Interconnected clusters of LiDAR sensors act as a feeder network to these VSPs which then process the data and reconstruct the best quality immersive environment possible. These data can then be leveraged to provide users with highly targeted virtual services by building upon the concept of digital twins (DTs) representing digital analogs of real-world items owned by parties that create and establish the communication channels connecting the DTs to their real-world counterparts. Logically, DTs represent data on servers where postprocessing can be shared easily across VSPs, giving rise to new marketplaces and economic frontiers. This paper presents a dynamic and distributed framework to enable high-quality reconstructions based on incoming data streams from sensors as well as to allow for the optimal allocation of VSPs to users. The optimal synchronization intensity control problem between the available VSPs and the feeder network is modeled using a simultaneous differential game, while the allocation of VSPs to users is modeled using a preference-based game-theoretic approach, where the users give strict preferences over the available VSPs.

During the winter season in India, the AQI experiences a decrease due to the limited dispersion of APs caused by MFs. Therefore, we developed a sophisticated green predictive model GAP, which utilizes our designed green technique and a customized big dataset. This dataset is derived from weather research and tailored to forecast future AQI levels in the Indian subcontinent during winter. This dataset has been meticulously curated by amalgamating samples of APs and MFs concentrations, further adjusted to reflect the yearly activity data across various Indian states. The dataset reveals an amplified national emissions rate for $\pmb {PM_{2.5}}$ , $\pmb {NO_{2}}$ , and $\pmb {CO}$ pollutants, exhibiting an increase of 3.6%, 1.3%, and 2.5% in gigagrams per day. ML/DL regressors are then applied to this dataset, with the most effective ML/DL regressors being selected based on their performance. Our paper encompasses an exhaustive examination of existing literature within the realm of air pollution epidemiology. The evaluation results demonstrate that the prediction accuracy of GAP when utilizing LSTM, CNN, MLP, and RNN achieve accuracies of 98.53%, 95.9222%, 96.1555%, and 97.344% in predicting the $\pmb {PM_{2.5}}$ , $\pmb {NO_{2}}$ , and $\pmb {CO}$ concentrations. In contrast, RF, KNN, and SVR yield lower accuracies of 92.511%, 90.333%, and 93.566% for the same AQIs.