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In this paper, the performance of smart city network infrastructure is studied using space shift keying (SSK) modulation and radio-frequency (RF) based energy harvesting with cooperative amplify-and-forward (AF) and decode-and-forward (DF) relaying. Here, the Wireless-enabled sites can communicate data to the control unit via the access point (AP). The sites function by utilizing an energy-saving mechanism, which leads to a notable improvement in spectral efficiency. The Markov chain considers the number of active sites in the network dynamically. Moreover, energy harvesting-assisted AP helps overcome the rising demands of the city by providing an enhanced data rate, low latency, shorter coverage, widespread connectivity of the massive number of sites, and energy efficiency. The channel between sites to the AP is Nakagami-m distributed, whereas the AP is connected with the control unit through the free space optical (FSO) link. The FSO link is modelled with Gamma–Gamma distribution with pointing error impairments. The mathematical equations for average bit error probability (ABEP) are obtained for smart cities by considering mixed channels and utilizing the multi-user selection combination. The Monte Carlo simulation technique verifies the accuracy of the numerical results. According to the findings, using SSK modulation in combination with energy harvesting strategies can enhance the effectiveness of smart cities. [ABSTRACT FROM AUTHOR]

It is well-known that nanofluids differ significantly from traditional heat transfer fluids in terms of their thermal and transfer characteristics. Two of CO2 transfer characteristics, its thermal conductivity and its viscosity, are crucial to improved oil retrieval methods and industries refrigeration. By combining molecular modelling with various machine learning algorithms, this study predicts the conduction characteristics of iron oxide CO2 nanofluids. It is possible to evaluate the accuracy of these transfer parameter estimates by applying machine learning methods such as decision tree, K-nearest neighbors, and linear regression. Predicting these transfer qualities requires knowing the size, fraction of nanoparticle volume, and temperature. To determine the characteristics, molecular dynamics simulations are run using the large-scale atom Vastly equivalent simulant. An inter- and intra-variable Pearson correlation was established to confirm that the input variables were reliant on m and thermal conductivity. The results were finally confirmed by using statistical coefficients of determination. For a variety of temperature ranges, volume fractions, and nanoparticle sizes, the study found that the decision tree model was the best at predicting the transport parameters of nanofluids. It has a 99% success rate. [ABSTRACT FROM AUTHOR]