Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks. Localization is the main issue in WSNs; if the sensor node location is unknown, then coverage and power and routing are not optimal. This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities. These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants, such as PM2.5 particulate matter, PM10, nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization. The dataset is divided into training and testing parts based on 10 cross-validations. The evaluation on predicting the air pollutant for localization is performed with the training dataset. Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.     

Functionalization of carbon nanomaterials for advanced polymer nanocomposites: A comparison study between CNT and graphene

The allotropes of carbon nanomaterials (carbon nanotubes, graphene) are the most unique and promising substances of the last decade. Due to their nanoscale diameter and high aspect ratio, a small amount of these nanomaterials can produce a dramatic improvement in the properties of their composite materials. Although carbon nanotubes (CNTs) and graphene exhibit numerous extraordinary properties, their reported commercialization is still limited due to their bundle and layer forming behavior. Functionalization of CNTs and graphene is essential for achieving their outstanding mechanical, electrical and biological functions and enhancing their dispersion in polymer matrices. A considerable portion of the recent publications on CNTs and graphene have focused on enhancing their dispersion and solubilization using covalent and non-covalent functionalization methods. This review article collectively introduces a variety of reactions (e.g. click chemistry, radical polymerization, electrochemical polymerization, dendritic polymers, block copolymers, etc.) for functionalization of CNTs and graphene and fabrication of their polymer nanocomposites. A critical comparison between CNTs and graphene has focused on the significance of different functionalization approaches on their composite properties. In particular, the mechanical, electrical, and thermal behaviors of functionalized nanomaterials as well as their importance in the preparation of advanced hybrid materials for structures, solar cells, fuel cells, supercapacitors, drug delivery, etc. have been discussed thoroughly.     

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.