The revolutionary sixth-generation wireless communications technologies and applications, notably digital twin networks (DTN), connected autonomous vehicles (CAVs), space-air-ground integrated networks (SAGINs), zero-touch networks, industry 5.0, healthcare 5.0, agriculture 5.0, and more, are driving the evolution of next-generation wireless networks (NGWNs). These innovative technologies and groundbreaking innovative applications will generate a sheer volume of data that requires the swift transmission of massive data across wireless networks and the capability to connect trillions of devices, thereby fueling the use of sophisticated next-generation multiple access (NGMA) schemes.In particular, NGMA strives to cater to the massive connectivity in the 6G era, enabling the smooth and optimized operations of NGWNs compared to existing multiple access (MA) schemes. This survey showcases non-orthogonal multiple access (NOMA) as the frontrunner for NGMA, spotlighting its novel contributions within the existing literature in terms of What has NOMA delivered?, What is NOMA currently providing? and What lies ahead for NOMA?. We present different variants of NOMA in this comprehensive survey, detailing their fundamental operations. In addition, this survey highlights NOMA’s applicability in a broad range of wireless communications technologies such as multi-antenna systems, machine learning, reconfigurable intelligent surfaces (RIS), cognitive radio networks (CRN), integrated sensing and communications (ISAC), terahertz networks, unmanned aerial vehicles (UAVs), etc. This survey delves deeper by providing a comprehensive literature review of NOMA’s interplay with various state-of-the-art wireless technologies. Furthermore, despite the numerous perks and advantages of NOMA, we also highlight several technical challenges of NOMA, which NOMA-assisted NGWNs may encounter. Next, we unveil the research trends of NOMA in the 6G era, offering reliable, robust, and swift communications. Finally, we offer design recommendations and insights along with the future perspectives of NOMA as the leading choice for NGMA within the realm of NGWNs.

In wireless sensor networks (WSNs), it is inappropriate to use conventional unicast routing due to the broadcast storm problem and spatial diversity of communication links. Opportunistic Routing (OR) benefits the low duty-cycled WSNs by prioritizing the multiple candidates for each node instead of selecting one node as in conventional unicast routing. OR reduces the sender waiting time, but it also suffers from the duplicate packets problem due to multiple candidates waking up simultaneously. The number of candidates should be restricted to counterbalance between the sender waiting time and duplicate packets. In this paper, software-defined networking (SDN) is adapted for the flexible management of WSNs by allowing the decoupling of the control plane from the sensor nodes. This study presents an SDN based load balanced opportunistic routing for duty-cycled WSNs that addresses two parts. First, the candidates are computed and controlled in the control plane. Second, the metric used to prioritize the candidates considers the average of three probability distributions, namely transmission distance distribution, expected number of hops distribution and residual energy distribution so that more traffic is guided through the nodes with higher priority. Simulation results show that our proposed protocol can significantly improve the network lifetime, routing efficiency, energy consumption, sender waiting time and duplicate packets as compared with the benchmarks.

Purpose: Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.Design/methodology/approach: An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.Findings: The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.Originality/value: This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.