The Internet of Things (IoT) enables the interrelation of physical things and devices that can be accessed through the internet and it simply forms a single integrated network of various things. An IoT-facilitated smart city scenario spans several sectors, such as industrial applications, public transportation, smart grid, emergency services, health care, etc. In this paper, we propose an Intelligent Proficient Data Collection Approach (IPDCA) to deliver public data in a large-scale smart city set-up. IPDCA utilizes public vehicles as the mobile data collectors (D-collectors) that read (or collect) data from multiple Access Points (APs) and send them back to the central Base Station (BS). Moreover, IPDCA adopts a modified Bat algorithm for path finding of D-collectors, where we extend the Bat algorithm to solve our discrete optimization problem. Besides, for selecting D-collectors in smart city settings, we use a multi-objective fitness function that considers the count, travelled distance, and storage of D-collectors to ensure optimal use of resources. Efficiency of the proposed mechanism is proved through simulations.

The trend of creating and deploying intelligent transportation systems within the context of the Internet of Vehicles (IoV) concept is gaining traction and attracting interest from both academia and industry. Rear-end collisions, which constitute an immense amount of traffic accidents, are an essential concern in an effort to improve road safety in smart cities. The complexity entailed in tackling this problem, specifically concerning the prediction of rear-end crashes, demands the application of cutting-edge methodologies. Learning-based techniques have shown promise, but traditional methods are not very successful in offering solutions. Nevertheless, there are drawbacks to these techniques, especially with regard to feature extraction and prediction accuracy. This study presents the Bayesian Optimized Enhanced Ensemble Multi-Layer Perceptron, a novel method for predictive modeling in IoV safety for rear-end collision prediction. Our approach leverages the feature extraction capabilities of Convolutional Neural Networks (CNNs) to interpret complex sensor data from connected vehicles. In order to expedite experimentation, we utilize a meticulously selected subset of an extensive dataset that includes insightful records about vehicle behavior and collision incidents. This dataset undergoes preprocessing and normalization prior to one-dimensional (1D) CNN processing for feature extraction. Furthermore, our predictive modeling approach employs an ensemble of Multi-Layer Perceptrons (MLPs). In order to maximize model performance, hyper-parameters are optimized. We compare the effectiveness of our proposed model against alternative methods and show its superiority in predicting collision events through experimentation and rigorous evaluation. The results show that our suggested solution outperforms existing methods and considerably improves IoV safety systems. Specifically, the suggested model achieved an MSE of 0.00004403 and an RMSE of 0.006617, exhibiting a 30.03% improvement in RMSE over ANN, 27.51% over DRNN-LSTM, 24.71% over EMLP, 19.28% over BPNN, and 27.50% over A3C. These results highlight the model’s ability to reliably forecast rear-end crashes, which is critical for improving driver safety and reducing accident risks in traffic scenarios.

The progress made in self-interference revocation and suppression techniques for wireless communication has significantly enhanced the viability of employing full-duplex (FD) transmission abilities. Compared to half-duplex (HD) transmission, these capabilities notably enhance the end-to-end spectral efficiency (SE), leading to significant advantages for multi-hop routing in wireless communication scenarios. There has recently been an increase in interest in discovering paths in multi-hop wireless communication scenarios that exhibit higher SE. It has been proved that solving for higher SE paths within multi-hop wireless communication scenarios cannot be solved polynomially in networks employing FD interference-constrained systems. This study introduces ECapRO-SEM, an innovative route optimization algorithm called the Enhanced Capuchin Route Optimizer for SE maximization for wireless network communication. Its goal is to improve the end-to-end SE of wireless communication networks. The algorithm begins by formulating an optimization problem to maximize end-to-end SE in FD forwarding across specified paths, while effectively accounting for interference. Subsequently, the algorithm employs an enhanced nature-inspired meta-heuristic optimization technique to precisely identify the routing path that yields the utmost end-to-end SE. Nature-inspired optimization methods, exemplified by the Capuchin search algorithm, have emerged as potent meta-heuristic tools for approximating optimal solutions. We enhance the Capuchin search algorithm by integrating it with the Genetic algorithm (GA). The novel ECapRO-SEM algorithm demonstrates its superiority in achieving peak end-to-end SE for wireless network routing, outperforming existing methodologies in extensive simulations. Simulation results show that ECapRO outperforms Mod-Dijkstras, Pruned-HC and CapRO by 33.53%, 5.17% and 3.40%, respectively at different signal-to-noise ratios (SNR). Furthermore, with various self-interference (SI) cancelation factors, ECapRO outperforms Mod-Dijkstras, Pruned-HC and CapRO by 210.33%, 5.25% and 2.14%, respectively.